+ 
-
+
LM generative pre-trained transformer
+🔗 CrystaLLM - Generate a crystal structure from a composition. Language models generative pre-trained transformer
🔗 matsci.org - A community forum for the discussion of anything materials science, with a focus on computational materials science..
@@ -159,7 +159,7 @@ _Projects that collect atomistic ML resources or foster communication within com
git clone https://github.com/ml-tooling/best-of-ml-python
```
-MIT general-ml rep-learnMIT general-ml rep-learnMIT datasets benchmarkingMIT datasets benchmarkingMIT generative pre-trained drug-discoveryGPL-3.0 licenseApache-2 transformer Language models pre-trained drug-discoveryUnlicensed educational rep-learnUnlicensed datasetspre-trained LM
+🔗 DeepChem Models - DeepChem models on HuggingFace. pre-trained Language models
🔗 JARVIS-Leaderboard ( ⭐ 41) - This project provides benchmark-performances for materials science applications including Artificial Intelligence.. benchmarking
@@ -247,6 +271,14 @@ _Datasets, databases and trained models for atomistic ML._
🔗 sGDML Datasets - MD17, MD22, DFT datasets.
+🔗 MoleculeNet benchmarking
+
+🔗 ZINC15 graph biomolecules
+
+🔗 ZINC graph biomolecules
+
+🔗 NRELMatDB - Computational materials database with the specific focus on materials for renewable energy applications including, but..
+
MITMIT biomolecules benchmarkingCC-BY-4.0CC-BY-NC-SA 4.0 ML-DFTMITLGPL-2.1 knowledge-base pre-trained
-- ANI-1 Dataset (🥈8 · ⭐ 91 · 💀) - A data set of 20 million calculated off-equilibrium conformations for organic molecules. MIT
-- MoleculeNet Leaderboard (🥈8 · ⭐ 77 · 💀) - MIT benchmarking
+- ANI-1 Dataset (🥉8 · ⭐ 91 · 💀) - A data set of 20 million calculated off-equilibrium conformations for organic molecules. MIT
+- MoleculeNet Leaderboard (🥉8 · ⭐ 77 · 💀) - MIT benchmarking
- 2DMD dataset (🥉7 · ⭐ 2) - Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of.. Apache-2 material-defect
- GEOM (🥉6 · ⭐ 150 · 💀) - GEOM: Energy-annotated molecular conformations. Unlicensed drug-discovery
- ANI-1x Datasets (🥉6 · ⭐ 47 · 💀) - The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules. MIT
@@ -328,7 +380,7 @@ _Projects that focus on providing data structures used in atomistic machine lear
conda install -c deepmodeling dpdata
```
BSD-3 Rust C-lang C++ PythonBSD-3 Rust C-lang C++ PythonApache-2 C++Custom ML-DFTApache-2Apache-2GPL-3.0 rep-learnApache-2LGPL-3.0MIT JuliaMIT JuliaBSD-3
- PROPhet (🥈9 · ⭐ 61 · 💀) - PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches. GPL-3.0 ML-IAP MD single-paper C++
-- Libnxc (🥈7 · ⭐ 15 · 💀) - A library for using machine-learned exchange-correlation functionals for density-functional theory. MPL-2.0 C++ Fortran
+- Libnxc (🥉7 · ⭐ 15 · 💀) - A library for using machine-learned exchange-correlation functionals for density-functional theory. MPL-2.0 C++ Fortran
+- Mat2Spec (🥉6 · ⭐ 24 · 💀) - MIT spectroscopy
- gprep (🥉4 · 💀) - Fitting DFTB repulsive potentials with GPR. MIT single-paper
- xDeepH (🥉3 · ⭐ 23) - Extended DeepH (xDeepH) method for magnetic materials. LGPL-3.0 magnetism Julia
- ML-DFT (🥉3 · ⭐ 19 · 💀) - A package for density functional approximation using machine learning. MIT
- DeepCDP (🥉3 · ⭐ 3) - DeepCDP: Deep learning Charge Density Prediction. Unlicensed
- CSNN (🥉3 · ⭐ 1 · 💀) - Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning. BSD-3
- MALADA (🥉3 · 💤) - MALA Data Acquisition: Helpful tools to build data for MALA. BSD-3
+- A3MD (🥉2 · ⭐ 5 · 💀) - MPNN-like + Analytic Density Model = Accurate electron densities. Unlicensed representation-learning single-paper
- kdft (🥉1 · ⭐ 2 · 💀) - The Kernel Density Functional (KDF) code allows generating ML based DFT functionals. Unlicensed
+- APET (🥉1 · ⭐ 2 · ➕) - Atomic Positional Embedding-based Transformer. GPL-3.0 density-of-states transformer
active-learning
+
+MIT rep-learnCustomCCO-1.0Apache-2MIT
- MAChINE (🥈8 · ⭐ 1) - Client-Server Web App to introduce usage of ML in materials science to beginners. MIT
@@ -507,9 +612,11 @@ _Tutorials, guides, cookbooks, recipes, etc._
- Applied AI for Materials (🥉6 · ⭐ 50 · 💀) - Course materials for Applied AI for Materials Science and Engineering. Unlicensed
- AI4Science101 (🥉5 · ⭐ 72 · 💀) - AI for Science. Unlicensed
- Machine Learning for Materials Hard and Soft (🥉5 · ⭐ 33 · 💀) - ESI-DCAFM-TACO-VDSP Summer School on Machine Learning for Materials Hard and Soft. Unlicensed
+- MACE-tutorials (🥉5 · ⭐ 3 · 🐣) - Another set of tutorials for the MACE interatomic potential by one of the authors. MIT ML-IAP rep-learn MD
- ML-in-chemistry-101 (🥉4 · ⭐ 57 · 💀) - The course materials for Machine Learning in Chemistry 101. Unlicensed
- chemrev-gpr (🥉4 · ⭐ 6 · 💀) - Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020. Unlicensed
- MLDensity_tutorial (🥉2 · ⭐ 6 · 💤) - Tutorial files to work with ML for the charge density in molecules and solids. Unlicensed
+- LAMMPS-style pair potentials with GAP (🥉2 · ⭐ 3 · 💀) - A tutorial on how to create LAMMPS-style pair potentials and use them in combination with GAP potentials to run MD.. Unlicensed ML-IAP MD rep-eng
- MALA Tutorial (🥉2 · ⭐ 2) - A full MALA hands-on tutorial. Unlicensed
- PiNN Lab (🥉2 · ⭐ 2 · 💤) - GPL-3.0
MITMITApache-2 drug-discovery pre-trained rep-learnMIT XAICustom MD pre-trained electrostatics magnetism structure-relaxationCustom MD pre-trained electrostatics magnetism structure-relaxationMIT pre-trainedMIT pre-trainedBSD-3BSD-3MIT JuliaMIT JuliaCustomCustom JuliaCustom FortranCustomUnlicensedMIT JuliaUnlicensed rep-learn transformerGPL-3.0 C++
- TensorMol (🥈12 · ⭐ 260 · 💀) - Tensorflow + Molecules = TensorMol. GPL-3.0 single-paper
- ANI-1 (🥈12 · ⭐ 210 · 💀) - ANI-1 neural net potential with python interface (ASE). MIT
-- SIMPLE-NN (🥉11 · ⭐ 44 · 💀) - SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network). GPL-3.0
-- NNsforMD (🥉10 · ⭐ 10 · 💀) - Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings. MIT
+- SIMPLE-NN (🥈11 · ⭐ 44 · 💀) - SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network). GPL-3.0
+- NNsforMD (🥈10 · ⭐ 10 · 💀) - Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings. MIT
- SchNet (🥉9 · ⭐ 190 · 💀) - SchNet - a deep learning architecture for quantum chemistry. MIT
- SNAP (🥉8 · ⭐ 32 · 💀) - Repository for spectral neighbor analysis potential (SNAP) model development. BSD-3
- SIMPLE-NN v2 (🥉8 · ⭐ 31) - GPL-3.0
- Atomistic Adversarial Attacks (🥉8 · ⭐ 25 · 💀) - Code for performing adversarial attacks on atomistic systems using NN potentials. MIT probabilistic
-- PhysNet (🥉7 · ⭐ 85 · 💀) - Code for training PhysNet models. MIT electrostatics
+- PhysNet (🥉7 · ⭐ 85 · 💀) - Code for training PhysNet models. MIT electrostatics
- AIMNet (🥉7 · ⭐ 79 · 💀) - Atoms In Molecules Neural Network Potential. MIT single-paper
- testing-framework (🥉6 · ⭐ 11 · 💀) - The purpose of this repository is to aid the testing of a large number of interatomic potentials for a variety of.. Unlicensed benchmarking
- PANNA (🥉6 · ⭐ 7 · 💀) - A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic.. MIT benchmarking
- Alchemical learning (🥉5 · ⭐ 2 · 💤) - Code for the Modeling high-entropy transition metal alloys with alchemical compression article. BSD-3
- glp (🥉4 · ⭐ 13) - tools for graph-based machine-learning potentials in jax. MIT
- TensorPotential (🥉4 · ⭐ 5) - Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic.. Custom
+- PeriodicPotentials (🥉4 · 💀) - A Periodic table app that displays potentials based on the selected elements. MIT community-resource viz JavaScript
- SingleNN (🥉2 · ⭐ 7 · 💀) - An efficient package for training and executing neural-network interatomic potentials. Unlicensed C++
+- MEGNetSparse (🥉2 · ⭐ 1 · 🐣) - A library imlementing a graph neural network with sparse representation from Code for Kazeev, N., Al-Maeeni, A.R.,.. MIT material-defect
- RuNNer (🥉2) - The RuNNer Neural Network Energy Representation is a Fortran-based framework for the construction of Behler-.. GPL-3.0 Fortran
+- Allegro-JAX (🥉1 · ⭐ 11 · 🐣) - JAX implementation of the Allegro interatomic potential. Unlicensed
+- mlp (🥉1 · ⭐ 1 · 💀) - Proper orthogonal descriptors for efficient and accurate interatomic potentials... Unlicensed Julia
MIT datasetsMITApache-2 transformer pre-trained drug-discoveryMITMIT
+- ChemDataExtractor (🥈16 · ⭐ 270 · 💀) - Automatically extract chemical information from scientific documents. MIT literature-data
+- MAPI_LLM (🥉5 · ⭐ 4 · ➕) - A LLM application developed during the LLM March MADNESS Hackathon https://doi.org/10.1039/D3DD00113J. MIT dataset
+- ChemDataWriter (🥉4 · ⭐ 8 · 🐣) - ChemDataWriter is a transformer-based library for automatically generating research books in the chemistry area. MIT literature-data
+- SciBot (🥉3 · ⭐ 14 · 🐣) - SciBot is a simple demo of building a domain-specific chatbot for science. Unlicensed
+- CatBERTa (🥉3 · ⭐ 13 · ➕) - Large Language Model for Catalyst Property Prediction. Unlicensed transformer catalysis
- BERT-PSIE-TC (🥉3 · ⭐ 2) - A dataset of Curie temperatures automatically extracted from scientific literature with the use of the BERT-PSIE.. MIT magnetism
MIT probabilistic active-learningMIT probabilistic active-learningMIT rep-learnMPL-2.0 C++MIT Julia
- cnine (🥉4 · ⭐ 2) - Cnine tensor library. Unlicensed C++
+- torch_spex (🥉4 · ➕) - Spherical expansions in PyTorch. Unlicensed
- Wigner Kernels (🥉2 · ⭐ 1) - Collection of programs to benchmark Wigner kernels. Unlicensed benchmarking
Custom ML-IAP C++Custom ML-IAP C++MIT ML-IAP rep-learnMIT ML-IAP rep-learn
- interface-lammps-mlip-3 (🥉4 · ⭐ 5 · 💤) - An interface between LAMMPS and MLIP (version 3). GPL-2.0
MIT benchmarking
- SkipAtom (🥉7 · ⭐ 22 · 💀) - Distributed representations of atoms, inspired by the Skip-gram model. MIT
@@ -1589,7 +1792,8 @@ _Projects that offer implementations of representations aka descriptors, fingerp
- SA-GPR (🥉6 · ⭐ 14 · 💀) - Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR). LGPL-3.0 C-lang
- fplib (🥉6 · ⭐ 7 · 💀) - a fingerprint library. MIT C-lang single-paper
- SOAPxx (🥉6 · ⭐ 7 · 💀) - A SOAP implementation. GPL-2.0 C++
-- pyLODE (🥉6 · ⭐ 2) - Pythonic implementation of LOng Distance Equivariants. Apache-2 electrostatics
+- pyLODE (🥉6 · ⭐ 2) - Pythonic implementation of LOng Distance Equivariants. Apache-2 electrostatics
+- soap_turbo (🥉5 · ⭐ 4 · 💤) - soap_turbo comprises a series of libraries to be used in combination with QUIP/GAP and TurboGAP. Custom Fortran
- SISSO++ (🥉4 · ⭐ 2 · 💀) - C++ Implementation of SISSO with python bindings. Apache-2 C++
- magnetism-prediction (🥉4 · ⭐ 1) - DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides. Apache-2 magnetism single-paper
- ML-for-CurieTemp-Predictions (🥉4 · 🐣) - Machine Learning Predictions of High-Curie-Temperature Materials. MIT single-paper magnetism
@@ -1687,6 +1891,18 @@ _General models that learn a representations aka embeddings of atomistic systems
git clone https://github.com/NVIDIA/DeepLearningExamples
```
GPL-3.0BSD-3MIT magnetismMIT transformerMIT
- Cormorant (🥉7 · ⭐ 55 · 💀) - Codebase for Cormorant Neural Networks. Custom
- ai4material_design (🥉7 · ⭐ 2) - Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of.. Apache-2 pre-trained material-defect
-- charge_transfer_nnp (🥉6 · ⭐ 24 · 💀) - Graph neural network potential with charge transfer. MIT electrostatics
+- charge_transfer_nnp (🥉6 · ⭐ 24 · 💀) - Graph neural network potential with charge transfer. MIT electrostatics
- tensorfieldnetworks (🥉5 · ⭐ 140 · 💀) - MIT
- Autobahn (🥉5 · ⭐ 28 · 💀) - Repository for Autobahn: Automorphism Based Graph Neural Networks. MIT
-- SCFNN (🥉5 · ⭐ 15 · 💀) - Self-consistent determination of long-range electrostatics in neural network potentials. MIT C++ electrostatics single-paper
+- SCFNN (🥉5 · ⭐ 15 · 💀) - Self-consistent determination of long-range electrostatics in neural network potentials. MIT C++ electrostatics single-paper
- FieldSchNet (🥉5 · ⭐ 11 · 💀) - MIT
- Per-Site CGCNN (🥉5 · ⭐ 1) - Crystal graph convolutional neural networks for predicting material properties. MIT pre-trained single-paper
- Atom2Vec (🥉4 · ⭐ 26 · 💀) - Atom2Vec: a simple way to describe atoms for machine learning. Unlicensed
@@ -2032,6 +2256,14 @@ _Projects and models that focus on quantities of wavefunction theory methods, su
MIT pre-trainedApache-2
+- DIG: Dive into Graphs (🥈21 · ⭐ 1.7K · ➕) - A library for graph deep learning research. GPL-3.0
+- ATOM3D (🥇18 · ⭐ 280 · 💤) - ATOM3D: tasks on molecules in three dimensions. MIT biomolecules benchmarking
+- ChemCrow (🥇17 · ⭐ 320 · 🐣) - Chemcrow. MIT
+- ChemDataExtractor (🥈16 · ⭐ 270 · 💀) - Automatically extract chemical information from scientific documents. MIT literature-data
+- ChemNLP project (🥈16 · ⭐ 110 · ➕) - ChemNLP project. MIT datasets
+- GT4SD - Generative Toolkit for Scientific Discovery (🥈15 · ⭐ 280 · ➕) - Gradio apps of generative models in GT4SD. MIT generative pre-trained drug-discovery
+- dlpack (🥉14 · ⭐ 800 · 💤) - common in-memory tensor structure. Apache-2 C++
+- Geometric GNN Dojo (🥇12 · ⭐ 350 · ➕) - New to geometric GNNs: try our practical notebook, prepared for MPhil students at the University of Cambridge. MIT rep-learn
+- QH9: A Quantum Hamiltonian Prediction Benchmark (🥈12 · ⭐ 280 · ➕) - Artificial Intelligence for Science (AIRS). CC-BY-NC-SA 4.0 ML-DFT
+- QHNet (🥈12 · ⭐ 280 · ➕) - Artificial Intelligence for Science (AIRS). GPL-3.0 rep-learn
+- Grad DFT (🥈12 · ⭐ 43 · ➕) - Grad-DFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation.. Apache-2
+- pretrained-gnns (🥇10 · ⭐ 870 · ➕) - Strategies for Pre-training Graph Neural Networks. MIT pre-trained
+- DSECOP (🥈10 · ⭐ 31 · ➕) - This repository contains data science educational materials developed by DSECOP Fellows. CCO-1.0
+- pair_nequip (🥉10 · ⭐ 29 · 💀) - LAMMPS pair style for NequIP. MIT ML-IAP rep-learn
+- tinker-hp (🥉9 · ⭐ 69 · ➕) - Tinker-HP: High-Performance Massively Parallel Evolution of Tinker on CPUs & GPUs. Custom
+- lie-nn (🥈9 · ⭐ 22 · ➕) - Tools for building equivariant polynomials on reductive Lie groups. MIT rep-learn
+- TurboGAP (🥉9 · ⭐ 14 · ➕) - The TurboGAP code. Custom Fortran
+- MoLFormers UI (🥉8 · ⭐ 140 · ➕) - Repository for MolFormer. Apache-2 transformer Language models pre-trained drug-discovery
+- MoLFormer (🥉8 · ⭐ 140 · ➕) - Repository for MolFormer. Apache-2 transformer pre-trained drug-discovery
+- pair_allegro (🥉8 · ⭐ 26 · ➕) - LAMMPS pair style for Allegro deep learning interatomic potentials with parallelization support. MIT ML-IAP rep-learn
+- chemlift (🥉8 · ⭐ 10 · 🐣) - Language-interfaced fine-tuning for chemistry. MIT
+- T-e3nn (🥉8 · ⭐ 6 · 💤) - Time-reversal Euclidean neural networks based on e3nn. MIT magnetism
+- Awesome Neural Geometry (🥉7 · ⭐ 780 · ➕) - A curated collection of resources and research related to the geometry of representations in the brain, deep networks,.. Unlicensed educational rep-learn
+- COATI (🥉6 · ⭐ 59 · 🐣) - COATI: multi-modal contrastive pre-training for representing and traversing chemical space. Apache-2 drug-discovery pre-trained rep-learn
+- Mat2Spec (🥉6 · ⭐ 24 · 💀) - MIT spectroscopy
+- NequIP-JAX (🥉5 · ⭐ 10 · ➕) - JAX implementation of the NequIP interatomic potential. Unlicensed
+- MAPI_LLM (🥉5 · ⭐ 4 · ➕) - A LLM application developed during the LLM March MADNESS Hackathon https://doi.org/10.1039/D3DD00113J. MIT dataset
+- soap_turbo (🥉5 · ⭐ 4 · 💤) - soap_turbo comprises a series of libraries to be used in combination with QUIP/GAP and TurboGAP. Custom Fortran
+- MACE-tutorials (🥉5 · ⭐ 3 · 🐣) - Another set of tutorials for the MACE interatomic potential by one of the authors. MIT ML-IAP rep-learn MD
+- Point Edge Transformer (PET) (🥉5 · ➕) - Point Edge Transformer. Unlicensed rep-learn transformer
+- ChemDataWriter (🥉4 · ⭐ 8 · 🐣) - ChemDataWriter is a transformer-based library for automatically generating research books in the chemistry area. MIT literature-data
+- torch_spex (🥉4 · ➕) - Spherical expansions in PyTorch. Unlicensed
+- PeriodicPotentials (🥉4 · 💀) - A Periodic table app that displays potentials based on the selected elements. MIT community-resource viz JavaScript
+- SciBot (🥉3 · ⭐ 14 · 🐣) - SciBot is a simple demo of building a domain-specific chatbot for science. Unlicensed
+- CatBERTa (🥉3 · ⭐ 13 · ➕) - Large Language Model for Catalyst Property Prediction. Unlicensed transformer catalysis
+- A3MD (🥉2 · ⭐ 5 · 💀) - MPNN-like + Analytic Density Model = Accurate electron densities. Unlicensed representation-learning single-paper
+- LAMMPS-style pair potentials with GAP (🥉2 · ⭐ 3 · 💀) - A tutorial on how to create LAMMPS-style pair potentials and use them in combination with GAP potentials to run MD.. Unlicensed ML-IAP MD rep-eng
+- MEGNetSparse (🥉2 · ⭐ 1 · 🐣) - A library imlementing a graph neural network with sparse representation from Code for Kazeev, N., Al-Maeeni, A.R.,.. MIT material-defect
+- Allegro-JAX (🥉1 · ⭐ 11 · 🐣) - JAX implementation of the Allegro interatomic potential. Unlicensed
+- APET (🥉1 · ⭐ 2 · ➕) - Atomic Positional Embedding-based Transformer. GPL-3.0 density-of-states transformer
+- mlp (🥉1 · ⭐ 1 · 💀) - Proper orthogonal descriptors for efficient and accurate interatomic potentials... Unlicensed Julia
+
diff --git a/history/2023-12-03_projects.csv b/history/2023-12-03_projects.csv
index 651b95c..3c823ef 100644
--- a/history/2023-12-03_projects.csv
+++ b/history/2023-12-03_projects.csv
@@ -1,295 +1,342 @@
-,name,resource,category,license,homepage,description,projectrank,show,labels,github_id,github_url,created_at,updated_at,last_commit_pushed_at,commit_count,recent_commit_count,fork_count,watchers_count,pr_count,open_issue_count,closed_issue_count,star_count,latest_stable_release_published_at,latest_stable_release_number,release_count,contributor_count,pypi_id,conda_id,dependent_project_count,github_dependent_project_count,pypi_url,pypi_monthly_downloads,monthly_downloads,conda_url,conda_latest_release_published_at,conda_total_downloads,projectrank_placing,dockerhub_id,dockerhub_url,dockerhub_latest_release_published_at,dockerhub_stars,dockerhub_pulls,github_release_downloads,maven_id,maven_url,updated_github_id,npm_id,npm_url,npm_monthly_downloads,gitlab_id,gitlab_url,ignore,docs_url
-0,AI for Science Map,True,community,GPL-3.0 license,https://www.air4.science/map,"Interactive mindmap of the AI4Science research field, including atomistic machine learning, including papers,..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-1,Atomic Cluster Expansion,True,community,,https://cortner.github.io/ACEweb/,Atomic Cluster Expansion (ACE) community homepage.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2,CrystaLLM,True,community,https://materialis.ai/terms.html,https://crystallm.com,Generate a crystal structure from a composition.,0,True,"['lm', 'generative', 'pre-trained', 'transformer']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-3,matsci.org,True,community,,https://matsci.org/,"A community forum for the discussion of anything materials science, with a focus on computational materials science..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-4,Matter Modeling Stack Exchange - Machine Learning,True,community,,https://mattermodeling.stackexchange.com/questions/tagged/machine-learning,"Forum StackExchange, site Matter Modeling, ML-tagged questions.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-5,Catalysis Hub,True,datasets,,https://www.catalysis-hub.org/,A web-platform for sharing data and software for computational catalysis research!.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-6,Citrination Datasets,True,datasets,MIT,https://citrination.com/,AI-Powered Materials Data Platform. Open Citrination has been decommissioned.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-7,crystals.ai,True,datasets,,https://crystals.ai/,Curated datasets for reproducible AI in materials science.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-8,DeepChem Models,True,datasets,,https://huggingface.co/DeepChem,DeepChem models on HuggingFace.,0,True,"['pre-trained', 'lm']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-9,JARVIS-Leaderboard,True,datasets,https://github.com/usnistgov/jarvis_leaderboard/blob/main/LICENSE.rst,https://pages.nist.gov/jarvis_leaderboard/,This project provides benchmark-performances for materials science applications including Artificial Intelligence..,0,True,['benchmarking'],usnistgov/jarvis_leaderboard,https://github.com/usnistgov/jarvis_leaderboard,2022-07-15 16:48:33,2023-11-27 01:50:42.000,2023-10-13 13:39:27,706.0,30.0,26.0,6.0,275.0,4.0,2.0,41.0,2023-08-04 17:33:22,2023.08.01,21.0,24.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-10,Materials Project - Charge Densities,True,datasets,,https://materialsproject.org/ml/charge_densities,Materials Project has started offering charge density information available for download via their public API.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-11,matterverse.ai,True,datasets,,https://matterverse.ai/,Database of yet-to-be-sythesized materials predicted using state-of-the-art machine learning algorithms.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-12,Quantum-Machine.org Datasets,True,datasets,,http://quantum-machine.org/datasets/,"Collection of datasets, including QM7, QM9, etc. MD, DFT. Small organic molecules, mostly.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-13,sGDML Datasets,True,datasets,,http://sgdml.org/#datasets,"MD17, MD22, DFT datasets.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-14,Quantum Chemistry in the Age of Machine Learning,True,educational,,https://www.elsevier.com/books-and-journals/book-companion/9780323900492,"Book, 2022.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-15,Deep Graph Library (DGL),,rep-learn,Apache-2.0,https://github.com/dmlc/dgl,"Python package built to ease deep learning on graph, on top of existing DL frameworks.",38,True,,dmlc/dgl,https://github.com/dmlc/dgl,2018-04-20 14:49:09,2023-12-02 12:26:24.000,2023-12-01 08:30:42,3656.0,278.0,2828.0,170.0,4213.0,324.0,2119.0,12505.0,2023-08-15 07:31:40,1.1.2,76.0,281.0,dgl,dglteam/dgl,166.0,166.0,https://pypi.org/project/dgl,69432.0,74469.0,https://anaconda.org/dglteam/dgl,2023-09-14 15:04:21.699,302265.0,1.0,,,,,,,,,,,,,,,,
-16,DeepChem,,general-tool,MIT,https://github.com/deepchem/deepchem,"Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology.",36,True,,deepchem/deepchem,https://github.com/deepchem/deepchem,2015-09-24 23:20:28,2023-12-02 20:50:00.000,2023-12-01 17:26:24,9737.0,447.0,1477.0,146.0,2054.0,429.0,1211.0,4750.0,2022-12-01 13:22:37,2.7.1,18.0,230.0,deepchem,conda-forge/deepchem,286.0,286.0,https://pypi.org/project/deepchem,16179.0,18515.0,https://anaconda.org/conda-forge/deepchem,2023-06-16 19:18:02.015,104276.0,1.0,deepchemio/deepchem,https://hub.docker.com/r/deepchemio/deepchem,2022-03-11 05:24:00.723691,4.0,7027.0,,,,,,,,,,,
-17,PyG Models,,rep-learn,MIT,https://github.com/pyg-team/pytorch_geometric/tree/master/torch_geometric/nn/models,Representation learning models implemented in PyTorch Geometric.,30,True,['general-ml'],pyg-team/pytorch_geometric,https://github.com/pyg-team/pytorch_geometric,2017-10-06 16:03:03,2023-12-03 16:59:07.000,2023-12-03 16:59:06,7175.0,247.0,3333.0,254.0,2599.0,757.0,2504.0,19060.0,2023-10-12 08:28:59,2.4.0,36.0,468.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
-18,RDKit,,general-tool,BSD-3-Clause,https://github.com/rdkit/rdkit,,30,True,['lang-cpp'],rdkit/rdkit,https://github.com/rdkit/rdkit,2013-05-12 06:19:15,2023-12-02 06:31:04.000,2023-12-02 06:31:04,7551.0,82.0,775.0,85.0,2895.0,874.0,2083.0,2258.0,2023-11-10 06:45:02,Release_2023_09_2,98.0,207.0,rdkit,rdkit/rdkit,2.0,2.0,https://pypi.org/project/rdkit,324378.0,346727.0,https://anaconda.org/rdkit/rdkit,2023-06-16 12:54:07.547,2546590.0,1.0,,,,,,1407.0,,,,,,,,,,
-19,DeePMD-kit,,ml-iap,LGPL-3.0,https://github.com/deepmodeling/deepmd-kit,A deep learning package for many-body potential energy representation and molecular dynamics.,28,True,['lang-cpp'],deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2023-12-02 23:23:49.000,2023-10-27 13:10:24,2353.0,107.0,437.0,46.0,1373.0,26.0,451.0,1267.0,2023-10-27 19:33:04,2.2.7,41.0,64.0,deepmd-kit,deepmodeling/deepmd-kit,12.0,12.0,https://pypi.org/project/deepmd-kit,893.0,1521.0,https://anaconda.org/deepmodeling/deepmd-kit,2023-10-27 21:14:49.194,353.0,1.0,deepmodeling/deepmd-kit,https://hub.docker.com/r/deepmodeling/deepmd-kit,2023-10-29 23:31:03.651515,1.0,2033.0,29093.0,,,,,,,,,,
-20,SchNetPack,,rep-learn,MIT,https://github.com/atomistic-machine-learning/schnetpack,SchNetPack - Deep Neural Networks for Atomistic Systems.,28,True,,atomistic-machine-learning/schnetpack,https://github.com/atomistic-machine-learning/schnetpack,2018-09-03 15:44:35,2023-12-01 09:07:49.000,2023-12-01 08:57:55,1598.0,43.0,188.0,31.0,370.0,2.0,217.0,670.0,2023-09-29 14:31:19,2.0.4,8.0,32.0,schnetpack,,64.0,64.0,https://pypi.org/project/schnetpack,602.0,602.0,,,,1.0,,,,,,,,,,,,,,,,
-21,Matminer,,general-tool,https://github.com/hackingmaterials/matminer/blob/main/LICENSE,https://github.com/hackingmaterials/matminer,Data mining for materials science.,27,True,,hackingmaterials/matminer,https://github.com/hackingmaterials/matminer,2015-09-24 20:37:00,2023-11-27 08:05:39.000,2023-11-10 10:02:34,4112.0,4.0,176.0,28.0,701.0,27.0,187.0,419.0,2023-06-27 15:36:52,0.9.0,66.0,50.0,matminer,conda-forge/matminer,252.0,252.0,https://pypi.org/project/matminer,14008.0,15412.0,https://anaconda.org/conda-forge/matminer,2023-10-24 08:40:46.186,53358.0,1.0,,,,,,,,,,,,,,,,
-22,paper-qa,,lm,Apache-2.0,https://github.com/whitead/paper-qa,LLM Chain for answering questions from documents with citations.,25,True,,whitead/paper-qa,https://github.com/whitead/paper-qa,2023-02-05 01:07:25,2023-12-01 22:47:51.000,2023-12-01 22:47:34,178.0,11.0,297.0,41.0,90.0,46.0,66.0,3268.0,2023-12-01 22:47:51,3.13.3,74.0,12.0,paper-qa,,39.0,39.0,https://pypi.org/project/paper-qa,2756.0,2756.0,,,,1.0,,,,,,,,,,,,,,,,
-23,e3nn,,rep-learn,MIT,https://github.com/e3nn/e3nn,A modular framework for neural networks with Euclidean symmetry.,25,True,,e3nn/e3nn,https://github.com/e3nn/e3nn,2020-01-31 13:06:42,2023-11-03 06:14:55.000,2023-09-02 22:23:35,2157.0,,111.0,18.0,204.0,16.0,131.0,797.0,2022-12-12 21:42:03,0.5.1,28.0,29.0,e3nn,conda-forge/e3nn,133.0,133.0,https://pypi.org/project/e3nn,97925.0,98498.0,https://anaconda.org/conda-forge/e3nn,2023-06-18 08:41:30.723,10900.0,1.0,,,,,,,,,,,,,,,,
-24,cdk,,rep-eng,LGPL-2.1,https://github.com/cdk/cdk,The Chemistry Development Kit.,25,True,"['cheminformatics', 'lang-java']",cdk/cdk,https://github.com/cdk/cdk,2010-05-11 08:30:07,2023-12-02 12:41:00.000,2023-11-28 15:36:13,17429.0,15.0,147.0,39.0,762.0,25.0,237.0,446.0,2023-08-21 19:50:47,cdk-2.9,20.0,163.0,,,18.0,18.0,,,152.0,,,,1.0,,,,,,16896.0,org.openscience.cdk:cdk-bundle,https://search.maven.org/artifact/org.openscience.cdk/cdk-bundle,,,,,,,,
-25,QUIP,,general-tool,GPL-2.0,https://github.com/libAtoms/QUIP,libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io.,24,True,,libAtoms/QUIP,https://github.com/libAtoms/QUIP,2013-07-02 15:21:59,2023-10-09 12:20:02.000,2023-08-29 12:01:13,10833.0,,117.0,26.0,165.0,91.0,345.0,309.0,2023-06-15 19:11:24,0.9.14,15.0,80.0,quippy-ase,,25.0,25.0,https://pypi.org/project/quippy-ase,1289.0,1373.0,,,,2.0,libatomsquip/quip,https://hub.docker.com/r/libatomsquip/quip,2023-04-24 21:25:17.345957,4.0,9857.0,347.0,,,,,,,,,,
-26,MAML,,general-tool,BSD-3-Clause,https://github.com/materialsvirtuallab/maml,"Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.",24,True,,materialsvirtuallab/maml,https://github.com/materialsvirtuallab/maml,2020-01-25 15:04:21,2023-11-30 13:05:25.000,2023-11-06 19:58:55,1617.0,55.0,63.0,21.0,522.0,4.0,60.0,299.0,2023-09-09 22:24:24,2023.9.9,13.0,29.0,maml,,4.0,4.0,https://pypi.org/project/maml,228.0,228.0,,,,2.0,,,,,,,,,,,,,,,,
-27,Best-of Machine Learning with Python,,community,CC-BY-4.0,https://github.com/ml-tooling/best-of-ml-python,A ranked list of awesome machine learning Python libraries. Updated weekly.,23,True,"['general-ml', 'lang-py']",ml-tooling/best-of-ml-python,https://github.com/ml-tooling/best-of-ml-python,2020-11-29 19:41:36,2023-11-30 16:05:11.000,2023-11-30 16:05:10,442.0,27.0,2102.0,382.0,234.0,17.0,33.0,14694.0,2023-11-30 16:05:19,2023.11.30,100.0,44.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
-28,dgl-lifesci,,rep-learn,Apache-2.0,https://github.com/awslabs/dgl-lifesci,Python package for graph neural networks in chemistry and biology.,23,True,,awslabs/dgl-lifesci,https://github.com/awslabs/dgl-lifesci,2020-04-23 07:14:21,2023-11-01 19:32:07.000,2023-04-16 03:55:52,236.0,,135.0,17.0,141.0,24.0,57.0,641.0,2023-02-13 08:45:17,0.3.2,8.0,22.0,dgllife,,142.0,142.0,https://pypi.org/project/dgllife,13202.0,13202.0,,,,1.0,,,,,,,,,,,,,,,,
-29,DP-GEN,,ml-iap,LGPL-3.0,https://github.com/deepmodeling/dpgen,The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field.,23,True,['workflows'],deepmodeling/dpgen,https://github.com/deepmodeling/dpgen,2019-06-13 11:43:56,2023-11-28 05:00:21.000,2023-11-02 05:59:11,2047.0,23.0,162.0,13.0,758.0,17.0,233.0,246.0,2023-11-02 06:14:15,0.12.0,17.0,62.0,dpgen,deepmodeling/dpgen,4.0,4.0,https://pypi.org/project/dpgen,519.0,556.0,https://anaconda.org/deepmodeling/dpgen,2023-06-16 19:27:03.566,191.0,1.0,,,,,,1529.0,,,,,,,,,,
-30,dpdata,,data-structures,LGPL-3.0,https://github.com/deepmodeling/dpdata,"Manipulating multiple atomic simulation data formats, including DeePMD-kit, VASP, LAMMPS, ABACUS, etc.",23,True,,deepmodeling/dpdata,https://github.com/deepmodeling/dpdata,2019-04-12 13:24:23,2023-12-03 04:35:56.000,2023-10-31 01:50:24,666.0,23.0,109.0,8.0,407.0,10.0,66.0,152.0,2023-10-31 02:23:47,0.2.17,24.0,51.0,dpdata,deepmodeling/dpdata,109.0,109.0,https://pypi.org/project/dpdata,4298.0,4304.0,https://anaconda.org/deepmodeling/dpdata,2023-09-27 20:07:36.945,174.0,1.0,,,,,,,,,,,,,,,,
-31,kgcnn,,rep-learn,MIT,https://github.com/aimat-lab/gcnn_keras,"Graph convolutions in Keras with TensorFlow, PyTorch or Jax.",23,True,,aimat-lab/gcnn_keras,https://github.com/aimat-lab/gcnn_keras,2020-07-17 11:12:46,2023-12-02 14:51:47.000,2023-12-02 14:47:51,3016.0,231.0,25.0,7.0,30.0,8.0,73.0,90.0,2023-09-16 11:47:45,3.1.0,24.0,7.0,kgcnn,,15.0,15.0,https://pypi.org/project/kgcnn,290.0,290.0,,,,1.0,,,,,,,,,,,,,,,,
-32,MPContribs,,datasets,MIT,https://github.com/materialsproject/MPContribs,Platform for materials scientists to contribute and disseminate their materials data through Materials Project.,23,True,,materialsproject/MPContribs,https://github.com/materialsproject/MPContribs,2014-12-11 18:25:27,2023-11-29 22:50:51.000,2023-11-29 22:50:41,5387.0,113.0,19.0,11.0,1581.0,20.0,78.0,32.0,2023-11-28 23:05:55,5.6.2,71.0,25.0,mpcontribs-client,,27.0,27.0,https://pypi.org/project/mpcontribs-client,6493.0,6493.0,,,,1.0,,,,,,,,,,,,,,,,
-33,AlphaFold,,biomolecules,Apache-2.0,https://github.com/google-deepmind/alphafold,Open source code for AlphaFold.,22,True,,deepmind/alphafold,https://github.com/google-deepmind/alphafold,2021-06-17 14:06:06,2023-12-01 15:03:42.000,2023-11-01 12:32:02,133.0,5.0,1905.0,213.0,91.0,186.0,581.0,11091.0,2023-04-05 09:45:53,2.3.2,13.0,19.0,,,7.0,7.0,,,,,,,1.0,,,,,,,,,google-deepmind/alphafold,,,,,,,
-34,JAX-MD,,md,Apache-2.0,https://github.com/jax-md/jax-md,"Differentiable, Hardware Accelerated, Molecular Dynamics.",22,True,,jax-md/jax-md,https://github.com/jax-md/jax-md,2019-05-13 21:03:37,2023-10-23 08:23:20.000,2023-08-29 15:10:31,838.0,,161.0,49.0,157.0,63.0,74.0,1022.0,2022-11-27 12:42:00,jax-md-v0.2.24,7.0,28.0,jax-md,,37.0,37.0,https://pypi.org/project/jax-md,1253.0,1253.0,,,,1.0,,,,,,,,,,,,,,,,
-35,DeepQMC,,ml-wft,MIT,https://github.com/deepqmc/deepqmc,Deep learning quantum Monte Carlo for electrons in real space.,22,True,,deepqmc/deepqmc,https://github.com/deepqmc/deepqmc,2019-12-06 14:50:59,2023-11-20 10:29:54.000,2023-11-20 10:29:53,1456.0,135.0,56.0,23.0,154.0,3.0,36.0,307.0,2023-11-20 10:09:02,1.1.2,10.0,13.0,deepqmc,,1.0,1.0,https://pypi.org/project/deepqmc,149.0,149.0,,,,1.0,,,,,,,,,,,,,,,,
-36,NVIDIA Deep Learning Examples for Tensor Cores,,rep-learn,https://github.com/NVIDIA/DeepLearningExamples/blob/master/DGLPyTorch/DrugDiscovery/SE3Transformer/LICENSE,https://github.com/NVIDIA/DeepLearningExamples#graph-neural-networks,State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and..,21,True,"['educational', 'drug-discovery']",NVIDIA/DeepLearningExamples,https://github.com/NVIDIA/DeepLearningExamples,2018-05-02 17:04:05,2023-11-08 13:05:42.000,2023-10-06 19:54:45,1427.0,3.0,2817.0,293.0,529.0,232.0,558.0,11793.0,,,,115.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-37,MEGNet,,ml-iap,BSD-3-Clause,https://github.com/materialsvirtuallab/megnet,Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals.,21,True,,materialsvirtuallab/megnet,https://github.com/materialsvirtuallab/megnet,2018-12-12 21:31:28,2023-04-27 02:39:17.000,2023-04-27 02:39:17,1146.0,,142.0,24.0,314.0,17.0,57.0,461.0,2022-11-16 21:24:36,1.3.2,34.0,13.0,megnet,,71.0,71.0,https://pypi.org/project/megnet,315.0,315.0,,,,1.0,,,,,,,,,,,,,,,,
-38,TorchANI,,ml-iap,MIT,https://github.com/aiqm/torchani,Accurate Neural Network Potential on PyTorch.,21,True,,aiqm/torchani,https://github.com/aiqm/torchani,2018-04-02 15:43:04,2023-11-30 14:35:04.000,2023-11-14 16:32:59,434.0,1.0,112.0,28.0,481.0,19.0,142.0,413.0,2023-11-14 16:38:04,2.2.4,24.0,17.0,torchani,conda-forge/torchani,29.0,29.0,https://pypi.org/project/torchani,1935.0,7597.0,https://anaconda.org/conda-forge/torchani,2023-11-16 16:26:58.145,226497.0,1.0,,,,,,,,,,,,,,,,
-39,DScribe,,rep-eng,Apache-2.0,https://github.com/SINGROUP/dscribe,DScribe is a python package for creating machine learning descriptors for atomistic systems.,21,True,,SINGROUP/dscribe,https://github.com/SINGROUP/dscribe,2017-05-08 08:29:51,2023-09-06 07:04:16.227,2023-09-05 18:02:57,1287.0,3.0,80.0,20.0,25.0,8.0,79.0,354.0,,,14.0,18.0,dscribe,conda-forge/dscribe,162.0,162.0,https://pypi.org/project/dscribe,7955.0,9917.0,https://anaconda.org/conda-forge/dscribe,2023-09-06 07:04:16.227,80470.0,1.0,,,,,,,,,,,,,,,,
-40,JARVIS-Tools,,general-tool,https://github.com/usnistgov/jarvis/blob/master/LICENSE.rst,https://github.com/usnistgov/jarvis,JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:..,21,True,,usnistgov/jarvis,https://github.com/usnistgov/jarvis,2017-06-22 19:34:02,2023-10-18 14:10:33.000,2023-09-22 00:18:02,2094.0,1.0,107.0,27.0,222.0,41.0,43.0,255.0,2023-08-11 17:26:26,2023.08.01,69.0,15.0,jarvis-tools,conda-forge/jarvis-tools,66.0,66.0,https://pypi.org/project/jarvis-tools,929.0,2487.0,https://anaconda.org/conda-forge/jarvis-tools,2023-06-16 19:23:23.093,59226.0,2.0,,,,,,,,,,,,,,,,
-41,MatGL (Materials Graph Library),,rep-learn,BSD-3-Clause,https://github.com/materialsvirtuallab/matgl,Graph deep learning library for materials.,21,True,,materialsvirtuallab/matgl,https://github.com/materialsvirtuallab/matgl,2022-08-29 18:36:05,2023-11-27 21:05:00.000,2023-11-27 21:04:36,903.0,51.0,32.0,8.0,143.0,3.0,43.0,146.0,2023-11-17 20:11:53,0.9.1,25.0,13.0,m3gnet,,17.0,17.0,https://pypi.org/project/m3gnet,487.0,487.0,,,,2.0,,,,,,,,,,,,,,,,
-42,DM21,,ml-dft,Apache-2.0,https://github.com/deepmind/deepmind-research/tree/master/density_functional_approximation_dm21,This package provides a PySCF interface to the DM21 (DeepMind 21) family of exchange-correlation functionals described..,20,True,,deepmind/deepmind-research,https://github.com/google-deepmind/deepmind-research,2019-01-15 09:54:13,2023-11-27 23:26:48.000,2023-06-02 17:04:50,369.0,,2431.0,337.0,180.0,166.0,135.0,12311.0,,,,92.0,,,,,,,,,,,1.0,,,,,,,,,google-deepmind/deepmind-research,,,,,,,
-43,NequIP,,ml-iap,MIT,https://github.com/mir-group/nequip,NequIP is a code for building E(3)-equivariant interatomic potentials.,20,True,,mir-group/nequip,https://github.com/mir-group/nequip,2021-03-15 23:44:39,2023-10-27 20:35:22.000,2023-03-26 21:37:08,1670.0,,105.0,18.0,149.0,14.0,52.0,471.0,2022-12-20 18:52:46,0.5.6,14.0,8.0,nequip,conda-forge/nequip,16.0,16.0,https://pypi.org/project/nequip,550.0,745.0,https://anaconda.org/conda-forge/nequip,2023-06-18 08:41:30.787,3708.0,1.0,,,,,,,,,,,,,,,,
-44,CHGNet,,ml-iap,https://github.com/CederGroupHub/chgnet/blob/main/LICENSE,https://github.com/CederGroupHub/chgnet,Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov.,20,True,"['md', 'pre-trained', 'electrostatics', 'magnetism', 'structure-relaxation']",CederGroupHub/chgnet,https://github.com/CederGroupHub/chgnet,2023-02-24 23:44:24,2023-11-19 04:15:36.000,2023-11-19 04:10:58,345.0,62.0,29.0,4.0,61.0,3.0,26.0,143.0,2023-11-19 04:15:36,0.3.2,10.0,7.0,chgnet,,9.0,9.0,https://pypi.org/project/chgnet,6985.0,6985.0,,,,1.0,,,,,,,,,,,,,,,,
-45,ocp,,rep-learn,MIT,https://github.com/Open-Catalyst-Project/ocp,ocp is the Open Catalyst Projects library of state-of-the-art machine learning algorithms for catalysis.,19,True,,Open-Catalyst-Project/ocp,https://github.com/Open-Catalyst-Project/ocp,2019-09-26 04:47:27,2023-11-28 00:24:43.000,2023-11-27 18:44:01,710.0,12.0,179.0,23.0,457.0,13.0,132.0,520.0,2022-10-01 03:00:41,0.1.0,4.0,32.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-46,Pre-trained OCP models,,ml-iap,MIT,https://github.com/Open-Catalyst-Project/ocp/blob/main/MODELS.md,Pre-trained models released as part of the Open Catalyst Project.,19,True,['pre-trained'],Open-Catalyst-Project/ocp,https://github.com/Open-Catalyst-Project/ocp,2019-09-26 04:47:27,2023-11-28 00:24:43.000,2023-11-27 18:44:01,710.0,12.0,179.0,23.0,457.0,13.0,132.0,520.0,2022-10-01 03:00:41,0.1.0,4.0,32.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-47,exmol,,xai,MIT,https://github.com/ur-whitelab/exmol,Explainer for black box models that predict molecule properties.,19,True,,ur-whitelab/exmol,https://github.com/ur-whitelab/exmol,2021-08-03 17:56:06,2023-11-27 18:45:16.000,2023-06-19 19:49:27,188.0,,40.0,9.0,76.0,10.0,57.0,259.0,2023-06-19 20:50:51,3.0.3,27.0,7.0,exmol,,14.0,14.0,https://pypi.org/project/exmol,784.0,784.0,,,,1.0,,,,,,,,,,,,,,,,
-48,Metatensor,,data-structures,BSD-3-Clause,https://github.com/lab-cosmo/metatensor,Self-describing sparse tensor data format for atomistic machine learning and beyond.,19,True,"['lang-rust', 'lang-c', 'lang-cpp', 'lang-py']",lab-cosmo/metatensor,https://github.com/lab-cosmo/metatensor,2022-03-01 15:58:28,2023-12-02 19:16:48.000,2023-11-30 14:51:44,461.0,84.0,11.0,17.0,306.0,41.0,80.0,29.0,2023-10-11 15:57:13,metatensor-torch-v0.1.0,13.0,17.0,,,6.0,6.0,,,1128.0,,,,3.0,,,,,,2256.0,,,,,,,,,,
-49,Graph-based Deep Learning Literature,,community,MIT,https://github.com/naganandy/graph-based-deep-learning-literature,links to conference publications in graph-based deep learning.,18,True,"['general-ml', 'rep-learn']",naganandy/graph-based-deep-learning-literature,https://github.com/naganandy/graph-based-deep-learning-literature,2017-12-01 14:48:35,2023-11-19 05:55:34.000,2023-11-19 05:55:28,7632.0,10.0,725.0,243.0,21.0,,13.0,4451.0,,,,12.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-50,Open Catalyst datasets,,datasets,CC-BY-4.0,https://github.com/Open-Catalyst-Project/ocp/blob/main/DATASET.md,"The datasets of the Open Catalyst project, OC20, OC22.",18,True,,Open-Catalyst-Project/ocp,https://github.com/Open-Catalyst-Project/ocp,2019-09-26 04:47:27,2023-11-28 00:24:43.000,2023-11-27 18:44:01,710.0,12.0,179.0,23.0,457.0,13.0,132.0,520.0,2022-10-01 03:00:41,0.1.0,4.0,32.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
-51,FLARE,,active-learning,MIT,https://github.com/mir-group/flare,An open-source Python package for creating fast and accurate interatomic potentials.,18,True,"['lang-cpp', 'ml-iap']",mir-group/flare,https://github.com/mir-group/flare,2018-08-30 23:40:56,2023-09-29 22:57:11.000,2023-05-26 02:06:09,4382.0,,59.0,18.0,185.0,23.0,164.0,253.0,2022-04-21 18:33:10,0.2.4,5.0,37.0,,,10.0,10.0,,,0.0,,,,1.0,,,,,,2.0,,,,,,,,,,
-52,MACE,,ml-iap,MIT,https://github.com/ACEsuit/mace,MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.,18,True,,ACEsuit/mace,https://github.com/ACEsuit/mace,2022-06-21 18:44:34,2023-12-01 22:19:38.000,2023-12-01 11:29:48,373.0,70.0,77.0,18.0,99.0,18.0,86.0,218.0,2023-11-09 17:28:57,0.3.0,2.0,16.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-53,ALIGNN,,rep-learn,https://github.com/usnistgov/alignn/blob/main/LICENSE.rst,https://github.com/usnistgov/alignn,Atomistic Line Graph Neural Network.,18,True,,usnistgov/alignn,https://github.com/usnistgov/alignn,2021-04-19 20:08:09,2023-10-24 20:00:53.000,2023-10-24 20:00:52,623.0,19.0,64.0,11.0,90.0,24.0,23.0,165.0,2023-08-11 04:51:42,2023.08.01,40.0,7.0,alignn,,8.0,8.0,https://pypi.org/project/alignn,404.0,404.0,,,,2.0,,,,,,,,,,,,,,,,
-54,e3nn-jax,,rep-learn,Apache-2.0,https://github.com/e3nn/e3nn-jax,jax library for E3 Equivariant Neural Networks.,18,True,,e3nn/e3nn-jax,https://github.com/e3nn/e3nn-jax,2021-06-08 13:21:51,2023-11-24 09:26:06.000,2023-11-24 09:26:01,951.0,57.0,14.0,9.0,30.0,,10.0,125.0,2023-11-17 11:39:54,0.20.3,39.0,5.0,e3nn-jax,,,,https://pypi.org/project/e3nn-jax,1878.0,1878.0,,,,2.0,,,,,,,,,,,,,,,,
-55,FitSNAP,,md,GPL-2.0,https://github.com/FitSNAP/FitSNAP,Software for generating SNAP machine-learning interatomic potentials.,18,True,,FitSNAP/FitSNAP,https://github.com/FitSNAP/FitSNAP,2019-09-12 14:46:18,2023-11-07 18:49:16.000,2023-11-04 02:17:28,1366.0,62.0,45.0,7.0,174.0,8.0,55.0,125.0,2023-06-28 16:00:48,3.1.0,7.0,24.0,,conda-forge/fitsnap3,,,,,139.0,https://anaconda.org/conda-forge/fitsnap3,2023-06-16 00:19:04.615,5163.0,2.0,,,,,,7.0,,,,,,,,,,
-56,Chemiscope,,visualization,BSD-3-Clause,https://github.com/lab-cosmo/chemiscope,An interactive structure/property explorer for materials and molecules.,18,True,['lang-js'],lab-cosmo/chemiscope,https://github.com/lab-cosmo/chemiscope,2019-10-03 09:59:42,2023-11-30 23:51:24.000,2023-11-30 23:49:13,684.0,33.0,27.0,18.0,205.0,33.0,80.0,94.0,2023-10-23 15:11:17,0.6.0,13.0,19.0,,,5.0,5.0,,,26.0,,,,1.0,,,,,,138.0,,,,chemiscope,https://www.npmjs.com/package/chemiscope,23.0,,,,
-57,MALA,,ml-dft,BSD-3-Clause,https://github.com/mala-project/mala,Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.,18,True,,mala-project/mala,https://github.com/mala-project/mala,2021-03-31 11:40:38,2023-11-28 10:04:41.000,2023-10-26 10:42:52,2086.0,16.0,20.0,9.0,256.0,26.0,211.0,55.0,2023-09-28 13:54:19,1.2.0,8.0,41.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
-58,Uni-Mol,,rep-learn,MIT,https://github.com/dptech-corp/Uni-Mol,Official Repository for the Uni-Mol Series Methods.,17,True,['pre-trained'],dptech-corp/Uni-Mol,https://github.com/dptech-corp/Uni-Mol,2022-05-22 13:26:41,2023-12-03 05:26:31.000,2023-12-03 05:26:31,95.0,12.0,86.0,16.0,70.0,44.0,75.0,456.0,2023-07-07 09:02:23,0.2,2.0,13.0,,,,,,,543.0,,,,2.0,,,,,,7604.0,,,,,,,,,,
-59,GT4SD,,generative,MIT,https://github.com/GT4SD/gt4sd-core,"GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process.",17,True,"['pre-trained', 'drug-discovery', 'rep-learn']",GT4SD/gt4sd-core,https://github.com/GT4SD/gt4sd-core,2022-02-11 19:06:58,2023-11-27 08:23:51.000,2023-10-16 07:22:44,283.0,3.0,56.0,16.0,136.0,2.0,92.0,280.0,2023-05-06 06:55:52,1.3.1,54.0,19.0,gt4sd,,,,https://pypi.org/project/gt4sd,817.0,817.0,,,,1.0,,,,,,,,,,,,,,,,
-60,M3GNet,,ml-iap,BSD-3-Clause,https://github.com/materialsvirtuallab/m3gnet,Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art..,17,True,,materialsvirtuallab/m3gnet,https://github.com/materialsvirtuallab/m3gnet,2022-01-18 18:10:58,2023-06-06 23:56:08.000,2023-06-06 23:56:03,261.0,,53.0,10.0,33.0,15.0,20.0,188.0,2022-11-17 23:25:35,0.2.4,16.0,14.0,m3gnet,,18.0,18.0,https://pypi.org/project/m3gnet,487.0,487.0,,,,2.0,,,,,,,,,,,,,,,,
-61,KFAC-JAX,,math,Apache-2.0,https://github.com/google-deepmind/kfac-jax,Second Order Optimization and Curvature Estimation with K-FAC in JAX.,17,True,,deepmind/kfac-jax,https://github.com/google-deepmind/kfac-jax,2022-03-18 10:19:24,2023-11-29 18:10:59.000,2023-11-29 18:10:53,168.0,25.0,13.0,8.0,180.0,2.0,9.0,173.0,2023-05-16 18:03:40,0.0.5,4.0,11.0,kfac-jax,,8.0,8.0,https://pypi.org/project/kfac-jax,692.0,692.0,,,,1.0,,,,,,,,,google-deepmind/kfac-jax,,,,,,,
-62,XenonPy,,general-tool,BSD-3-Clause,https://github.com/yoshida-lab/XenonPy,XenonPy is a Python Software for Materials Informatics.,17,True,,yoshida-lab/XenonPy,https://github.com/yoshida-lab/XenonPy,2018-01-17 10:13:29,2023-11-20 14:25:49.000,2023-11-20 14:25:43,692.0,10.0,55.0,12.0,183.0,17.0,66.0,113.0,2023-05-21 15:54:32,0.6.8,45.0,10.0,xenonpy,,,,https://pypi.org/project/xenonpy,469.0,487.0,,,,2.0,,,,,,1244.0,,,,,,,,,,
-63,MatBench,,community,MIT,https://github.com/materialsproject/matbench,Matbench: Benchmarks for materials science property prediction.,17,True,"['datasets', 'benchmarking']",materialsproject/matbench,https://github.com/materialsproject/matbench,2021-02-24 03:58:42,2023-11-14 15:38:45.000,2023-11-14 15:38:41,746.0,6.0,32.0,9.0,252.0,31.0,26.0,86.0,2022-07-27 04:40:26,0.6,5.0,23.0,matbench,,11.0,11.0,https://pypi.org/project/matbench,138.0,138.0,,,,2.0,,,,,,,,,,,,,,,,
-64,Ultra-Fast Force Fields (UF3),,ml-iap,Apache-2.0,https://github.com/uf3/uf3,UF3: a python library for generating ultra-fast interatomic potentials.,17,True,,uf3/uf3,https://github.com/uf3/uf3,2021-10-01 13:21:44,2023-12-01 20:06:04.000,2023-10-27 16:18:56,514.0,51.0,18.0,6.0,70.0,12.0,24.0,45.0,2023-10-27 16:36:21,0.4.0,4.0,8.0,uf3,,,,https://pypi.org/project/uf3,38.0,38.0,,,,2.0,,,,,,,,,,,,,,,,
-65,MoLeR,,generative,MIT,https://github.com/microsoft/molecule-generation,Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation.,16,True,,microsoft/molecule-generation,https://github.com/microsoft/molecule-generation,2022-02-17 19:16:29,2023-11-23 13:48:47.000,2023-08-09 14:17:01,63.0,,35.0,11.0,33.0,7.0,26.0,222.0,2023-06-18 21:03:46,0.4.0,4.0,5.0,molecule-generation,,,,https://pypi.org/project/molecule-generation,222.0,222.0,,,,2.0,,,,,,,,,,,,,,,,
-66,CatLearn,,rep-eng,GPL-3.0,https://github.com/SUNCAT-Center/CatLearn,,16,True,['surface-science'],SUNCAT-Center/CatLearn,https://github.com/SUNCAT-Center/CatLearn,2018-04-20 04:16:14,2023-07-25 21:09:47.000,2023-02-07 09:31:25,1960.0,,50.0,19.0,79.0,10.0,16.0,94.0,2020-03-27 09:26:03,0.6.2,8.0,22.0,catlearn,,4.0,4.0,https://pypi.org/project/catlearn,250.0,250.0,,,,1.0,,,,,,,,,,,,,,,,
-67,MAST-ML,,general-tool,MIT,https://github.com/uw-cmg/MAST-ML,MAterials Simulation Toolkit for Machine Learning (MAST-ML).,16,True,,uw-cmg/MAST-ML,https://github.com/uw-cmg/MAST-ML,2017-02-16 17:03:57,2023-12-01 20:57:10.000,2023-07-28 18:33:43,3162.0,,52.0,13.0,36.0,22.0,191.0,88.0,2023-05-01 21:32:25,3.1.7,6.0,19.0,,,7.0,7.0,,,2.0,,,,2.0,,,,,,84.0,,,,,,,,,,
-68,matsciml,,rep-learn,MIT,https://github.com/IntelLabs/matsciml,Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery..,16,True,"['workflows', 'benchmarking']",IntelLabs/matsciml,https://github.com/IntelLabs/matsciml,2022-09-13 20:27:28,2023-12-01 21:58:18.000,2023-12-01 17:55:58,1501.0,168.0,12.0,4.0,58.0,5.0,10.0,83.0,2023-08-31 23:59:40,1.0.0,2.0,8.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-69,Scikit-Matter,,general-tool,BSD-3-Clause,https://github.com/scikit-learn-contrib/scikit-matter,A collection of scikit-learn compatible utilities that implement methods born out of the materials science and..,16,True,['scikit-learn'],scikit-learn-contrib/scikit-matter,https://github.com/scikit-learn-contrib/scikit-matter,2020-10-12 19:23:26,2023-11-20 15:32:27.000,2023-09-22 08:33:28,364.0,2.0,15.0,17.0,150.0,12.0,56.0,65.0,2023-08-24 17:18:49,0.2.0,7.0,13.0,skmatter,conda-forge/skmatter,7.0,7.0,https://pypi.org/project/skmatter,421.0,499.0,https://anaconda.org/conda-forge/skmatter,2023-08-24 19:08:29.551,702.0,2.0,,,,,,,,,,,,,,,,
-70,mlcolvar,,md,MIT,https://github.com/luigibonati/mlcolvar,A unified framework for machine learning collective variables for enhanced sampling simulations.,16,True,['enhanced-sampling'],luigibonati/mlcolvar,https://github.com/luigibonati/mlcolvar,2021-09-21 21:32:04,2023-11-17 11:27:21.000,2023-11-17 11:27:16,815.0,21.0,13.0,5.0,56.0,12.0,36.0,59.0,2023-10-25 08:58:49,1.0.1,8.0,7.0,mlcolvar,,,,https://pypi.org/project/mlcolvar,80.0,80.0,,,,2.0,,,,,,,,,,,,,,,,
-71,MatBench Discovery,,community,MIT,https://github.com/janosh/matbench-discovery,An evaluation framework for machine learning models simulating high-throughput materials discovery.,16,True,"['datasets', 'benchmarking']",janosh/matbench-discovery,https://github.com/janosh/matbench-discovery,2022-06-20 18:32:44,2023-12-01 22:13:04.000,2023-12-01 22:12:59,297.0,32.0,5.0,7.0,41.0,3.0,24.0,44.0,2023-09-13 14:29:49,1.0.0,2.0,5.0,matbench-discovery,,,,https://pypi.org/project/matbench-discovery,32.0,32.0,,,,3.0,,,,,,,,,,,,,,,,
-72,FermiNet,,ml-wft,Apache-2.0,https://github.com/google-deepmind/ferminet,An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations.,15,True,['transformer'],deepmind/ferminet,https://github.com/google-deepmind/ferminet,2020-10-06 12:21:06,2023-11-27 07:54:15.000,2023-11-27 07:53:26,216.0,24.0,102.0,34.0,28.0,,42.0,615.0,,,,18.0,,,,,,,,,,,2.0,,,,,,,,,google-deepmind/ferminet,,,,,,,
-73,Uni-Fold,,biomolecules,Apache-2.0,https://github.com/dptech-corp/Uni-Fold,An open-source platform for developing protein models beyond AlphaFold.,15,True,,dptech-corp/Uni-Fold,https://github.com/dptech-corp/Uni-Fold,2022-07-30 03:37:29,2023-11-30 07:37:27.000,2023-11-30 07:37:23,97.0,2.0,53.0,7.0,76.0,14.0,48.0,313.0,2022-10-19 12:44:31,2.2.0,3.0,7.0,,,,,,,187.0,,,,3.0,,,,,,2618.0,,,,,,,,,,
-74,QML,,general-tool,MIT,https://github.com/qmlcode/qml,QML: Quantum Machine Learning.,15,False,,qmlcode/qml,https://github.com/qmlcode/qml,2017-04-22 04:48:38,2023-07-26 12:01:42.000,2018-09-10 11:14:35,75.0,,80.0,23.0,101.0,27.0,19.0,189.0,,,3.0,2.0,qml,,22.0,22.0,https://pypi.org/project/qml,294.0,294.0,,,,3.0,,,,,,,,,,,,,,,,
-75,gpax,,math,MIT,https://github.com/ziatdinovmax/gpax,Gaussian Processes for Experimental Sciences.,15,True,"['probabilistic', 'active-learning']",ziatdinovmax/gpax,https://github.com/ziatdinovmax/gpax,2021-10-28 13:43:18,2023-12-02 18:09:54.000,2023-12-02 18:09:54,583.0,78.0,16.0,4.0,40.0,5.0,14.0,148.0,2023-10-11 22:13:25,0.1.1,10.0,2.0,gpax,,,,https://pypi.org/project/gpax,142.0,142.0,,,,2.0,,,,,,,,,,,,,,,,
-76,openmm-torch,,md,https://github.com/openmm/openmm-torch#license,https://github.com/openmm/openmm-torch,OpenMM plugin to define forces with neural networks.,15,True,"['ml-iap', 'lang-cpp']",openmm/openmm-torch,https://github.com/openmm/openmm-torch,2019-09-27 18:15:19,2023-11-29 21:24:23.296,2023-10-03 16:21:43,61.0,5.0,22.0,13.0,56.0,16.0,54.0,137.0,2023-10-09 08:49:10,1.4,16.0,7.0,,conda-forge/openmm-torch,,,,,6504.0,https://anaconda.org/conda-forge/openmm-torch,2023-11-29 21:24:23.296,227674.0,3.0,,,,,,,,,,,,,,,,
-77,Automatminer,,general-tool,https://github.com/hackingmaterials/automatminer/blob/main/LICENSE,https://github.com/hackingmaterials/automatminer,An automatic engine for predicting materials properties.,15,False,,hackingmaterials/automatminer,https://github.com/hackingmaterials/automatminer,2018-05-10 18:27:08,2023-11-12 10:09:39.000,2022-01-06 19:39:49,1666.0,,47.0,12.0,233.0,36.0,138.0,131.0,2020-07-28 02:19:07,1.0.3.20200727,17.0,13.0,automatminer,,7.0,7.0,https://pypi.org/project/automatminer,135.0,135.0,,,,3.0,,,,,,,,,,,,,,,,
-78,sGDML,,ml-iap,MIT,https://github.com/stefanch/sGDML,sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model.,15,True,,stefanch/sGDML,https://github.com/stefanch/sGDML,2018-07-11 15:20:30,2023-08-31 12:58:49.000,2023-08-31 12:57:53,205.0,,35.0,8.0,12.0,6.0,11.0,127.0,2023-08-31 12:58:49,1.0.2,15.0,8.0,sgdml,,8.0,8.0,https://pypi.org/project/sgdml,111.0,111.0,,,,2.0,,,,,,,,,,,,,,,,
-79,NNPOps,,ml-iap,MIT,https://github.com/openmm/NNPOps,High-performance operations for neural network potentials.,15,True,"['md', 'lang-cpp']",openmm/NNPOps,https://github.com/openmm/NNPOps,2020-09-10 21:02:00,2023-11-17 10:54:59.000,2023-07-25 21:23:53,94.0,,15.0,10.0,62.0,20.0,33.0,72.0,2023-07-26 11:21:58,0.6,7.0,7.0,,conda-forge/nnpops,,,,,3492.0,https://anaconda.org/conda-forge/nnpops,2023-11-06 09:36:30.898,73337.0,2.0,,,,,,,,,,,,,,,,
-80,Open Databases Integration for Materials Design (OPTIMADE),,datasets,CC-BY-4.0,https://github.com/Materials-Consortia/OPTIMADE,Specification of a common REST API for access to materials databases.,15,True,,Materials-Consortia/OPTIMADE,https://github.com/Materials-Consortia/OPTIMADE,2018-01-08 23:32:29,2023-12-03 16:46:00.000,2023-06-22 15:32:33,1276.0,,35.0,22.0,266.0,72.0,145.0,62.0,2021-07-08 15:20:37,1.1.0,7.0,19.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-81,MODNet,,rep-eng,MIT,https://github.com/ppdebreuck/modnet,MODNet: a framework for machine learning materials properties.,15,True,"['pre-trained', 'small-data', 'transfer-learning']",ppdebreuck/modnet,https://github.com/ppdebreuck/modnet,2020-03-13 07:39:21,2023-12-01 21:36:10.000,2023-11-13 22:45:08,259.0,2.0,28.0,7.0,145.0,13.0,23.0,62.0,2023-07-29 11:00:10,0.4.1,18.0,7.0,,,5.0,5.0,,,,,,,2.0,,,,,,,,,,,,,,,,
-82,benchmarking-gnns,,rep-learn,MIT,https://github.com/graphdeeplearning/benchmarking-gnns,Repository for benchmarking graph neural networks.,14,False,"['single-paper', 'benchmarking']",graphdeeplearning/benchmarking-gnns,https://github.com/graphdeeplearning/benchmarking-gnns,2020-03-03 03:42:50,2023-06-22 04:03:53.000,2022-05-10 13:22:20,45.0,,421.0,59.0,17.0,4.0,61.0,2300.0,,,,5.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-83,escnn,,rep-learn,https://github.com/QUVA-Lab/escnn/blob/master/LICENSE,https://github.com/QUVA-Lab/escnn,Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/.,14,True,,QUVA-Lab/escnn,https://github.com/QUVA-Lab/escnn,2022-03-16 10:15:02,2023-11-27 17:43:58.000,2023-10-17 22:37:11,244.0,4.0,35.0,16.0,32.0,21.0,36.0,255.0,,,,10.0,escnn,,,,https://pypi.org/project/escnn,518.0,518.0,,,,2.0,,,,,,,,,,,,,,,,
-84,gptchem,,lm,MIT,https://github.com/kjappelbaum/gptchem,Use GPT-3 to solve chemistry problems.,14,True,,kjappelbaum/gptchem,https://github.com/kjappelbaum/gptchem,2023-01-06 15:34:32,2023-11-30 09:31:35.000,2023-10-04 11:27:09,147.0,29.0,30.0,8.0,4.0,15.0,2.0,165.0,2023-11-30 09:31:51,0.0.4,2.0,2.0,gptchem,,,,https://pypi.org/project/gptchem,39.0,39.0,,,,2.0,,,,,,,,,,,,,,,,
-85,DADApy,,unsupervised,Apache-2.0,https://github.com/sissa-data-science/DADApy,Distance-based Analysis of DAta-manifolds in python.,14,True,,sissa-data-science/DADApy,https://github.com/sissa-data-science/DADApy,2021-02-16 17:45:23,2023-12-03 16:50:36.000,2023-11-16 14:28:31,654.0,11.0,11.0,8.0,77.0,11.0,15.0,77.0,2023-05-25 16:37:17,0.2.0,3.0,16.0,dadapy,,2.0,2.0,https://pypi.org/project/dadapy,80.0,80.0,,,,1.0,,,,,,,,,,,,,,,,
-86,SpheriCart,,math,Apache-2.0,https://github.com/lab-cosmo/sphericart,Multi-language library for the calculation of spherical harmonics in Cartesian coordinates.,14,True,,lab-cosmo/sphericart,https://github.com/lab-cosmo/sphericart,2023-02-04 15:15:25,2023-11-29 14:24:27.000,2023-11-27 12:47:38,332.0,15.0,5.0,4.0,72.0,9.0,7.0,46.0,2023-04-26 12:06:09,0.3.0,1.0,9.0,sphericart,,1.0,1.0,https://pypi.org/project/sphericart,212.0,212.0,,,,2.0,,,,,,1.0,,,,,,,,,,
-87,mp-pyrho,,data-structures,https://github.com/materialsproject/pyrho,https://github.com/materialsproject/pyrho,Tools for re-griding volumetric quantum chemistry data for machine-learning purposes.,14,True,['ml-dft'],materialsproject/pyrho,https://github.com/materialsproject/pyrho,2020-05-25 22:44:02,2023-11-28 00:13:22.000,2023-03-21 17:22:18,244.0,,6.0,9.0,104.0,1.0,3.0,29.0,2022-10-20 05:07:16,0.3.0,23.0,8.0,mp-pyrho,,18.0,18.0,https://pypi.org/project/mp-pyrho,81.0,81.0,,,,3.0,,,,,,,,,,,,,,,,
-88,KLIFF,,ml-iap,LGPL-2.1,https://github.com/openkim/kliff,KIM-based Learning-Integrated Fitting Framework (KLIFF).,14,True,"['probabilistic', 'workflows']",openkim/kliff,https://github.com/openkim/kliff,2017-08-01 20:33:58,2023-11-28 00:02:15.000,2023-11-16 05:26:53,994.0,10.0,17.0,2.0,105.0,18.0,16.0,27.0,2022-10-07 05:16:11,0.4.1,16.0,10.0,kliff,conda-forge/kliff,,,https://pypi.org/project/kliff,45.0,1767.0,https://anaconda.org/conda-forge/kliff,2023-10-07 06:09:56.646,65461.0,2.0,,,,,,,,,,,,,,,,
-89,Polynomials4ML.jl,,math,MIT,https://github.com/ACEsuit/Polynomials4ML.jl,"Polynomials for ML: fast evaluation, batching, differentiation.",14,True,['lang-julia'],ACEsuit/Polynomials4ML.jl,https://github.com/ACEsuit/Polynomials4ML.jl,2022-09-20 23:05:53,2023-11-25 22:48:16.000,2023-11-23 22:48:55,327.0,41.0,5.0,4.0,34.0,15.0,29.0,12.0,2023-11-15 23:52:25,0.2.7,11.0,9.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-90,n2p2,,ml-iap,GPL-3.0,https://github.com/CompPhysVienna/n2p2,n2p2 - A Neural Network Potential Package.,13,False,['lang-cpp'],CompPhysVienna/n2p2,https://github.com/CompPhysVienna/n2p2,2018-07-25 12:29:17,2023-05-11 16:26:02.000,2022-09-05 10:56:20,387.0,,69.0,12.0,53.0,57.0,85.0,195.0,2022-05-23 12:53:39,2.2.0,11.0,9.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-91,DeepH-pack,,ml-dft,LGPL-3.0,https://github.com/mzjb/DeepH-pack,Deep neural networks for density functional theory Hamiltonian.,13,True,['lang-julia'],mzjb/DeepH-pack,https://github.com/mzjb/DeepH-pack,2022-05-13 02:51:32,2023-08-03 05:36:54.000,2023-07-11 08:11:15,54.0,,27.0,5.0,13.0,5.0,31.0,155.0,2023-07-11 08:13:06,0.2.2,2.0,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-92,DMFF,,ml-iap,LGPL-3.0,https://github.com/deepmodeling/DMFF,DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable..,13,True,,deepmodeling/DMFF,https://github.com/deepmodeling/DMFF,2022-02-14 01:35:50,2023-11-22 10:04:10.000,2023-11-09 06:31:11,427.0,6.0,32.0,11.0,131.0,7.0,14.0,119.0,2023-11-09 14:32:37,1.0.0,4.0,13.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-93,SPICE,,datasets,MIT,https://github.com/openmm/spice-dataset,A collection of QM data for training potential functions.,13,True,"['ml-iap', 'md']",openmm/spice-dataset,https://github.com/openmm/spice-dataset,2021-08-31 18:52:05,2023-10-22 18:03:55.000,2023-10-22 18:03:55,34.0,3.0,5.0,16.0,38.0,12.0,37.0,104.0,2023-08-07 19:52:03,1.1.4,6.0,,,,,,,,13.0,,,,2.0,,,,,,221.0,,,,,,,,,,
-94,PyXtalFF,,ml-iap,MIT,https://github.com/MaterSim/PyXtal_FF,Machine Learning Interatomic Potential Predictions.,13,True,,MaterSim/PyXtal_FF,https://github.com/MaterSim/PyXtal_FF,2019-01-08 08:43:35,2023-08-17 01:22:23.000,2023-08-17 01:22:18,559.0,,19.0,9.0,2.0,9.0,51.0,75.0,2023-06-09 17:17:24,0.2.3,19.0,8.0,pyxtal_ff,,,,https://pypi.org/project/pyxtal_ff,32.0,32.0,,,,2.0,,,,,,,,,,,,,,,,
-95,aviary,,materials-discovery,MIT,https://github.com/CompRhys/aviary,The Wren sits on its Roost in the Aviary.,13,True,,CompRhys/aviary,https://github.com/CompRhys/aviary,2021-09-28 12:29:05,2023-11-10 23:21:39.000,2023-11-10 23:21:36,607.0,4.0,8.0,2.0,51.0,4.0,22.0,30.0,2023-08-10 01:55:58,0.1.1,4.0,4.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
-96,mat2vec,,lm,MIT,https://github.com/materialsintelligence/mat2vec,Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from materials..,12,True,['rep-learn'],materialsintelligence/mat2vec,https://github.com/materialsintelligence/mat2vec,2019-04-25 07:55:30,2023-05-06 22:45:49.000,2023-05-06 22:45:49,55.0,,174.0,39.0,7.0,7.0,17.0,598.0,,,,5.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-97,Crystal Graph Convolutional Neural Networks (CGCNN),,rep-learn,MIT,https://github.com/txie-93/cgcnn,Crystal graph convolutional neural networks for predicting material properties.,12,False,,txie-93/cgcnn,https://github.com/txie-93/cgcnn,2018-03-14 20:41:21,2021-09-06 05:23:51.000,2021-09-06 05:23:38,25.0,,253.0,23.0,7.0,16.0,20.0,535.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-98,AI for Science Resources,,community,GPL-3.0 license,https://github.com/divelab/AIRS/blob/main/Overview/resources.md,"List of resources for AI4Science research, including learning resources.",12,True,,divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2023-11-25 05:19:28.000,2023-11-25 05:19:28,353.0,29.0,37.0,18.0,4.0,,2.0,281.0,,,,24.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-99,Artificial Intelligence for Science (AIRS),,general-tool,GPL-3.0 license,https://github.com/divelab/AIRS,Artificial Intelligence for Science (AIRS).,12,True,"['rep-learn', 'generative', 'ml-iap', 'md', 'ml-dft', 'ml-wft', 'biomolecules']",divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2023-11-25 05:19:28.000,2023-11-25 05:19:28,353.0,29.0,37.0,18.0,4.0,,2.0,281.0,,,,24.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-100,TensorMol,,ml-iap,GPL-3.0,https://github.com/jparkhill/TensorMol,Tensorflow + Molecules = TensorMol.,12,False,['single-paper'],jparkhill/TensorMol,https://github.com/jparkhill/TensorMol,2016-10-28 19:40:11,2021-02-11 00:12:00.000,2018-03-30 12:26:14,1724.0,,70.0,45.0,8.0,18.0,19.0,264.0,2017-11-08 18:05:50,0.1,1.0,12.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-101,ANI-1,,ml-iap,MIT,https://github.com/isayev/ASE_ANI,ANI-1 neural net potential with python interface (ASE).,12,False,,isayev/ASE_ANI,https://github.com/isayev/ASE_ANI,2016-12-08 05:09:32,2020-12-14 19:57:50.000,2020-06-05 22:46:43,111.0,,56.0,33.0,9.0,16.0,21.0,209.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-102,ASAP,,unsupervised,MIT,https://github.com/BingqingCheng/ASAP,ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.,12,True,,BingqingCheng/ASAP,https://github.com/BingqingCheng/ASAP,2019-08-11 12:45:14,2023-08-30 13:55:44.000,2023-08-30 13:55:44,759.0,,25.0,7.0,37.0,6.0,18.0,121.0,2023-08-30 13:54:23,1,1.0,6.0,,,5.0,5.0,,,,,,,2.0,,,,,,,,,,,,,,,,
-103,Librascal,,rep-eng,LGPL-2.1,https://github.com/lab-cosmo/librascal,A scalable and versatile library to generate representations for atomic-scale learning.,12,True,,lab-cosmo/librascal,https://github.com/lab-cosmo/librascal,2018-02-01 08:38:51,2023-11-30 14:48:28.000,2023-11-30 14:48:28,2931.0,1.0,18.0,19.0,200.0,99.0,131.0,74.0,,,3.0,29.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-104,AMPtorch,,general-tool,GPL-3.0,https://github.com/ulissigroup/amptorch,AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch.,12,True,,ulissigroup/amptorch,https://github.com/ulissigroup/amptorch,2019-01-24 15:15:48,2023-07-16 02:11:38.000,2023-07-16 02:08:13,759.0,,32.0,9.0,99.0,5.0,26.0,57.0,2023-07-16 02:11:38,1.0,3.0,14.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-105,Neural fingerprint (nfp),,rep-learn,https://github.com/NREL/nfp/blob/master/LICENSE,https://github.com/NREL/nfp,Keras layers for end-to-end learning with rdkit and pymatgen.,12,False,,NREL/nfp,https://github.com/NREL/nfp,2018-11-20 23:55:23,2022-08-29 21:59:14.000,2022-06-14 22:18:28,143.0,,24.0,7.0,18.0,,6.0,56.0,2022-04-27 17:05:25,0.3.12,13.0,4.0,,,12.0,12.0,,,,,,,2.0,,,,,,,,,,,,,,,,
-106,OpenMM-ML,,md,MIT,https://github.com/openmm/openmm-ml,High level API for using machine learning models in OpenMM simulations.,12,True,['ml-iap'],openmm/openmm-ml,https://github.com/openmm/openmm-ml,2021-02-10 20:55:25,2023-11-08 14:33:27.000,2023-08-21 20:11:13,28.0,,16.0,14.0,23.0,23.0,20.0,56.0,2023-08-21 23:32:24,1.1,5.0,2.0,,conda-forge/openmm-ml,,,,,165.0,https://anaconda.org/conda-forge/openmm-ml,2023-08-21 23:33:29.497,2655.0,3.0,,,,,,,,,,,,,,,,
-107,Pacemaker,,ml-iap,https://github.com/ICAMS/python-ace/blob/master/LICENSE.md,https://cortner.github.io/ACEweb/software/,Python package for fitting atomic cluster expansion (ACE) potentials.,12,True,,ICAMS/python-ace,https://github.com/ICAMS/python-ace,2021-11-19 11:39:54,2023-11-30 11:28:12.000,2023-11-18 19:44:01,136.0,14.0,14.0,3.0,23.0,8.0,30.0,51.0,2022-10-24 19:59:33,0.2.8,2.0,5.0,python-ace,,,,https://pypi.org/project/python-ace,7.0,7.0,,,,2.0,,,,,,,,,,,,,,,,
-108,So3krates (MLFF),,ml-iap,MIT,https://github.com/thorben-frank/mlff,Build neural networks for machine learning force fields with JAX.,12,True,,thorben-frank/mlff,https://github.com/thorben-frank/mlff,2022-09-30 07:40:17,2023-11-09 18:38:08.000,2023-11-09 18:38:08,114.0,13.0,7.0,6.0,11.0,3.0,4.0,40.0,2023-07-12 08:34:56,0.2.0,1.0,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-109,Rascaline,,rep-eng,BSD-3-Clause,https://github.com/Luthaf/rascaline,Computing representations for atomistic machine learning.,12,True,"['lang-rust', 'lang-cpp']",Luthaf/rascaline,https://github.com/Luthaf/rascaline,2020-09-24 14:28:34,2023-11-30 15:04:20.000,2023-11-30 15:04:19,493.0,41.0,11.0,6.0,215.0,23.0,23.0,34.0,,,,13.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-110,GlassPy,,rep-eng,GPL-3.0,https://github.com/drcassar/glasspy,Python module for scientists working with glass materials.,12,True,,drcassar/glasspy,https://github.com/drcassar/glasspy,2019-07-18 23:15:43,2023-10-05 19:51:40.000,2023-10-05 19:31:00,324.0,5.0,6.0,5.0,8.0,1.0,4.0,18.0,2023-10-05 19:32:01,0.4.5,7.0,,glasspy,,2.0,2.0,https://pypi.org/project/glasspy,79.0,79.0,,,,2.0,,,,,,,,,,,,,,,,
-111,Compositionally-Restricted Attention-Based Network (CrabNet),,rep-learn,MIT,https://github.com/sparks-baird/CrabNet,Predict materials properties using only the composition information!.,12,True,,sparks-baird/CrabNet,https://github.com/sparks-baird/CrabNet,2021-09-17 07:58:15,2023-06-19 09:35:52.000,2023-06-19 09:35:52,427.0,,3.0,1.0,54.0,15.0,2.0,11.0,2023-06-07 01:07:33,2.0.8,5.0,5.0,crabnet,,11.0,11.0,https://pypi.org/project/crabnet,198.0,198.0,,,,2.0,,,,,,,,,,,,,,,,
-112,CCS_fit,,ml-iap,GPL-3.0,https://github.com/Teoroo-CMC/CCS,Curvature Constrained Splines.,12,True,,Teoroo-CMC/CCS,https://github.com/Teoroo-CMC/CCS,2021-12-13 14:29:53,2023-11-20 17:26:52.000,2023-10-04 14:41:11,760.0,3.0,9.0,3.0,12.0,8.0,6.0,6.0,2023-10-04 14:41:14,0.22.4,100.0,8.0,ccs_fit,,,,https://pypi.org/project/ccs_fit,59.0,88.0,,,,2.0,,,,,,381.0,,,,,,,,,,
-113,OpenChem,,general-tool,MIT,https://github.com/Mariewelt/OpenChem,OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research.,11,False,,Mariewelt/OpenChem,https://github.com/Mariewelt/OpenChem,2018-07-10 01:27:33,2023-11-26 05:03:36.000,2022-04-27 19:27:40,444.0,,106.0,37.0,12.0,15.0,2.0,626.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-114,Deep Learning for Molecules and Materials Book,,educational,https://github.com/whitead/dmol-book/blob/main/LICENSE,https://dmol.pub/,Deep learning for molecules and materials book.,11,True,,whitead/dmol-book,https://github.com/whitead/dmol-book,2020-08-19 19:24:32,2023-07-02 18:02:57.000,2023-07-02 18:02:56,558.0,,101.0,17.0,92.0,27.0,128.0,541.0,,,,19.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
-115,ReLeaSE,,reinforcement-learning,MIT,https://github.com/isayev/ReLeaSE,Deep Reinforcement Learning for de-novo Drug Design.,11,False,['drug-discovery'],isayev/ReLeaSE,https://github.com/isayev/ReLeaSE,2018-04-26 14:50:34,2021-12-08 19:49:36.000,2021-12-08 19:49:36,160.0,,126.0,19.0,9.0,27.0,8.0,318.0,,,,5.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
-116,DeepLearningLifeSciences,,educational,MIT,https://github.com/deepchem/DeepLearningLifeSciences,Example code from the book Deep Learning for the Life Sciences.,11,False,,deepchem/DeepLearningLifeSciences,https://github.com/deepchem/DeepLearningLifeSciences,2019-02-05 17:16:18,2021-09-17 05:10:37.000,2021-09-17 05:10:37,52.0,,140.0,23.0,15.0,9.0,10.0,307.0,2019-10-28 18:46:28,1.0,1.0,10.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
-117,SISSO,,rep-eng,Apache-2.0,https://github.com/rouyang2017/SISSO,A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models.,11,True,['lang-fortran'],rouyang2017/SISSO,https://github.com/rouyang2017/SISSO,2017-10-16 11:31:57,2023-09-12 08:50:38.000,2023-09-12 08:50:38,166.0,2.0,65.0,6.0,2.0,2.0,51.0,193.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-118,jarvis-tools-notebooks,,educational,NIST,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/.,11,True,,JARVIS-Materials-Design/jarvis-tools-notebooks,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,2020-06-27 20:22:02,2023-10-16 02:44:55.000,2023-10-16 02:44:55,542.0,25.0,21.0,4.0,38.0,,,44.0,,,,5.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
-119,SIMPLE-NN,,ml-iap,GPL-3.0,https://github.com/MDIL-SNU/SIMPLE-NN,SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network).,11,False,,MDIL-SNU/SIMPLE-NN,https://github.com/MDIL-SNU/SIMPLE-NN,2018-03-26 23:53:35,2022-01-27 05:04:05.000,2022-01-27 05:04:05,586.0,,18.0,12.0,91.0,4.0,26.0,44.0,2021-09-23 01:41:42,1.1.1,9.0,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-120,nlcc,,lm,MIT,https://github.com/whitead/nlcc,Natural language computational chemistry command line interface.,11,True,['single-paper'],whitead/nlcc,https://github.com/whitead/nlcc,2021-08-19 18:23:52,2023-02-04 03:07:56.000,2023-02-04 03:06:33,144.0,,6.0,5.0,1.0,,9.0,43.0,2023-02-04 03:07:56,0.6.0,10.0,3.0,nlcc,,,,https://pypi.org/project/nlcc,30.0,30.0,,,,3.0,,,,,,,,,,,,,,,,
-121,cmlkit,,rep-eng,MIT,https://github.com/sirmarcel/cmlkit,tools for machine learning in condensed matter physics and quantum chemistry.,11,False,['benchmarking'],sirmarcel/cmlkit,https://github.com/sirmarcel/cmlkit,2018-05-31 07:56:52,2022-04-01 00:39:14.000,2022-03-25 22:27:04,526.0,,6.0,3.0,1.0,6.0,2.0,32.0,,,,,cmlkit,,4.0,4.0,https://pypi.org/project/cmlkit,151.0,151.0,,,,2.0,,,,,,,,,,,,,,,,
-122,synspace,,generative,MIT,https://github.com/whitead/synspace,Synthesis generative model.,11,True,,whitead/synspace,https://github.com/whitead/synspace,2022-12-28 00:59:14,2023-04-15 22:42:57.000,2023-04-15 18:04:16,27.0,,3.0,3.0,1.0,2.0,1.0,31.0,2023-04-15 22:42:57,0.3.0,3.0,2.0,synspace,,6.0,6.0,https://pypi.org/project/synspace,664.0,664.0,,,,2.0,,,,,,,,,,,,,,,,
-123,PACE,,md,https://github.com/ICAMS/lammps-user-pace/blob/main/LICENSE,https://github.com/ICAMS/lammps-user-pace,"The LAMMPS ML-IAP `pair_style pace`, aka Atomic Cluster Expansion (ACE), aka ML-PACE,..",11,True,,ICAMS/lammps-user-pace,https://github.com/ICAMS/lammps-user-pace,2021-02-25 10:04:48,2023-11-27 21:29:13.000,2023-11-27 21:28:13,59.0,13.0,10.0,5.0,16.0,1.0,5.0,21.0,2023-11-25 21:58:41,.2023.11.25.fix,6.0,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-124,EDM,,generative,MIT,https://github.com/ehoogeboom/e3_diffusion_for_molecules,E(3) Equivariant Diffusion Model for Molecule Generation in 3D.,10,False,,ehoogeboom/e3_diffusion_for_molecules,https://github.com/ehoogeboom/e3_diffusion_for_molecules,2022-04-15 14:34:35,2022-07-10 17:56:18.000,2022-07-10 17:56:12,6.0,,81.0,7.0,,1.0,32.0,319.0,,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-125,Awesome Materials Informatics,,community,https://github.com/tilde-lab/awesome-materials-informatics#license,https://github.com/tilde-lab/awesome-materials-informatics,Curated list of known efforts in materials informatics = modern materials science.,10,True,,tilde-lab/awesome-materials-informatics,https://github.com/tilde-lab/awesome-materials-informatics,2018-02-15 15:14:16,2023-10-30 16:02:26.000,2023-10-30 16:02:26,134.0,4.0,74.0,15.0,54.0,,8.0,311.0,2023-03-02 19:56:59,2023.03.02,1.0,19.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-126,GDC,,rep-learn,MIT,https://github.com/gasteigerjo/gdc,"Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019).",10,True,['generative'],gasteigerjo/gdc,https://github.com/gasteigerjo/gdc,2019-10-26 16:05:11,2023-04-26 14:22:40.000,2023-04-26 14:22:40,28.0,,38.0,3.0,1.0,,10.0,229.0,,,,3.0,,,1.0,1.0,,,,,,,2.0,,,,,,,,,,,,,,,,
-127,Neural Force Field,,ml-iap,MIT,https://github.com/learningmatter-mit/NeuralForceField,Neural Network Force Field based on PyTorch.,10,True,['pre-trained'],learningmatter-mit/NeuralForceField,https://github.com/learningmatter-mit/NeuralForceField,2020-10-04 15:17:41,2023-07-25 15:37:02.000,2023-07-25 15:37:01,122.0,,42.0,7.0,4.0,,16.0,200.0,,,,10.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-128,PiNN,,ml-iap,BSD-3-Clause,https://github.com/Teoroo-CMC/PiNN,A Python library for building atomic neural networks.,10,True,,Teoroo-CMC/PiNN,https://github.com/Teoroo-CMC/PiNN,2019-10-04 08:13:18,2023-11-15 09:09:29.000,2023-09-28 09:19:27,130.0,4.0,27.0,7.0,6.0,1.0,5.0,98.0,2019-10-09 09:21:30,0.3.0,1.0,2.0,,,,,,,4.0,,,,3.0,teoroo/pinn,https://hub.docker.com/r/teoroo/pinn,2023-10-19 13:57:40.768924,,224.0,,,,,,,,,,,
-129,MolSkill,,lm,MIT,https://github.com/microsoft/molskill,Extracting medicinal chemistry intuition via preference machine learning.,10,True,"['drug-discovery', 'recommender']",microsoft/molskill,https://github.com/microsoft/molskill,2023-01-12 13:48:31,2023-10-31 17:03:36.000,2023-10-31 17:03:36,81.0,1.0,7.0,8.0,8.0,2.0,3.0,88.0,2023-08-04 12:22:15,1.2b,5.0,4.0,,msr-ai4science/molskill,,,,,14.0,https://anaconda.org/msr-ai4science/molskill,2023-06-18 17:27:43.196,140.0,3.0,,,,,,,,,,,,,,,,
-130,flare++,,active-learning,MIT,https://github.com/mir-group/flare_pp,A many-body extension of the FLARE code.,10,False,"['lang-cpp', 'ml-iap']",mir-group/flare_pp,https://github.com/mir-group/flare_pp,2019-11-20 22:46:32,2022-02-27 21:05:09.000,2022-02-24 19:00:50,989.0,,6.0,6.0,28.0,8.0,17.0,35.0,,,,10.0,flare_pp,,,,https://pypi.org/project/flare_pp,38.0,38.0,,,,2.0,,,,,,,,,,,,,,,,
-131,Finetuna,,active-learning,MIT,https://github.com/ulissigroup/finetuna,Active Learning for Machine Learning Potentials.,10,True,,ulissigroup/finetuna,https://github.com/ulissigroup/finetuna,2020-09-22 14:39:52,2023-10-31 18:51:45.000,2023-10-03 18:18:06,1198.0,2.0,10.0,3.0,39.0,4.0,15.0,34.0,,,,11.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-132,SchNetPack G-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/schnetpack-gschnet,G-SchNet extension for SchNetPack.,10,True,,atomistic-machine-learning/schnetpack-gschnet,https://github.com/atomistic-machine-learning/schnetpack-gschnet,2022-04-21 12:34:13,2023-11-07 11:31:47.000,2023-11-07 11:31:47,117.0,1.0,6.0,4.0,,1.0,9.0,31.0,2023-04-25 14:09:07,1.0.0,2.0,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-133,NeuralXC,,ml-dft,BSD-3-Clause,https://github.com/semodi/neuralxc,Implementation of a machine learned density functional.,10,False,,semodi/neuralxc,https://github.com/semodi/neuralxc,2019-03-14 18:13:40,2022-11-30 11:39:22.000,2021-07-05 21:36:23,337.0,,9.0,5.0,9.0,5.0,5.0,30.0,,,3.0,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-134,OpenKIM,,datasets,LGPL-2.1,https://openkim.org/,"The Open Knowledgebase of Interatomic Models (OpenKIM) aims to be an online resource for standardized testing, long-..",10,False,"['knowledge-base', 'pre-trained']",openkim/kim-api,https://github.com/openkim/kim-api,2014-07-28 21:21:08,2023-08-16 00:09:44.000,2022-03-17 23:01:36,2371.0,,19.0,11.0,55.0,17.0,18.0,29.0,,,,23.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-135,NNsforMD,,ml-iap,MIT,https://github.com/aimat-lab/NNsForMD,"Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings.",10,False,,aimat-lab/NNsForMD,https://github.com/aimat-lab/NNsForMD,2020-08-31 11:14:18,2022-11-10 13:04:49.000,2022-11-10 13:04:45,265.0,,3.0,3.0,,,,10.0,2022-04-12 15:10:32,2.0.0,5.0,2.0,pyNNsMD,,,,https://pypi.org/project/pyNNsMD,39.0,39.0,,,,3.0,,,,,,,,,,,,,,,,
-136,ACEfit,,ml-iap,MIT,https://github.com/ACEsuit/ACEfit.jl,,10,True,['lang-julia'],ACEsuit/ACEfit.jl,https://github.com/ACEsuit/ACEfit.jl,2022-01-01 00:09:17,2023-11-03 18:46:32.000,2023-08-18 16:46:11,210.0,,3.0,4.0,18.0,22.0,32.0,5.0,,,,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-137,SE(3)-Transformers,,rep-learn,MIT,https://github.com/FabianFuchsML/se3-transformer-public,code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503.,9,False,"['single-paper', 'transformer']",FabianFuchsML/se3-transformer-public,https://github.com/FabianFuchsML/se3-transformer-public,2020-08-31 10:36:57,2023-07-10 05:13:25.000,2021-11-18 09:11:56,63.0,,66.0,16.0,5.0,9.0,17.0,427.0,,,,2.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-138,molecularGNN_smiles,,rep-learn,Apache-2.0,https://github.com/masashitsubaki/molecularGNN_smiles,"The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius..",9,False,,masashitsubaki/molecularGNN_smiles,https://github.com/masashitsubaki/molecularGNN_smiles,2018-11-06 00:25:26,2020-11-28 02:04:45.000,2020-11-28 02:04:45,79.0,,70.0,6.0,,6.0,1.0,255.0,,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-139,DimeNet,,ml-iap,https://github.com/gasteigerjo/dimenet/blob/master/LICENSE.md,https://github.com/gasteigerjo/dimenet,"DimeNet and DimeNet++ models, as proposed in Directional Message Passing for Molecular Graphs (ICLR 2020) and Fast and..",9,True,,gasteigerjo/dimenet,https://github.com/gasteigerjo/dimenet,2020-02-14 12:40:15,2023-10-03 09:57:19.000,2023-10-03 09:57:19,103.0,1.0,56.0,5.0,,1.0,29.0,253.0,,,,2.0,,,1.0,1.0,,,,,,,3.0,,,,,,,,,,,,,,,,
-140,Allegro,,ml-iap,MIT,https://github.com/mir-group/allegro,Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic..,9,True,,mir-group/allegro,https://github.com/mir-group/allegro,2022-02-06 23:50:40,2023-05-08 21:16:45.000,2023-05-08 21:16:45,38.0,,36.0,18.0,2.0,8.0,13.0,247.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-141,SchNet,,ml-iap,MIT,https://github.com/atomistic-machine-learning/SchNet,SchNet - a deep learning architecture for quantum chemistry.,9,False,,atomistic-machine-learning/SchNet,https://github.com/atomistic-machine-learning/SchNet,2017-10-03 11:52:20,2018-09-04 08:42:35.000,2018-09-04 08:42:34,53.0,,58.0,16.0,,1.0,2.0,189.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-142,QDF for molecule,,ml-esm,MIT,https://github.com/masashitsubaki/QuantumDeepField_molecule,"Quantum deep field: data-driven wave function, electron density generation, and energy prediction and extrapolation..",9,False,,masashitsubaki/QuantumDeepField_molecule,https://github.com/masashitsubaki/QuantumDeepField_molecule,2020-11-11 01:06:09,2021-02-20 03:46:18.000,2021-02-20 03:46:09,20.0,,37.0,4.0,,,3.0,176.0,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,
-143,GemNet,,ml-iap,https://github.com/TUM-DAML/gemnet_pytorch/blob/master/LICENSE,https://github.com/TUM-DAML/gemnet_pytorch,"GemNet model in PyTorch, as proposed in GemNet: Universal Directional Graph Neural Networks for Molecules (NeurIPS..",9,True,,TUM-DAML/gemnet_pytorch,https://github.com/TUM-DAML/gemnet_pytorch,2021-10-11 07:30:36,2023-04-26 14:20:12.000,2023-04-26 14:20:12,36.0,,25.0,4.0,1.0,,14.0,159.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-144,DeePKS-kit,,ml-dft,LGPL-3.0,https://github.com/deepmodeling/deepks-kit,a package for developing machine learning-based chemically accurate energy and density functional models.,9,True,,deepmodeling/deepks-kit,https://github.com/deepmodeling/deepks-kit,2020-07-29 03:27:50,2023-08-24 00:29:24.000,2023-04-01 01:14:46,380.0,,31.0,13.0,39.0,2.0,9.0,95.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-145,ACE.jl,,ml-iap,https://github.com/ACEsuit/ACE.jl/blob/main/license/mit.md,https://github.com/ACEsuit/ACE.jl,Parameterisation of Equivariant Properties of Particle Systems.,9,True,['lang-julia'],ACEsuit/ACE.jl,https://github.com/ACEsuit/ACE.jl,2019-11-30 16:22:51,2023-06-09 21:31:30.000,2023-06-09 21:29:10,912.0,,15.0,8.0,65.0,24.0,58.0,62.0,,,,12.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-146,GATGNN: Global Attention Graph Neural Network,,rep-learn,MIT,https://github.com/superlouis/GATGNN,Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials..,9,False,,superlouis/GATGNN,https://github.com/superlouis/GATGNN,2020-06-21 03:27:36,2022-10-03 21:57:33.000,2022-10-03 21:57:33,99.0,,18.0,8.0,,3.0,3.0,61.0,2021-04-05 06:49:29,0.2,2.0,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-147,PROPhet,,ml-dft,GPL-3.0,https://github.com/biklooost/PROPhet,PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches.,9,False,"['ml-iap', 'md', 'single-paper', 'lang-cpp']",biklooost/PROPhet,https://github.com/biklooost/PROPhet,2016-09-16 16:21:06,2018-04-19 02:09:46.000,2018-04-19 02:00:46,120.0,,26.0,14.0,6.0,8.0,7.0,61.0,2018-04-15 16:55:15,1.2,3.0,4.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-148,hippynn,,rep-learn,https://github.com/lanl/hippynn/blob/main/LICENSE.txt,https://github.com/lanl/hippynn,python library for atomistic machine learning.,9,True,['workflows'],lanl/hippynn,https://github.com/lanl/hippynn,2021-11-17 00:45:13,2023-11-22 21:14:51.000,2023-11-15 23:40:55,109.0,8.0,19.0,7.0,42.0,2.0,4.0,48.0,,,2.0,11.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-149,DeepErwin,,ml-wft,https://github.com/mdsunivie/deeperwin/blob/master/LICENSE,https://github.com/mdsunivie/deeperwin,DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions..,9,True,,mdsunivie/deeperwin,https://github.com/mdsunivie/deeperwin,2021-06-14 15:18:32,2023-10-16 07:46:05.000,2023-10-16 07:46:05,57.0,2.0,5.0,3.0,3.0,,11.0,36.0,2022-07-18 10:18:25,arxiv_2105.08351v2,2.0,6.0,deeperwin,,,,https://pypi.org/project/deeperwin,49.0,49.0,,,,3.0,,,,,,,,,,,,,,,,
-150,iam-notebooks,,educational,Apache-2.0,https://github.com/ceriottm/iam-notebooks,Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling.,9,True,,ceriottm/iam-notebooks,https://github.com/ceriottm/iam-notebooks,2020-11-23 21:27:41,2023-08-07 23:02:34.000,2023-08-07 23:02:34,228.0,,4.0,4.0,7.0,3.0,,22.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-151,ACE1.jl,,ml-iap,https://github.com/ACEsuit/ACE1.jl/blob/main/ASL.md,https://acesuit.github.io/,Atomic Cluster Expansion for Modelling Invariant Atomic Properties.,9,True,['lang-julia'],ACEsuit/ACE1.jl,https://github.com/ACEsuit/ACE1.jl,2022-01-14 19:52:49,2023-11-13 19:43:52.000,2023-11-13 19:43:52,551.0,5.0,4.0,5.0,28.0,22.0,24.0,19.0,,,,7.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-152,wfl,,ml-iap,,https://github.com/libAtoms/workflow,Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows.,9,True,"['workflows', 'htc']",libAtoms/workflow,https://github.com/libAtoms/workflow,2021-11-04 17:03:34,2023-12-01 16:27:05.000,2023-10-30 13:46:29,939.0,20.0,14.0,9.0,142.0,62.0,68.0,18.0,,,,14.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-153,BenchML,,rep-eng,Apache-2.0,https://github.com/capoe/benchml,ML benchmarking and pipeling framework.,9,True,['benchmarking'],capoe/benchml,https://github.com/capoe/benchml,2020-04-28 13:26:29,2023-05-24 15:13:06.000,2023-05-24 15:04:57,341.0,,3.0,5.0,7.0,3.0,10.0,14.0,,,,9.0,benchml,,,,https://pypi.org/project/benchml,50.0,50.0,,,,3.0,,,,,,,,,,,,,,,,
-154,RDKit Tutorials,,educational,https://github.com/rdkit/rdkit-tutorials/blob/master/LICENSE,https://github.com/rdkit/rdkit-tutorials,Tutorials to learn how to work with the RDKit.,8,True,,rdkit/rdkit-tutorials,https://github.com/rdkit/rdkit-tutorials,2016-10-07 03:34:01,2023-03-19 13:36:55.000,2023-03-19 13:36:55,68.0,,67.0,16.0,7.0,3.0,1.0,215.0,,,,5.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-155,G-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/G-SchNet,G-SchNet - a generative model for 3d molecular structures.,8,True,,atomistic-machine-learning/G-SchNet,https://github.com/atomistic-machine-learning/G-SchNet,2019-10-21 13:48:59,2023-03-24 12:05:41.000,2023-03-24 12:05:41,64.0,,23.0,6.0,,,10.0,121.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-156,ANI-1 Dataset,,datasets,MIT,https://github.com/isayev/ANI1_dataset,A data set of 20 million calculated off-equilibrium conformations for organic molecules.,8,False,,isayev/ANI1_dataset,https://github.com/isayev/ANI1_dataset,2017-08-07 20:08:46,2022-08-08 15:56:17.000,2022-08-08 15:56:17,25.0,,19.0,12.0,2.0,6.0,3.0,91.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-157,MoleculeNet Leaderboard,,datasets,MIT,https://github.com/deepchem/moleculenet,,8,False,['benchmarking'],deepchem/moleculenet,https://github.com/deepchem/moleculenet,2020-02-24 18:14:05,2021-04-29 19:51:06.000,2021-04-29 19:51:06,78.0,,19.0,5.0,15.0,23.0,5.0,77.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-158,cG-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/cG-SchNet,cG-SchNet - a conditional generative neural network for 3d molecular structures.,8,True,,atomistic-machine-learning/cG-SchNet,https://github.com/atomistic-machine-learning/cG-SchNet,2021-12-02 15:35:18,2023-03-24 12:09:56.000,2023-03-24 12:09:56,28.0,,14.0,4.0,,,3.0,43.0,2022-02-21 13:36:41,1.0,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-159,Sketchmap,,unsupervised,GPL-3.0,https://github.com/lab-cosmo/sketchmap,Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular.,8,True,['lang-cpp'],lab-cosmo/sketchmap,https://github.com/lab-cosmo/sketchmap,2014-05-20 09:33:32,2023-05-24 22:56:06.000,2023-05-24 22:47:50,64.0,,10.0,29.0,1.0,3.0,5.0,40.0,,,,8.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-160,DeeperGATGNN,,rep-learn,MIT,https://github.com/usccolumbia/deeperGATGNN,Scalable graph neural networks for materials property prediction.,8,True,,usccolumbia/deeperGATGNN,https://github.com/usccolumbia/deeperGATGNN,2021-09-29 17:31:02,2023-04-19 18:59:51.000,2023-04-19 18:59:31,24.0,,7.0,3.0,1.0,,7.0,35.0,2022-03-08 02:14:28,1.0,1.0,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-161,GAP,,ml-iap,https://github.com/libAtoms/GAP/blob/main/LICENSE.md,https://libatoms.github.io/,Gaussian Approximation Potential (GAP).,8,True,,libAtoms/GAP,https://github.com/libAtoms/GAP,2021-03-22 14:48:56,2023-10-15 17:47:46.000,2023-10-15 17:42:33,200.0,1.0,19.0,10.0,63.0,,,34.0,,,,12.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-162,SNAP,,ml-iap,BSD-3-Clause,https://github.com/materialsvirtuallab/snap,Repository for spectral neighbor analysis potential (SNAP) model development.,8,False,,materialsvirtuallab/snap,https://github.com/materialsvirtuallab/snap,2017-06-26 21:56:00,2020-06-30 05:20:37.000,2020-06-30 05:20:37,38.0,,16.0,10.0,1.0,1.0,3.0,32.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-163,SIMPLE-NN v2,,ml-iap,GPL-3.0,https://github.com/MDIL-SNU/SIMPLE-NN_v2,,8,False,,MDIL-SNU/SIMPLE-NN_v2,https://github.com/MDIL-SNU/SIMPLE-NN_v2,2021-03-02 09:36:49,2023-10-11 05:12:51.000,2023-10-11 05:12:51,502.0,4.0,16.0,5.0,87.0,3.0,7.0,31.0,,,,12.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-164,Atomistic Adversarial Attacks,,ml-iap,MIT,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,Code for performing adversarial attacks on atomistic systems using NN potentials.,8,False,['probabilistic'],learningmatter-mit/Atomistic-Adversarial-Attacks,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,2021-03-28 17:39:52,2022-10-03 16:19:31.000,2022-10-03 16:19:29,33.0,,6.0,5.0,1.0,,1.0,25.0,2021-07-19 18:09:36,1.0.1,1.0,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-165,CGAT,,rep-learn,MIT,https://github.com/hyllios/CGAT,Crystal graph attention neural networks for materials prediction.,8,True,,hyllios/CGAT,https://github.com/hyllios/CGAT,2021-03-28 09:51:15,2023-07-18 12:04:35.000,2023-01-10 22:31:07,153.0,,7.0,3.0,1.0,,1.0,17.0,2023-07-18 12:04:35,0.1,1.0,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-166,GElib,,math,MPL-2.0,https://github.com/risi-kondor/GElib,C++/CUDA library for SO(3) equivariant operations.,8,True,['lang-cpp'],risi-kondor/GElib,https://github.com/risi-kondor/GElib,2021-08-24 20:56:40,2023-11-20 05:48:53.000,2023-10-25 18:07:13,597.0,17.0,2.0,2.0,2.0,2.0,3.0,16.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-167,SALTED,,ml-dft,GPL-3.0,https://github.com/andreagrisafi/SALTED,Symmetry-Adapted Learning of Three-dimensional Electron Densities.,8,True,,andreagrisafi/SALTED,https://github.com/andreagrisafi/SALTED,2020-01-22 10:24:29,2023-11-30 15:42:02.000,2023-11-30 15:42:02,242.0,74.0,2.0,1.0,10.0,,2.0,14.0,2023-04-10 16:25:44,2.0.0,1.0,13.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-168,UVVisML,,rep-learn,MIT,https://github.com/learningmatter-mit/uvvisml,Predict optical properties of molecules with machine learning.,8,True,"['optical-properties', 'single-paper', 'probabilistic']",learningmatter-mit/uvvisml,https://github.com/learningmatter-mit/uvvisml,2021-10-13 05:58:48,2023-05-26 22:35:14.000,2023-05-26 22:35:14,17.0,,5.0,4.0,1.0,,,14.0,2022-02-06 18:14:14,0.0.2,2.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-169,Equisolve,,general-tool,BSD-3-Clause,https://github.com/lab-cosmo/equisolve,A ML toolkit package utilizing the metatensor data format to build models for the prediction of equivariant properties..,8,False,['ml-iap'],lab-cosmo/equisolve,https://github.com/lab-cosmo/equisolve,2022-10-04 15:29:19,2023-10-27 10:03:59.000,2023-10-27 09:55:17,55.0,18.0,2.0,14.0,43.0,19.0,4.0,4.0,,,,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-170,MAChINE,,educational,MIT,https://github.com/aimat-lab/MAChINE,Client-Server Web App to introduce usage of ML in materials science to beginners.,8,False,,aimat-lab/MAChINE,https://github.com/aimat-lab/MAChINE,2023-04-17 14:29:06,2023-09-29 14:20:12.000,2023-09-29 10:20:31,1026.0,20.0,,,7.0,9.0,23.0,1.0,,,,7.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-171,Equiformer,,rep-learn,MIT,https://github.com/atomicarchitects/equiformer,[ICLR23 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs.,7,True,['transformer'],atomicarchitects/equiformer,https://github.com/atomicarchitects/equiformer,2023-02-28 00:21:30,2023-06-21 08:04:30.000,2023-06-21 08:03:53,3.0,,29.0,5.0,1.0,5.0,7.0,143.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-172,BestPractices,,educational,MIT,https://github.com/anthony-wang/BestPractices,Things that you should (and should not) do in your Materials Informatics research.,7,True,,anthony-wang/BestPractices,https://github.com/anthony-wang/BestPractices,2020-05-05 19:41:25,2023-11-17 02:58:25.000,2023-11-17 02:58:25,17.0,2.0,66.0,7.0,8.0,5.0,2.0,141.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-173,A Highly Opinionated List of Open-Source Materials Informatics Resources,,community,MIT,https://github.com/ncfrey/resources,A Highly Opinionated List of Open Source Materials Informatics Resources.,7,False,,ncfrey/resources,https://github.com/ncfrey/resources,2020-11-17 23:47:07,2022-02-18 13:37:51.000,2022-02-18 13:37:51,8.0,,19.0,9.0,,,,96.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-174,PhysNet,,ml-iap,MIT,https://github.com/MMunibas/PhysNet,Code for training PhysNet models.,7,False,['electrostatics'],MMunibas/PhysNet,https://github.com/MMunibas/PhysNet,2019-03-28 09:05:22,2022-10-16 17:45:42.000,2020-12-07 11:09:20,4.0,,26.0,9.0,1.0,5.0,,85.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-175,AIMNet,,ml-iap,MIT,https://github.com/aiqm/aimnet,Atoms In Molecules Neural Network Potential.,7,False,['single-paper'],aiqm/aimnet,https://github.com/aiqm/aimnet,2018-09-26 17:28:37,2019-11-21 23:49:01.000,2019-11-21 23:49:00,7.0,,20.0,10.0,2.0,4.0,,79.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-176,DTNN,,rep-learn,MIT,https://github.com/atomistic-machine-learning/dtnn,Deep Tensor Neural Network.,7,False,,atomistic-machine-learning/dtnn,https://github.com/atomistic-machine-learning/dtnn,2017-03-10 14:40:05,2017-07-11 08:26:15.000,2017-07-11 08:25:39,9.0,,30.0,15.0,,,3.0,76.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-177,JAXChem,,general-tool,MIT,https://github.com/deepchem/jaxchem,JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling.,7,False,,deepchem/jaxchem,https://github.com/deepchem/jaxchem,2020-05-11 18:54:41,2020-07-15 05:02:21.000,2020-07-15 04:55:41,96.0,,9.0,7.0,13.0,1.0,1.0,74.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-178,Cormorant,,rep-learn,https://github.com/risilab/cormorant/blob/master/LICENSE,https://github.com/risilab/cormorant,Codebase for Cormorant Neural Networks.,7,False,,risilab/cormorant,https://github.com/risilab/cormorant,2019-10-27 18:22:07,2022-05-11 12:49:05.000,2020-03-11 15:25:51,160.0,,10.0,6.0,1.0,3.0,,55.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-179,uncertainty_benchmarking,,general-tool,,https://github.com/ulissigroup/uncertainty_benchmarking,Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions.,7,False,"['benchmarking', 'probabilistic']",ulissigroup/uncertainty_benchmarking,https://github.com/ulissigroup/uncertainty_benchmarking,2019-08-28 19:39:28,2021-06-07 23:29:39.000,2021-06-07 23:27:19,265.0,,6.0,6.0,1.0,,,35.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-180,torchchem,,general-tool,MIT,https://github.com/deepchem/torchchem,An experimental repo for experimenting with PyTorch models.,7,False,,deepchem/torchchem,https://github.com/deepchem/torchchem,2020-03-07 17:06:44,2023-03-24 23:13:19.000,2020-05-01 20:12:23,49.0,,14.0,8.0,27.0,2.0,1.0,34.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-181,graphite,,rep-learn,MIT,https://github.com/LLNL/graphite,A repository for implementing graph network models based on atomic structures.,7,True,,llnl/graphite,https://github.com/LLNL/graphite,2022-06-27 19:15:27,2023-10-04 21:51:25.000,2023-10-04 21:51:05,23.0,1.0,6.0,5.0,4.0,2.0,1.0,29.0,,,,2.0,,,7.0,7.0,,,,,,,3.0,,,,,,,,,,,,,,,,
-182,AdsorbML,,rep-learn,MIT,https://github.com/Open-Catalyst-Project/AdsorbML,,7,True,"['surface-science', 'single-paper']",Open-Catalyst-Project/AdsorbML,https://github.com/Open-Catalyst-Project/AdsorbML,2022-11-30 01:38:20,2023-07-31 16:28:14.000,2023-07-31 16:28:09,56.0,,5.0,6.0,10.0,,1.0,25.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-183,SkipAtom,,rep-eng,MIT,https://github.com/lantunes/skipatom,"Distributed representations of atoms, inspired by the Skip-gram model.",7,False,,lantunes/skipatom,https://github.com/lantunes/skipatom,2021-06-19 13:09:13,2023-07-16 19:28:39.000,2022-05-04 13:18:30,46.0,,3.0,2.0,7.0,3.0,1.0,22.0,,,1.0,,skipatom,conda-forge/skipatom,,,https://pypi.org/project/skipatom,28.0,98.0,https://anaconda.org/conda-forge/skipatom,2023-06-18 08:42:05.505,1194.0,3.0,,,,,,,,,,,,,,,,
-184,escnn_jax,,rep-learn,https://github.com/emilemathieu/escnn_jax/blob/master/LICENSE,https://github.com/emilemathieu/escnn_jax,Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/.,7,True,,emilemathieu/escnn_jax,https://github.com/emilemathieu/escnn_jax,2023-06-15 09:45:45,2023-06-28 14:40:32.000,2023-06-28 14:39:56,203.0,,2.0,,,,,21.0,,,,8.0,escnn_jax,,,,https://pypi.org/project/escnn_jax,,,,,,3.0,,,,,,,,,,,,,,,,
-185,BOSS,,materials-discovery,,https://gitlab.com/cest-group/boss,Bayesian Optimization Structure Search (BOSS).,7,False,['probabilistic'],,,2020-02-12 08:48:33,2020-02-12 08:48:33.000,,,,10.0,,,2.0,24.0,18.0,,,14.0,,aalto-boss,,,,https://pypi.org/project/aalto-boss,233.0,233.0,,,,2.0,,,,,,,,,,,,,cest-group/boss,https://gitlab.com/cest-group/boss,,
-186,CBFV,,rep-eng,,https://github.com/Kaaiian/CBFV,Tool to quickly create a composition-based feature vector.,7,False,,kaaiian/CBFV,https://github.com/Kaaiian/CBFV,2019-09-05 23:07:46,2022-03-30 05:47:53.000,2021-10-24 17:10:17,49.0,,6.0,4.0,7.0,5.0,5.0,15.0,,,,3.0,CBFV,,4.0,4.0,https://pypi.org/project/CBFV,129.0,129.0,,,,3.0,,,,,,,,,,,,,,,,
-187,Libnxc,,ml-dft,MPL-2.0,https://github.com/semodi/libnxc,A library for using machine-learned exchange-correlation functionals for density-functional theory.,7,False,"['lang-cpp', 'lang-fortran']",semodi/libnxc/,https://github.com/semodi/libnxc,2020-07-01 18:21:50,2021-09-18 14:53:52.000,2021-08-14 16:26:32,100.0,,4.0,2.0,3.0,13.0,3.0,15.0,,,2.0,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-188,ACEhamiltonians,,ml-dft,MIT,https://github.com/ACEsuit/ACEhamiltonians.jl,"Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-..",7,True,['lang-julia'],ACEsuit/ACEhamiltonians.jl,https://github.com/ACEsuit/ACEhamiltonians.jl,2022-01-17 20:54:22,2023-04-12 15:11:09.000,2023-04-12 15:04:14,33.0,,3.0,4.0,41.0,1.0,3.0,9.0,2022-05-20 17:07:42,arXiv.2111.13736,1.0,4.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-189,bVAE-IM,,generative,MIT,https://github.com/tsudalab/bVAE-IM,Implementation of Chemical Design with GPU-based Ising Machine.,7,True,"['qml', 'single-paper']",tsudalab/bVAE-IM,https://github.com/tsudalab/bVAE-IM,2023-03-01 08:26:56,2023-07-11 04:39:24.000,2023-07-11 04:39:24,39.0,,2.0,8.0,,,,9.0,2023-03-01 14:26:13,1.0.0,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-190,COSMO Software Cookbook,,educational,BSD-3-Clause,https://github.com/lab-cosmo/software-cookbook,The COSMO cookbook contains recipes for atomic-scale modelling for materials and molecules.,7,False,,lab-cosmo/software-cookbook,https://github.com/lab-cosmo/software-cookbook,2023-05-23 10:33:47,2023-10-27 06:42:00.000,2023-10-27 06:30:34,35.0,12.0,1.0,13.0,27.0,2.0,1.0,3.0,,,,5.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-191,MEGAN: Multi Explanation Graph Attention Student,,xai,MIT,https://github.com/aimat-lab/graph_attention_student,Minimal implementation of graph attention student model architecture.,7,False,,aimat-lab/graph_attention_student,https://github.com/aimat-lab/graph_attention_student,2022-07-28 06:22:50,2023-11-20 09:38:47.000,2023-11-20 09:38:42,34.0,11.0,1.0,3.0,1.0,,,3.0,,,,2.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-192,MEGAN,,xai,MIT,https://github.com/aimat-lab/graph_attention_student,Minimal implementation of graph attention student model architecture.,7,False,"['xai', 'rep-learn']",aimat-lab/graph_attention_student,https://github.com/aimat-lab/graph_attention_student,2022-07-28 06:22:50,2023-11-20 09:38:47.000,2023-11-20 09:38:42,34.0,11.0,1.0,3.0,1.0,,,3.0,,,,2.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-193,2DMD dataset,,datasets,Apache-2.0,https://github.com/HSE-LAMBDA/ai4material_design/blob/main/docs/DATA.md,"Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of..",7,False,['material-defect'],HSE-LAMBDA/ai4material_design,https://github.com/HSE-LAMBDA/ai4material_design,2021-03-25 10:06:20,2023-11-21 11:30:42.000,2023-11-21 11:30:33,1118.0,2.0,1.0,7.0,28.0,,12.0,2.0,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-194,ai4material_design,,rep-learn,Apache-2.0,https://github.com/HSE-LAMBDA/ai4material_design,"Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of..",7,False,"['pre-trained', 'material-defect']",HSE-LAMBDA/ai4material_design,https://github.com/HSE-LAMBDA/ai4material_design,2021-03-25 10:06:20,2023-11-21 11:30:42.000,2023-11-21 11:30:33,1118.0,2.0,1.0,7.0,28.0,,12.0,2.0,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-195,ML4pXRDs,,rep-learn,MIT,https://github.com/aimat-lab/ML4pXRDs,Contains code to train neural networks based on simulated powder XRDs from synthetic crystals.,7,True,"['xrd', 'single-paper']",aimat-lab/ML4pXRDs,https://github.com/aimat-lab/ML4pXRDs,2022-12-01 16:24:29,2023-07-14 08:17:06.000,2023-07-14 08:17:04,1320.0,,,3.0,,,,,2023-03-22 11:04:31,1.0,1.0,,,,,,,,0.0,,,,3.0,,,,,,2.0,,,,,,,,,,
-196,The Collection of Database and Dataset Resources in Materials Science,,community,,https://github.com/sedaoturak/data-resources-for-materials-science,"A list of databases, datasets and books/handbooks where you can find materials properties for machine learning..",6,True,['datasets'],sedaoturak/data-resources-for-materials-science,https://github.com/sedaoturak/data-resources-for-materials-science,2021-02-20 06:38:45,2023-11-03 12:16:07.000,2023-11-03 12:16:07,28.0,2.0,28.0,10.0,1.0,,1.0,187.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-197,GEOM,,datasets,,https://github.com/learningmatter-mit/geom,GEOM: Energy-annotated molecular conformations.,6,False,['drug-discovery'],learningmatter-mit/geom,https://github.com/learningmatter-mit/geom,2020-06-03 17:58:37,2022-04-24 18:57:39.000,2022-04-24 18:57:39,95.0,,18.0,9.0,,1.0,10.0,154.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-198,EquiformerV2,,rep-learn,MIT,https://github.com/atomicarchitects/equiformer_v2,[arXiv23] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations.,6,True,,atomicarchitects/equiformer_v2,https://github.com/atomicarchitects/equiformer_v2,2023-06-21 07:09:58,2023-12-02 16:53:28.000,2023-12-02 16:53:11,9.0,1.0,14.0,4.0,,7.0,,90.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-199,Applied AI for Materials,,educational,,https://github.com/WardLT/applied-ai-for-materials,Course materials for Applied AI for Materials Science and Engineering.,6,False,,WardLT/applied-ai-for-materials,https://github.com/WardLT/applied-ai-for-materials,2020-10-12 19:39:06,2022-03-12 02:26:58.000,2022-03-12 02:26:41,107.0,,29.0,4.0,13.0,5.0,,50.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-200,ANI-1x Datasets,,datasets,MIT,https://github.com/aiqm/ANI1x_datasets,"The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules.",6,False,,aiqm/ANI1x_datasets,https://github.com/aiqm/ANI1x_datasets,2019-09-17 18:19:28,2022-04-11 17:25:55.000,2022-04-11 17:25:55,12.0,,5.0,6.0,,2.0,3.0,47.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-201,DeepDFT,,ml-dft,MIT,https://github.com/peterbjorgensen/DeepDFT,Official implementation of DeepDFT model.,6,True,,peterbjorgensen/DeepDFT,https://github.com/peterbjorgensen/DeepDFT,2020-11-03 11:51:15,2023-02-28 15:37:49.000,2023-02-28 15:37:37,128.0,,7.0,1.0,,,3.0,42.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-202,MACE-Jax,,ml-iap,MIT,https://github.com/ACEsuit/mace-jax,Equivariant machine learning interatomic potentials in JAX.,6,True,,ACEsuit/mace-jax,https://github.com/ACEsuit/mace-jax,2023-02-06 12:10:16,2023-10-04 08:07:35.000,2023-10-04 08:07:35,207.0,1.0,1.0,10.0,1.0,2.0,1.0,37.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-203,COMP6 Benchmark dataset,,datasets,MIT,https://github.com/isayev/COMP6,COMP6 Benchmark dataset for ML potentials.,6,False,,isayev/COMP6,https://github.com/isayev/COMP6,2017-12-29 16:58:35,2018-07-09 23:56:35.000,2018-07-09 23:56:34,27.0,,4.0,5.0,,2.0,1.0,36.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-204,DeepH-E3,,ml-dft,MIT,https://github.com/Xiaoxun-Gong/DeepH-E3,General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian.,6,True,['magnetism'],Xiaoxun-Gong/DeepH-E3,https://github.com/Xiaoxun-Gong/DeepH-E3,2023-03-16 11:25:58,2023-04-04 13:27:01.000,2023-04-04 13:26:27,16.0,,9.0,5.0,,3.0,6.0,36.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-205,milad,,rep-eng,GPL-3.0,https://github.com/muhrin/milad,Moment Invariants Local Atomic Descriptor.,6,True,['generative'],muhrin/milad,https://github.com/muhrin/milad,2020-04-23 09:14:24,2022-12-03 10:40:05.000,2022-12-03 10:39:59,110.0,,1.0,4.0,,,,27.0,,,,,,,1.0,1.0,,,,,,,3.0,,,,,,,,,,,,,,,,
-206,MACE-Layer,,rep-learn,MIT,https://github.com/ACEsuit/mace-layer,Higher order equivariant graph neural networks for 3D point clouds.,6,True,,ACEsuit/mace-layer,https://github.com/ACEsuit/mace-layer,2022-11-09 17:03:41,2023-06-27 15:32:49.000,2023-06-06 10:09:58,19.0,,4.0,5.0,2.0,1.0,,26.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-207,charge_transfer_nnp,,rep-learn,MIT,https://github.com/pfnet-research/charge_transfer_nnp,Graph neural network potential with charge transfer.,6,False,['electrostatics'],pfnet-research/charge_transfer_nnp,https://github.com/pfnet-research/charge_transfer_nnp,2022-04-06 01:48:18,2022-04-06 01:53:35.000,2022-04-06 01:53:22,1.0,,6.0,13.0,,,,24.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-208,ALF,,ml-iap,https://github.com/lanl/ALF/blob/main/LICENSE,https://github.com/lanl/ALF,A framework for performing active learning for training machine-learned interatomic potentials.,6,True,['active-learning'],lanl/alf,https://github.com/lanl/ALF,2023-01-04 23:13:24,2023-09-26 19:47:46.000,2023-08-04 15:53:59,139.0,,9.0,7.0,25.0,,,20.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-209,GLAMOUR,,rep-learn,MIT,https://github.com/learningmatter-mit/GLAMOUR,Graph Learning over Macromolecule Representations.,6,True,['single-paper'],learningmatter-mit/GLAMOUR,https://github.com/learningmatter-mit/GLAMOUR,2021-08-20 18:16:40,2022-12-31 17:56:21.000,2022-12-31 17:56:21,14.0,,6.0,3.0,,,8.0,18.0,2021-08-23 18:58:52,0.1,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-210,SA-GPR,,rep-eng,LGPL-3.0,https://github.com/dilkins/TENSOAP,Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR).,6,False,['lang-c'],dilkins/TENSOAP,https://github.com/dilkins/TENSOAP,2020-05-04 14:19:01,2023-04-07 09:58:08.000,2022-09-29 09:30:45,25.0,,9.0,3.0,10.0,2.0,5.0,14.0,2020-12-17 16:51:47,2020.0,1.0,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-211,NICE,,rep-eng,MIT,https://github.com/lab-cosmo/nice,NICE (N-body Iteratively Contracted Equivariants) is a set of tools designed for the calculation of invariant and..,6,True,,lab-cosmo/nice,https://github.com/lab-cosmo/nice,2020-07-03 08:47:41,2023-05-01 09:22:21.000,2023-05-01 09:21:56,231.0,,2.0,6.0,7.0,2.0,1.0,12.0,,,1.0,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-212,testing-framework,,ml-iap,,https://github.com/libAtoms/testing-framework,The purpose of this repository is to aid the testing of a large number of interatomic potentials for a variety of..,6,False,['benchmarking'],libAtoms/testing-framework,https://github.com/libAtoms/testing-framework,2020-03-04 11:43:15,2022-02-10 17:23:46.000,2022-02-10 17:23:46,225.0,,6.0,16.0,10.0,5.0,3.0,11.0,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-213,CatGym,,reinforcement-learning,GPL,https://github.com/ulissigroup/catgym,Surface segregation using Deep Reinforcement Learning.,6,False,,ulissigroup/catgym,https://github.com/ulissigroup/catgym,2019-08-06 19:25:27,2021-08-30 17:05:36.000,2021-08-30 17:05:32,162.0,,2.0,4.0,,1.0,,10.0,,,,7.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-214,AGOX,,materials-discovery,GPL-3.0,https://gitlab.com/agox/agox,AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional..,6,False,['structure-optimization'],,,2022-03-08 09:08:13,2022-03-08 09:08:13.000,,,,4.0,,,10.0,7.0,10.0,,,2.0,,agox,,,,https://pypi.org/project/agox,49.0,49.0,,,,2.0,,,,,,,,,,,,,agox/agox,https://gitlab.com/agox/agox,,
-215,ACEHAL,,active-learning,,https://github.com/ACEsuit/ACEHAL,Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials.,6,True,['lang-julia'],ACEsuit/ACEHAL,https://github.com/ACEsuit/ACEHAL,2023-02-24 17:33:47,2023-10-01 12:19:41.000,2023-09-21 21:50:43,121.0,14.0,2.0,5.0,15.0,4.0,6.0,9.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-216,PANNA,,ml-iap,MIT,https://gitlab.com/PANNAdevs/panna,A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic..,6,False,['benchmarking'],,,2018-11-09 10:47:48,2018-11-09 10:47:48.000,,,,10.0,,,,,7.0,,,2.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,PANNAdevs/panna,https://gitlab.com/PANNAdevs/panna,,
-217,fplib,,rep-eng,MIT,https://github.com/zhuligs/fplib,a fingerprint library.,6,False,"['lang-c', 'single-paper']",zhuligs/fplib,https://github.com/zhuligs/fplib,2015-09-07 08:18:27,2022-02-09 05:31:21.000,2022-02-09 05:31:12,37.0,,2.0,3.0,,,3.0,7.0,2021-02-03 21:40:23,pub,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-218,SOAPxx,,rep-eng,GPL-2.0,https://github.com/capoe/soapxx,A SOAP implementation.,6,False,['lang-cpp'],capoe/soapxx,https://github.com/capoe/soapxx,2016-03-29 10:00:00,2020-03-27 13:47:44.000,2020-03-27 13:47:36,289.0,,3.0,3.0,1.0,,2.0,7.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-219,SciGlass,,datasets,MIT,https://github.com/drcassar/SciGlass,The database contains a vast set of data on the properties of glass materials.,6,True,,drcassar/SciGlass,https://github.com/drcassar/SciGlass,2019-06-19 19:36:32,2023-08-27 13:46:44.000,2023-08-27 13:46:44,28.0,,3.0,1.0,,,,6.0,2023-08-27 13:48:09,2.0.1,1.0,2.0,,,,,,,0.0,,,,3.0,,,,,,3.0,,,,,,,,,,
-220,COSMO Toolbox,,math,,https://github.com/lab-cosmo/toolbox,Assorted libraries and utilities for atomistic simulation analysis.,6,True,['lang-cpp'],lab-cosmo/toolbox,https://github.com/lab-cosmo/toolbox,2014-05-20 11:23:13,2023-06-23 15:07:59.000,2023-06-23 15:07:29,106.0,,5.0,26.0,1.0,,,6.0,,,,9.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-221,pyLODE,,rep-eng,Apache-2.0,https://github.com/ceriottm/lode,Pythonic implementation of LOng Distance Equivariants.,6,False,['electrostatics'],ceriottm/lode,https://github.com/ceriottm/lode,2022-01-19 17:01:38,2023-07-05 09:57:29.000,2023-07-05 09:57:14,241.0,,1.0,3.0,,1.0,,2.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-222,Visual Graph Datasets,,datasets,MIT,https://github.com/aimat-lab/visual_graph_datasets,Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations..,6,False,,aimat-lab/visual_graph_datasets,https://github.com/aimat-lab/visual_graph_datasets,2023-06-01 11:33:18,2023-11-15 13:38:38.000,2023-11-15 13:38:25,32.0,28.0,1.0,3.0,,,,1.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-223,Computational Autonomy for Materials Discovery (CAMD),,materials-discovery,Apache-2.0,https://github.com/ulissigroup/CAMD,Agent-based sequential learning software for materials discovery.,6,False,,ulissigroup/CAMD,https://github.com/ulissigroup/CAMD,2023-01-10 19:42:57,2023-01-10 19:49:35.000,2023-01-10 19:49:13,1336.0,,,1.0,,,,1.0,,,,17.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,
-224,"Data Handling, DoE and Statistical Analysis for Material Chemists",,educational,GPL-3.0,https://github.com/Teoroo-CMC/DoE_Course_Material,"Notebooks for workshops of DoE course, hosted by the Computational Materials Chemistry group at Uppsala University.",6,True,,Teoroo-CMC/DoE_Course_Material,https://github.com/Teoroo-CMC/DoE_Course_Material,2023-05-22 08:11:41,2023-06-26 12:48:17.000,2023-06-26 12:48:15,157.0,,13.0,2.0,1.0,,,,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-225,Per-site PAiNN,,rep-learn,MIT,https://github.com/learningmatter-mit/per-site_painn,Fork of PaiNN for PerovskiteOrderingGCNNs.,6,True,"['probabilistic', 'pre-trained', 'single-paper']",learningmatter-mit/per-site_painn,https://github.com/learningmatter-mit/per-site_painn,2023-06-04 14:23:49,2023-06-05 17:35:19.000,2023-06-05 17:30:34,123.0,,,,,,,,,,,10.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-226,tensorfieldnetworks,,rep-learn,MIT,https://github.com/tensorfieldnetworks/tensorfieldnetworks,,5,False,,tensorfieldnetworks/tensorfieldnetworks,https://github.com/tensorfieldnetworks/tensorfieldnetworks,2018-02-09 23:18:13,2020-01-07 17:22:16.000,2020-01-07 17:22:15,10.0,,28.0,9.0,2.0,,2.0,144.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-227,AI4Science101,,educational,,https://github.com/deepmodeling/AI4Science101,AI for Science.,5,False,,deepmodeling/AI4Science101,https://github.com/deepmodeling/AI4Science101,2022-06-19 02:26:48,2022-09-04 02:06:18.000,2022-09-04 02:06:18,139.0,,12.0,10.0,28.0,,1.0,72.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-228,SchNOrb,,ml-wft,MIT,https://github.com/atomistic-machine-learning/SchNOrb,Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions.,5,False,,atomistic-machine-learning/SchNOrb,https://github.com/atomistic-machine-learning/SchNOrb,2019-09-17 12:41:48,2019-09-17 14:31:47.000,2019-09-17 14:31:19,2.0,,17.0,5.0,,1.0,,55.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-229,Machine Learning for Materials Hard and Soft,,educational,,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,ESI-DCAFM-TACO-VDSP Summer School on Machine Learning for Materials Hard and Soft.,5,False,,CompPhysVienna/MLSummerSchoolVienna2022,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,2022-07-01 08:42:41,2022-07-22 08:10:24.000,2022-07-22 08:10:24,49.0,,17.0,1.0,14.0,,,33.0,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-230,Autobahn,,rep-learn,MIT,https://github.com/risilab/Autobahn,Repository for Autobahn: Automorphism Based Graph Neural Networks.,5,False,,risilab/Autobahn,https://github.com/risilab/Autobahn,2021-03-02 01:14:40,2022-03-01 21:04:09.000,2022-03-01 21:04:04,11.0,,2.0,5.0,,,,28.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-231,MLIP-3,,ml-iap,BSD-2-Clause,https://gitlab.com/ashapeev/mlip-3,MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP).,5,True,['lang-cpp'],,,2023-04-24 14:05:53,2023-04-24 14:05:53.000,,,,3.0,,,15.0,5.0,18.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,ashapeev/mlip-3,https://gitlab.com/ashapeev/mlip-3,,
-232,EquivariantOperators.jl,,math,MIT,https://github.com/aced-differentiate/EquivariantOperators.jl,,5,False,['lang-julia'],aced-differentiate/EquivariantOperators.jl,https://github.com/aced-differentiate/EquivariantOperators.jl,2021-11-29 03:36:21,2023-09-27 18:34:44.000,2023-09-27 18:34:44,62.0,4.0,,4.0,,,,17.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-233,SCFNN,,rep-learn,MIT,https://github.com/andy90/SCFNN,Self-consistent determination of long-range electrostatics in neural network potentials.,5,False,"['lang-cpp', 'electrostatics', 'single-paper']",andy90/SCFNN,https://github.com/andy90/SCFNN,2021-09-22 12:02:00,2022-01-30 02:29:03.000,2022-01-24 09:40:40,10.0,,8.0,2.0,,,,15.0,2022-01-30 02:29:04,1.0.0,1.0,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-234,rxngenerator,,generative,MIT,https://github.com/tsudalab/rxngenerator,A generative model for molecular generation via multi-step chemical reactions.,5,False,,tsudalab/rxngenerator,https://github.com/tsudalab/rxngenerator,2021-06-18 07:44:53,2022-08-09 07:21:44.000,2022-08-09 07:21:05,16.0,,2.0,9.0,2.0,1.0,,12.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-235,FieldSchNet,,rep-learn,MIT,https://github.com/atomistic-machine-learning/field_schnet,,5,False,,atomistic-machine-learning/field_schnet,https://github.com/atomistic-machine-learning/field_schnet,2020-11-18 10:26:59,2022-05-19 09:28:38.000,2022-05-19 09:28:38,26.0,,4.0,3.0,1.0,1.0,,11.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-236,charge-density-models,,ml-dft,MIT,https://github.com/ulissigroup/charge-density-models,Tools to build charge density models using ocpmodels.,5,True,,ulissigroup/charge-density-models,https://github.com/ulissigroup/charge-density-models,2022-06-22 13:47:53,2023-11-29 15:07:42.000,2023-11-29 15:07:42,96.0,1.0,3.0,2.0,16.0,,2.0,8.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-237,CraTENet,,rep-learn,MIT,https://github.com/lantunes/CraTENet,An attention-based deep neural network for thermoelectric transport properties.,5,True,['transport-phenomena'],lantunes/CraTENet,https://github.com/lantunes/CraTENet,2022-06-30 10:40:06,2023-04-05 01:13:22.000,2023-04-05 01:13:11,24.0,,1.0,1.0,,,,8.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-238,MolSLEPA,,generative,MIT,https://github.com/tsudalab/MolSLEPA,Interpretable Fragment-based Molecule Design with Self-learning Entropic Population Annealing.,5,True,['xai'],tsudalab/MolSLEPA,https://github.com/tsudalab/MolSLEPA,2023-04-10 15:04:55,2023-04-13 12:48:49.000,2023-04-13 12:48:49,11.0,,1.0,8.0,2.0,,,5.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-239,COSMO tools,,others,,https://github.com/lab-cosmo/cosmo-tools,"Scripts, jupyter nbs, and general helpful stuff from COSMO by COSMO.",5,False,,lab-cosmo/cosmo-tools,https://github.com/lab-cosmo/cosmo-tools,2018-11-06 09:40:00,2023-11-13 06:28:17.000,2023-11-13 06:28:17,60.0,1.0,3.0,22.0,,,,2.0,,,,4.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,True,
-240,Alchemical learning,,ml-iap,BSD-3-Clause,https://github.com/Luthaf/alchemical-learning,Code for the Modeling high-entropy transition metal alloys with alchemical compression article.,5,False,,Luthaf/alchemical-learning,https://github.com/Luthaf/alchemical-learning,2021-12-02 17:02:00,2023-04-24 18:35:45.000,2023-04-07 10:19:10,120.0,,1.0,6.0,1.0,,4.0,2.0,,,,10.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-241,linear-regression-benchmarks,,datasets,MIT,https://github.com/BingqingCheng/linear-regression-benchmarks,Data sets used for linear regression benchmarks.,5,False,"['benchmarking', 'single-paper']",BingqingCheng/linear-regression-benchmarks,https://github.com/BingqingCheng/linear-regression-benchmarks,2020-04-16 20:48:28,2022-01-26 08:29:46.000,2022-01-26 08:29:46,24.0,,,3.0,2.0,,,1.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-242,Per-Site CGCNN,,rep-learn,MIT,https://github.com/learningmatter-mit/per-site_cgcnn,Crystal graph convolutional neural networks for predicting material properties.,5,False,"['pre-trained', 'single-paper']",learningmatter-mit/per-site_cgcnn,https://github.com/learningmatter-mit/per-site_cgcnn,2023-05-30 18:59:03,2023-06-05 17:38:46.000,2023-06-05 17:38:41,28.0,,,,,,,1.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-243,ACE1Pack.jl,,ml-iap,MIT,https://github.com/ACEsuit/ACE1pack.jl,"Provides convenience functionality for the usage of ACE1.jl, ACEfit.jl, JuLIP.jl for fitting interatomic potentials..",5,True,['lang-julia'],ACEsuit/ACE1pack.jl,https://github.com/ACEsuit/ACE1pack.jl,2023-08-21 16:25:00,2023-08-21 16:30:19.000,2023-08-21 15:48:54,547.0,,,1.0,,,,,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,https://acesuit.github.io/ACE1pack.jl
-244,ACE Workflows,,ml-iap,,https://github.com/ACEsuit/ACEworkflows,Workflow Examples for ACE Models.,5,True,"['lang-julia', 'workflows']",ACEsuit/ACEworkflows,https://github.com/ACEsuit/ACEworkflows,2023-04-04 16:57:36,2023-10-12 18:01:00.000,2023-10-12 18:00:39,45.0,9.0,1.0,3.0,7.0,1.0,,,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-245,ML-in-chemistry-101,,educational,,https://github.com/BingqingCheng/ML-in-chemistry-101,The course materials for Machine Learning in Chemistry 101.,4,False,,BingqingCheng/ML-in-chemistry-101,https://github.com/BingqingCheng/ML-in-chemistry-101,2020-02-09 17:47:07,2020-10-19 08:10:31.000,2020-10-19 08:10:30,13.0,,15.0,2.0,,,,57.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-246,Atom2Vec,,rep-learn,,https://github.com/idocx/Atom2Vec,Atom2Vec: a simple way to describe atoms for machine learning.,4,False,,idocx/Atom2Vec,https://github.com/idocx/Atom2Vec,2020-01-18 23:31:47,2022-10-16 05:43:31.000,2021-06-21 16:31:09,3.0,,8.0,1.0,1.0,1.0,1.0,26.0,,,,,atom2vec,,1.0,1.0,https://pypi.org/project/atom2vec,33.0,33.0,,,,3.0,,,,,,,,,,,,,,,,
-247,Graph Transport Network,,rep-learn,https://github.com/gasteigerjo/gtn/blob/main/LICENSE.md,https://github.com/gasteigerjo/gtn,"Graph transport network (GTN), as proposed in Scalable Optimal Transport in High Dimensions for Graph Distances,..",4,False,['transport-phenomena'],gasteigerjo/gtn,https://github.com/gasteigerjo/gtn,2021-07-11 23:36:22,2023-04-26 14:22:00.000,2023-04-26 14:22:00,9.0,,3.0,2.0,,,,15.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-248,glp,,ml-iap,MIT,https://github.com/sirmarcel/glp,tools for graph-based machine-learning potentials in jax.,4,False,,sirmarcel/glp,https://github.com/sirmarcel/glp,2023-03-27 15:19:40,2023-08-07 14:41:10.000,2023-08-07 14:41:05,10.0,,,1.0,2.0,,,13.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-249,3DSC Database,,datasets,https://github.com/aimat-lab/3DSC/blob/main/LICENSE.md,https://github.com/aimat-lab/3DSC,Repo for the paper publishing the superconductor database with 3D crystal structures.,4,False,"['superconductors', 'materials-discovery']",aimat-lab/3DSC,https://github.com/aimat-lab/3DSC,2021-11-02 09:07:57,2023-07-21 09:28:43.000,2023-07-21 09:26:12,52.0,,2.0,2.0,,,,9.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-250,chemrev-gpr,,educational,,https://github.com/gabor1/chemrev-gpr,Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020.,4,False,,gabor1/chemrev-gpr,https://github.com/gabor1/chemrev-gpr,2020-12-18 23:48:06,2021-05-04 19:21:34.000,2021-05-04 19:21:30,10.0,,6.0,4.0,,,,6.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-251,TensorPotential,,ml-iap,https://github.com/ICAMS/TensorPotential/blob/main/LICENSE.md,https://cortner.github.io/ACEweb/software/,"Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic..",4,False,,ICAMS/TensorPotential,https://github.com/ICAMS/TensorPotential,2021-12-08 12:10:04,2023-07-10 16:37:18.000,2023-07-10 16:37:18,18.0,,4.0,2.0,2.0,,,5.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-252,interface-lammps-mlip-3,,md,GPL-2.0,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,An interface between LAMMPS and MLIP (version 3).,4,False,,,,2023-04-24 12:48:51,2023-04-24 12:48:51.000,,,,3.0,,,4.0,,5.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,ivannovikov/interface-lammps-mlip-3,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,,
-253,ACEatoms,,general-tool,https://github.com/ACEsuit/ACEatoms.jl/blob/main/ASL.md,https://github.com/ACEsuit/ACEatoms.jl,Generic code for modelling atomic properties using ACE.,4,False,['lang-julia'],ACEsuit/ACEatoms.jl,https://github.com/ACEsuit/ACEatoms.jl,2021-03-23 23:50:03,2023-01-13 21:35:06.000,2023-01-13 21:28:08,134.0,,1.0,3.0,14.0,4.0,3.0,2.0,,,,10.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-254,atom_by_atom,,rep-learn,,https://github.com/learningmatter-mit/atom_by_atom,Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with Machine Learning.,4,False,"['surface-science', 'single-paper']",learningmatter-mit/atom_by_atom,https://github.com/learningmatter-mit/atom_by_atom,2023-05-30 20:18:00,2023-10-19 15:59:08.000,2023-10-19 15:35:49,74.0,10.0,,2.0,,,,2.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-255,SISSO++,,rep-eng,Apache-2.0,https://gitlab.com/sissopp_developers/sissopp,C++ Implementation of SISSO with python bindings.,4,False,['lang-cpp'],,,2021-04-30 14:20:59,2021-04-30 14:20:59.000,,,,3.0,,,,12.0,2.0,,,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,sissopp_developers/sissopp,https://gitlab.com/sissopp_developers/sissopp,,
-256,cnine,,math,,https://github.com/risi-kondor/cnine,Cnine tensor library.,4,False,['lang-cpp'],risi-kondor/cnine,https://github.com/risi-kondor/cnine,2022-10-07 20:54:54,2023-12-01 09:48:24.000,2023-12-01 09:48:18,257.0,67.0,1.0,1.0,1.0,,1.0,2.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,https://risi-kondor.github.io/cnine/
-257,magnetism-prediction,,rep-eng,Apache-2.0,https://github.com/dppant/magnetism-prediction,DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides.,4,False,"['magnetism', 'single-paper']",dppant/magnetism-prediction,https://github.com/dppant/magnetism-prediction,2022-09-13 03:58:10,2023-07-19 13:25:49.000,2023-07-19 13:25:49,46.0,,,3.0,,,,1.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-258,MLatom,,general-tool,https://creativecommons.org/licenses/by-nc-nd/4.0/,http://mlatom.com/,Machine learning for atomistic simulations.,4,False,,,,,,,,,,,,,,,,,,,MLatom,,,,https://pypi.org/project/MLatom,280.0,280.0,,,,3.0,,,,,,,,,,,,,,,,http://mlatom.com/manual/
-259,gprep,,ml-dft,MIT,https://gitlab.com/jmargraf/gprep,Fitting DFTB repulsive potentials with GPR.,4,False,['single-paper'],,,2019-09-30 09:15:04,2019-09-30 09:15:04.000,,,,0.0,,,,,,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,jmargraf/gprep,https://gitlab.com/jmargraf/gprep,,
-260,closed-loop-acceleration-benchmarks,,materials-discovery,MIT,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational..,4,False,"['materials-discovery', 'active-learning', 'single-paper']",aced-differentiate/closed-loop-acceleration-benchmarks,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,2022-11-10 20:22:30,2023-07-25 21:25:42.000,2023-05-02 17:07:48,17.0,,1.0,4.0,3.0,,,,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-261,ML-for-CurieTemp-Predictions,,rep-eng,MIT,https://github.com/msg-byu/ML-for-CurieTemp-Predictions,Machine Learning Predictions of High-Curie-Temperature Materials.,4,False,"['single-paper', 'magnetism']",msg-byu/ML-for-CurieTemp-Predictions,https://github.com/msg-byu/ML-for-CurieTemp-Predictions,2023-06-05 22:46:47,2023-06-14 19:05:50.000,2023-06-14 19:05:47,25.0,,,1.0,,,,,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-262,xDeepH,,ml-dft,LGPL-3.0,https://github.com/mzjb/xDeepH,Extended DeepH (xDeepH) method for magnetic materials.,3,False,"['magnetism', 'lang-julia']",mzjb/xDeepH,https://github.com/mzjb/xDeepH,2023-02-23 12:56:49,2023-06-14 11:44:53.000,2023-06-14 11:44:46,4.0,,1.0,2.0,,,,23.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-263,Coarse-Graining-Auto-encoders,,unsupervised,,https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders,,3,False,['single-paper'],learningmatter-mit/Coarse-Graining-Auto-encoders,https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders,2019-09-16 15:27:57,2019-08-16 21:39:34.000,2019-08-16 21:39:33,14.0,,7.0,6.0,,,,20.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-264,ML-DFT,,ml-dft,MIT,https://github.com/MihailBogojeski/ml-dft,A package for density functional approximation using machine learning.,3,False,,MihailBogojeski/ml-dft,https://github.com/MihailBogojeski/ml-dft,2020-09-14 22:15:56,2020-09-18 16:36:30.000,2020-09-18 16:36:30,9.0,,6.0,2.0,,1.0,1.0,19.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-265,SPINNER,,materials-discovery,GPL-3.0,https://github.com/MDIL-SNU/SPINNER,SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random..,3,False,"['lang-cpp', 'structure-prediction']",MDIL-SNU/SPINNER,https://github.com/MDIL-SNU/SPINNER,2021-07-15 02:10:58,2021-11-25 07:58:15.000,2021-11-25 07:58:15,102.0,,2.0,1.0,,1.0,,9.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-266,sl_discovery,,materials-discovery,Apache-2.0,https://github.com/CitrineInformatics-ERD-public/sl_discovery,Data processing and models related to Quantifying the performance of machine learning models in materials discovery.,3,False,"['materials-discovery', 'single-paper']",CitrineInformatics-ERD-public/sl_discovery,https://github.com/CitrineInformatics-ERD-public/sl_discovery,2022-10-24 18:10:14,2022-12-20 23:46:05.000,2022-12-20 23:45:57,5.0,,2.0,2.0,,,,5.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-267,Element encoder,,rep-learn,GPL-3.0,https://github.com/jeherr/element-encoder,Autoencoder neural network to compress properties of atomic species into a vector representation.,3,False,['single-paper'],jeherr/element-encoder,https://github.com/jeherr/element-encoder,2019-03-27 17:11:30,2020-01-09 15:54:27.000,2020-01-09 15:54:26,8.0,,1.0,4.0,,,1.0,5.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-268,KmdPlus,,unsupervised,,https://github.com/Minoru938/KmdPlus,"This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with..",3,False,,Minoru938/KmdPlus,https://github.com/Minoru938/KmdPlus,2023-03-26 10:06:34,2023-10-17 08:28:01.000,2023-10-17 08:28:01,7.0,3.0,,1.0,,,,3.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-269,DeepCDP,,ml-dft,,https://github.com/siddarthachar/deepcdp,DeepCDP: Deep learning Charge Density Prediction.,3,False,,siddarthachar/deepcdp,https://github.com/siddarthachar/deepcdp,2021-12-18 14:26:56,2023-06-16 20:38:23.000,2023-06-16 20:38:23,96.0,,,2.0,27.0,,,3.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-270,BERT-PSIE-TC,,lm,MIT,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,A dataset of Curie temperatures automatically extracted from scientific literature with the use of the BERT-PSIE..,3,False,['magnetism'],StefanoSanvitoGroup/BERT-PSIE-TC,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,2023-01-25 10:27:26,2023-08-18 11:47:45.000,2023-08-18 12:48:31,36.0,,2.0,1.0,,,,2.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-271,Linear vs blackbox,,xai,MIT,https://github.com/CitrineInformatics-ERD-public/linear-vs-blackbox,Code and data related to the publication: Interpretable models for extrapolation in scientific machine learning.,3,False,"['xai', 'single-paper', 'rep-eng']",CitrineInformatics-ERD-public/linear-vs-blackbox,https://github.com/CitrineInformatics-ERD-public/linear-vs-blackbox,2022-12-02 20:32:53,2022-12-16 18:48:12.000,2022-12-16 18:48:12,4.0,,,1.0,,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-272,CSNN,,ml-dft,BSD-3-Clause,https://github.com/foxjas/CSNN,Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning.,3,False,,foxjas/CSNN,https://github.com/foxjas/CSNN,2022-05-19 15:40:49,2022-10-11 04:27:40.000,2022-10-11 04:27:40,6.0,,,1.0,,,,1.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-273,Magpie,,general-tool,MIT,https://bitbucket.org/wolverton/magpie/,Materials Agnostic Platform for Informatics and Exploration (Magpie).,3,False,['lang-java'],,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-274,MALADA,,ml-dft,BSD-3-Clause,https://github.com/mala-project/malada,MALA Data Acquisition: Helpful tools to build data for MALA.,3,False,,mala-project/malada,https://github.com/mala-project/malada,2021-07-26 05:46:08,2023-05-24 09:18:24.000,2023-05-24 09:18:24,111.0,,1.0,2.0,4.0,17.0,2.0,,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-275,CSPML (crystal structure prediction with machine learning-based element substitution),,materials-discovery,,https://github.com/Minoru938/CSPML,Original implementation of CSPML.,2,False,['structure-prediction'],minoru938/cspml,https://github.com/Minoru938/CSPML,2022-01-15 10:59:27,2022-06-02 23:26:26.000,2022-06-02 23:26:26,7.0,,8.0,2.0,,2.0,1.0,14.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-276,SingleNN,,ml-iap,,https://github.com/lmj1029123/SingleNN,An efficient package for training and executing neural-network interatomic potentials.,2,False,['lang-cpp'],lmj1029123/SingleNN,https://github.com/lmj1029123/SingleNN,2020-03-11 18:36:16,2021-11-09 00:40:18.000,2021-11-09 00:40:10,17.0,,1.0,1.0,,1.0,,7.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-277,MLDensity_tutorial,,educational,,https://github.com/bfocassio/MLDensity_tutorial,Tutorial files to work with ML for the charge density in molecules and solids.,2,False,,bfocassio/MLDensity_tutorial,https://github.com/bfocassio/MLDensity_tutorial,2023-01-31 10:33:23,2023-02-22 19:20:32.000,2023-02-22 19:20:32,8.0,,1.0,1.0,,,,6.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-278,MALA Tutorial,,educational,,https://github.com/mala-project/mala_tutorial,A full MALA hands-on tutorial.,2,False,,mala-project/mala_tutorial,https://github.com/mala-project/mala_tutorial,2023-03-09 14:01:54,2023-11-28 11:20:39.000,2023-11-28 11:17:01,24.0,1.0,,2.0,,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-279,PiNN Lab,,educational,GPL-3.0,https://github.com/Teoroo-CMC/PiNN_lab,,2,False,,Teoroo-CMC/PiNN_lab,https://github.com/Teoroo-CMC/PiNN_lab,2019-03-17 22:09:30,2023-05-01 15:59:56.000,2023-05-01 15:59:22,9.0,,1.0,3.0,1.0,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-280,gkx: Green-Kubo Method in JAX,,rep-learn,MIT,https://github.com/sirmarcel/gkx,Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast.,2,False,['transport-phenomena'],sirmarcel/gkx,https://github.com/sirmarcel/gkx,2023-04-30 12:25:16,2023-04-30 14:14:57.000,2023-04-30 14:14:46,2.0,,,1.0,,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-281,e3psi,,ml-esm,LGPL-3.0,https://github.com/muhrin/e3psi,Equivariant machine learning library for learning from electronic structures.,2,False,,muhrin/e3psi,https://github.com/muhrin/e3psi,2022-08-08 10:48:30,2023-08-09 17:04:49.000,2023-04-10 17:04:33,14.0,,,2.0,,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-282,Wigner Kernels,,math,,https://github.com/lab-cosmo/wigner_kernels,Collection of programs to benchmark Wigner kernels.,2,False,['benchmarking'],lab-cosmo/wigner_kernels,https://github.com/lab-cosmo/wigner_kernels,2022-12-08 12:28:26,2023-07-08 15:48:41.000,2023-07-08 15:48:37,109.0,,,1.0,,,,1.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-283,quantum-structure-ml,,general-tool,,https://github.com/hgheiberger/quantum-structure-ml,Multi-class classification model for predicting the magnetic order of magnetic structures and a binary classification..,2,False,"['magnetism', 'benchmarking']",hgheiberger/quantum-structure-ml,https://github.com/hgheiberger/quantum-structure-ml,2020-10-05 01:11:01,2022-12-22 21:45:40.000,2022-12-22 21:45:40,19.0,,,2.0,,,,1.0,2022-08-18 05:25:24,1.0.0,1.0,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-284,AMP,,rep-eng,,https://bitbucket.org/andrewpeterson/amp/,Amp is an open-source package designed to easily bring machine-learning to atomistic calculations.,2,False,,,,,,,,,,,,,,,,,,,amp-atomistics,,,,https://pypi.org/project/amp-atomistics,87.0,87.0,,,,3.0,,,,,,,,,,,,,,,,https://amp.readthedocs.io/
-285,q-pac,,ml-esm,MIT,,,2,False,['electrostatics'],,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,jmargraf/kqeq,,,
-286,RuNNer,,ml-iap,GPL-3.0,https://www.uni-goettingen.de/de/560580.html,The RuNNer Neural Network Energy Representation is a Fortran-based framework for the construction of Behler-..,2,False,['lang-fortran'],,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,https://theochemgoettingen.gitlab.io/RuNNer/
-287,Point Edge Transformer,,rep-learn,CC-BY-4.0,https://zenodo.org/record/7967079,"Smooth, exact rotational symmetrization for deep learning on point clouds.",2,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-288,SphericalNet,,rep-learn,,https://github.com/risilab/SphericalNet,Implementation of Clebsch-Gordan Networks (CGnet: https://arxiv.org/pdf/1806.09231.pdf) by GElib & cnine libraries in..,1,False,,risilab/SphericalNet,https://github.com/risilab/SphericalNet,2022-05-31 14:39:05,2022-06-07 03:57:10.000,2022-06-07 03:53:49,1.0,,,2.0,,,,3.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,
-289,kdft,,ml-dft,,https://gitlab.com/jmargraf/kdf,The Kernel Density Functional (KDF) code allows generating ML based DFT functionals.,1,False,,,,2020-11-07 21:50:22,2020-11-07 21:50:22.000,,,,0.0,,,,,2.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,jmargraf/kdf,https://gitlab.com/jmargraf/kdf,,
-290,Descriptor Embedding and Clustering for Atomisitic-environment Framework (DECAF),,unsupervised,,https://gitlab.mpcdf.mpg.de/klai/decaf,Provides a workflow to obtain clustering of local environments in dataset of structures.,0,False,,,,,,,41.0,,,,,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
-291,GitHub topic materials-informatics,,community,,https://github.com/topics/materials-informatics,,0,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-292,MateriApps,,community,,https://ma.issp.u-tokyo.ac.jp/en/,,0,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-293,MLDensity,,ml-dft,,{},Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure..,0,False,,StefanoSanvitoGroup/MLdensity,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,name,resource,category,license,homepage,description,projectrank,show,labels,github_id,github_url,created_at,updated_at,last_commit_pushed_at,commit_count,recent_commit_count,fork_count,watchers_count,pr_count,open_issue_count,closed_issue_count,star_count,latest_stable_release_published_at,latest_stable_release_number,release_count,contributor_count,pypi_id,conda_id,dependent_project_count,github_dependent_project_count,pypi_url,pypi_monthly_downloads,monthly_downloads,conda_url,conda_latest_release_published_at,conda_total_downloads,projectrank_placing,dockerhub_id,dockerhub_url,dockerhub_latest_release_published_at,dockerhub_stars,dockerhub_pulls,github_release_downloads,new_addition,maven_id,maven_url,updated_github_id,npm_id,npm_url,npm_monthly_downloads,gitlab_id,gitlab_url,ignore,docs_url
+0,AI for Science Map,True,community,GPL-3.0 license,https://www.air4.science/map,"Interactive mindmap of the AI4Science research field, including atomistic machine learning, including papers,..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+1,Atomic Cluster Expansion,True,community,,https://cortner.github.io/ACEweb/,Atomic Cluster Expansion (ACE) community homepage.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+2,CrystaLLM,True,community,https://materialis.ai/terms.html,https://crystallm.com,Generate a crystal structure from a composition.,0,True,"['language-models', 'generative', 'pre-trained', 'transformer']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,matsci.org,True,community,,https://matsci.org/,"A community forum for the discussion of anything materials science, with a focus on computational materials science..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,Matter Modeling Stack Exchange - Machine Learning,True,community,,https://mattermodeling.stackexchange.com/questions/tagged/machine-learning,"Forum StackExchange, site Matter Modeling, ML-tagged questions.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+5,Catalysis Hub,True,datasets,,https://www.catalysis-hub.org/,A web-platform for sharing data and software for computational catalysis research!.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,Citrination Datasets,True,datasets,MIT,https://citrination.com/,AI-Powered Materials Data Platform. Open Citrination has been decommissioned.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+7,crystals.ai,True,datasets,,https://crystals.ai/,Curated datasets for reproducible AI in materials science.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,DeepChem Models,True,datasets,,https://huggingface.co/DeepChem,DeepChem models on HuggingFace.,0,True,"['pre-trained', 'language-models']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,JARVIS-Leaderboard,True,datasets,https://github.com/usnistgov/jarvis_leaderboard/blob/main/LICENSE.rst,https://pages.nist.gov/jarvis_leaderboard/,This project provides benchmark-performances for materials science applications including Artificial Intelligence..,0,True,['benchmarking'],usnistgov/jarvis_leaderboard,https://github.com/usnistgov/jarvis_leaderboard,2022-07-15 16:48:33,2023-11-27 01:50:42.000,2023-10-13 13:39:27,706.0,30.0,26.0,6.0,275.0,4.0,2.0,41.0,2023-08-04 17:33:22,2023.08.01,21.0,24.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+10,Materials Project - Charge Densities,True,datasets,,https://materialsproject.org/ml/charge_densities,Materials Project has started offering charge density information available for download via their public API.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,matterverse.ai,True,datasets,,https://matterverse.ai/,Database of yet-to-be-sythesized materials predicted using state-of-the-art machine learning algorithms.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+12,Quantum-Machine.org Datasets,True,datasets,,http://quantum-machine.org/datasets/,"Collection of datasets, including QM7, QM9, etc. MD, DFT. Small organic molecules, mostly.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,sGDML Datasets,True,datasets,,http://sgdml.org/#datasets,"MD17, MD22, DFT datasets.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+14,MoleculeNet,True,datasets,TBD,https://moleculenet.org/,,0,True,['benchmarking'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,ZINC15,True,datasets,,https://zinc15.docking.org/,,0,True,"['graph', 'biomolecules']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+16,ZINC,True,datasets,,https://zinc.docking.org/,,0,True,"['graph', 'biomolecules']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+17,NRELMatDB,True,datasets,,https://materials.nrel.gov/,"Computational materials database with the specific focus on materials for renewable energy applications including, but..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+18,Quantum Chemistry in the Age of Machine Learning,True,educational,,https://www.elsevier.com/books-and-journals/book-companion/9780323900492,"Book, 2022.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+19,AL4MS 2023 workshop tutorials,True,educational,,https://sites.utu.fi/al4ms2023/media-and-tutorials/,,0,True,['active-learning'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+20,Deep Graph Library (DGL),,rep-learn,Apache-2.0,https://github.com/dmlc/dgl,"Python package built to ease deep learning on graph, on top of existing DL frameworks.",38,True,,dmlc/dgl,https://github.com/dmlc/dgl,2018-04-20 14:49:09,2023-12-02 12:26:24.000,2023-12-01 08:30:42,3656.0,277.0,2828.0,170.0,4213.0,324.0,2119.0,12505.0,2023-08-15 07:31:40,1.1.2,76.0,281.0,dgl,dglteam/dgl,166.0,166.0,https://pypi.org/project/dgl,69432.0,74469.0,https://anaconda.org/dglteam/dgl,2023-09-14 15:04:21.699,302272.0,1.0,,,,,,,,,,,,,,,,,
+21,DeepChem,,general-tool,MIT,https://github.com/deepchem/deepchem,"Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology.",36,True,,deepchem/deepchem,https://github.com/deepchem/deepchem,2015-09-24 23:20:28,2023-12-02 20:50:00.000,2023-12-01 17:26:24,9737.0,447.0,1477.0,146.0,2054.0,429.0,1211.0,4750.0,2022-12-01 13:22:37,2.7.1,18.0,230.0,deepchem,conda-forge/deepchem,286.0,286.0,https://pypi.org/project/deepchem,16179.0,18515.0,https://anaconda.org/conda-forge/deepchem,2023-06-16 19:18:02.015,104276.0,1.0,deepchemio/deepchem,https://hub.docker.com/r/deepchemio/deepchem,2022-03-11 05:24:00.723691,4.0,7027.0,,,,,,,,,,,,
+22,PyG Models,,rep-learn,MIT,https://github.com/pyg-team/pytorch_geometric/tree/master/torch_geometric/nn/models,Representation learning models implemented in PyTorch Geometric.,30,True,['general-ml'],pyg-team/pytorch_geometric,https://github.com/pyg-team/pytorch_geometric,2017-10-06 16:03:03,2023-12-03 19:07:33.000,2023-12-03 19:07:32,7176.0,248.0,3333.0,254.0,2600.0,757.0,2504.0,19060.0,2023-10-12 08:28:59,2.4.0,36.0,468.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,
+23,RDKit,,general-tool,BSD-3-Clause,https://github.com/rdkit/rdkit,,30,True,['lang-cpp'],rdkit/rdkit,https://github.com/rdkit/rdkit,2013-05-12 06:19:15,2023-12-03 18:49:22.000,2023-12-02 06:31:04,7551.0,82.0,775.0,85.0,2895.0,874.0,2083.0,2258.0,2023-11-10 06:45:02,Release_2023_09_2,98.0,207.0,rdkit,rdkit/rdkit,2.0,2.0,https://pypi.org/project/rdkit,324378.0,346727.0,https://anaconda.org/rdkit/rdkit,2023-06-16 12:54:07.547,2546600.0,1.0,,,,,,1407.0,,,,,,,,,,,
+24,DeePMD-kit,,ml-iap,LGPL-3.0,https://github.com/deepmodeling/deepmd-kit,A deep learning package for many-body potential energy representation and molecular dynamics.,28,True,['lang-cpp'],deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2023-12-02 23:23:49.000,2023-10-27 13:10:24,2353.0,106.0,438.0,46.0,1373.0,26.0,451.0,1267.0,2023-10-27 19:33:04,2.2.7,41.0,64.0,deepmd-kit,deepmodeling/deepmd-kit,12.0,12.0,https://pypi.org/project/deepmd-kit,893.0,1521.0,https://anaconda.org/deepmodeling/deepmd-kit,2023-10-27 21:14:49.194,353.0,1.0,deepmodeling/deepmd-kit,https://hub.docker.com/r/deepmodeling/deepmd-kit,2023-10-29 23:31:03.651515,1.0,2033.0,29095.0,,,,,,,,,,,
+25,SchNetPack,,rep-learn,MIT,https://github.com/atomistic-machine-learning/schnetpack,SchNetPack - Deep Neural Networks for Atomistic Systems.,28,True,,atomistic-machine-learning/schnetpack,https://github.com/atomistic-machine-learning/schnetpack,2018-09-03 15:44:35,2023-12-01 09:07:49.000,2023-12-01 08:57:55,1598.0,43.0,188.0,31.0,370.0,2.0,217.0,670.0,2023-09-29 14:31:19,2.0.4,8.0,32.0,schnetpack,,64.0,64.0,https://pypi.org/project/schnetpack,602.0,602.0,,,,1.0,,,,,,,,,,,,,,,,,
+26,Matminer,,general-tool,https://github.com/hackingmaterials/matminer/blob/main/LICENSE,https://github.com/hackingmaterials/matminer,Data mining for materials science.,27,True,,hackingmaterials/matminer,https://github.com/hackingmaterials/matminer,2015-09-24 20:37:00,2023-11-27 08:05:39.000,2023-11-10 10:02:34,4112.0,4.0,176.0,28.0,701.0,27.0,187.0,419.0,2023-06-27 15:36:52,0.9.0,66.0,50.0,matminer,conda-forge/matminer,252.0,252.0,https://pypi.org/project/matminer,14008.0,15412.0,https://anaconda.org/conda-forge/matminer,2023-10-24 08:40:46.186,53358.0,1.0,,,,,,,,,,,,,,,,,
+27,JAX-DFT,,ml-dft,Apache-2.0,https://github.com/google-research/google-research/tree/master/jax_dft,Google Research.,25,True,,google-research/google-research,https://github.com/google-research/google-research,2018-10-04 18:42:48,2023-12-02 23:50:17.000,2023-12-02 23:50:14,4340.0,143.0,7410.0,747.0,652.0,814.0,296.0,31538.0,,,,751.0,,,,,,,,,,,1.0,,,,,,,True,,,,,,,,,,
+28,paper-qa,,language-models,Apache-2.0,https://github.com/whitead/paper-qa,LLM Chain for answering questions from documents with citations.,25,True,,whitead/paper-qa,https://github.com/whitead/paper-qa,2023-02-05 01:07:25,2023-12-01 22:47:51.000,2023-12-01 22:47:34,178.0,11.0,297.0,41.0,90.0,46.0,66.0,3268.0,2023-12-01 22:47:51,3.13.3,74.0,12.0,paper-qa,,39.0,39.0,https://pypi.org/project/paper-qa,2756.0,2756.0,,,,1.0,,,,,,,,,,,,,,,,,
+29,e3nn,,rep-learn,MIT,https://github.com/e3nn/e3nn,A modular framework for neural networks with Euclidean symmetry.,25,True,,e3nn/e3nn,https://github.com/e3nn/e3nn,2020-01-31 13:06:42,2023-11-03 06:14:55.000,2023-09-02 22:23:35,2157.0,,111.0,18.0,204.0,16.0,131.0,797.0,2022-12-12 21:42:03,0.5.1,28.0,29.0,e3nn,conda-forge/e3nn,133.0,133.0,https://pypi.org/project/e3nn,97925.0,98498.0,https://anaconda.org/conda-forge/e3nn,2023-06-18 08:41:30.723,10900.0,1.0,,,,,,,,,,,,,,,,,
+30,cdk,,rep-eng,LGPL-2.1,https://github.com/cdk/cdk,The Chemistry Development Kit.,25,True,"['cheminformatics', 'lang-java']",cdk/cdk,https://github.com/cdk/cdk,2010-05-11 08:30:07,2023-12-02 12:41:00.000,2023-11-28 15:36:13,17429.0,15.0,147.0,39.0,762.0,25.0,237.0,446.0,2023-08-21 19:50:47,cdk-2.9,20.0,163.0,,,18.0,18.0,,,152.0,,,,1.0,,,,,,16896.0,,org.openscience.cdk:cdk-bundle,https://search.maven.org/artifact/org.openscience.cdk/cdk-bundle,,,,,,,,
+31,QUIP,,general-tool,GPL-2.0,https://github.com/libAtoms/QUIP,libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io.,24,True,,libAtoms/QUIP,https://github.com/libAtoms/QUIP,2013-07-02 15:21:59,2023-10-09 12:20:02.000,2023-08-29 12:01:13,10833.0,,117.0,26.0,165.0,91.0,345.0,309.0,2023-06-15 19:11:24,0.9.14,15.0,80.0,quippy-ase,,25.0,25.0,https://pypi.org/project/quippy-ase,1289.0,1373.0,,,,2.0,libatomsquip/quip,https://hub.docker.com/r/libatomsquip/quip,2023-04-24 21:25:17.345957,4.0,9857.0,347.0,,,,,,,,,,,
+32,MAML,,general-tool,BSD-3-Clause,https://github.com/materialsvirtuallab/maml,"Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.",24,True,,materialsvirtuallab/maml,https://github.com/materialsvirtuallab/maml,2020-01-25 15:04:21,2023-11-30 13:05:25.000,2023-11-06 19:58:55,1617.0,55.0,63.0,21.0,522.0,4.0,60.0,299.0,2023-09-09 22:24:24,2023.9.9,13.0,29.0,maml,,4.0,4.0,https://pypi.org/project/maml,228.0,228.0,,,,2.0,,,,,,,,,,,,,,,,,
+33,Best-of Machine Learning with Python,,community,CC-BY-4.0,https://github.com/ml-tooling/best-of-ml-python,A ranked list of awesome machine learning Python libraries. Updated weekly.,23,True,"['general-ml', 'lang-py']",ml-tooling/best-of-ml-python,https://github.com/ml-tooling/best-of-ml-python,2020-11-29 19:41:36,2023-11-30 16:05:11.000,2023-11-30 16:05:10,442.0,27.0,2102.0,382.0,234.0,17.0,33.0,14695.0,2023-11-30 16:05:19,2023.11.30,100.0,44.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,
+34,dgl-lifesci,,rep-learn,Apache-2.0,https://github.com/awslabs/dgl-lifesci,Python package for graph neural networks in chemistry and biology.,23,True,,awslabs/dgl-lifesci,https://github.com/awslabs/dgl-lifesci,2020-04-23 07:14:21,2023-11-01 19:32:07.000,2023-04-16 03:55:52,236.0,,135.0,17.0,141.0,24.0,57.0,641.0,2023-02-13 08:45:17,0.3.2,8.0,22.0,dgllife,,142.0,142.0,https://pypi.org/project/dgllife,13202.0,13202.0,,,,1.0,,,,,,,,,,,,,,,,,
+35,DP-GEN,,ml-iap,LGPL-3.0,https://github.com/deepmodeling/dpgen,The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field.,23,True,['workflows'],deepmodeling/dpgen,https://github.com/deepmodeling/dpgen,2019-06-13 11:43:56,2023-11-28 05:00:21.000,2023-11-02 05:59:11,2047.0,23.0,162.0,13.0,758.0,17.0,233.0,246.0,2023-11-02 06:14:15,0.12.0,17.0,62.0,dpgen,deepmodeling/dpgen,4.0,4.0,https://pypi.org/project/dpgen,519.0,556.0,https://anaconda.org/deepmodeling/dpgen,2023-06-16 19:27:03.566,191.0,1.0,,,,,,1529.0,,,,,,,,,,,
+36,dpdata,,data-structures,LGPL-3.0,https://github.com/deepmodeling/dpdata,"Manipulating multiple atomic simulation data formats, including DeePMD-kit, VASP, LAMMPS, ABACUS, etc.",23,True,,deepmodeling/dpdata,https://github.com/deepmodeling/dpdata,2019-04-12 13:24:23,2023-12-03 04:35:56.000,2023-10-31 01:50:24,666.0,23.0,109.0,8.0,407.0,10.0,66.0,152.0,2023-10-31 02:23:47,0.2.17,24.0,51.0,dpdata,deepmodeling/dpdata,109.0,109.0,https://pypi.org/project/dpdata,4298.0,4304.0,https://anaconda.org/deepmodeling/dpdata,2023-09-27 20:07:36.945,174.0,1.0,,,,,,,,,,,,,,,,,
+37,kgcnn,,rep-learn,MIT,https://github.com/aimat-lab/gcnn_keras,"Graph convolutions in Keras with TensorFlow, PyTorch or Jax.",23,True,,aimat-lab/gcnn_keras,https://github.com/aimat-lab/gcnn_keras,2020-07-17 11:12:46,2023-12-02 14:51:47.000,2023-12-02 14:47:51,3016.0,231.0,25.0,7.0,30.0,8.0,73.0,90.0,2023-09-16 11:47:45,3.1.0,24.0,7.0,kgcnn,,15.0,15.0,https://pypi.org/project/kgcnn,290.0,290.0,,,,1.0,,,,,,,,,,,,,,,,,
+38,MPContribs,,datasets,MIT,https://github.com/materialsproject/MPContribs,Platform for materials scientists to contribute and disseminate their materials data through Materials Project.,23,True,,materialsproject/MPContribs,https://github.com/materialsproject/MPContribs,2014-12-11 18:25:27,2023-11-29 22:50:51.000,2023-11-29 22:50:41,5387.0,113.0,19.0,11.0,1581.0,20.0,78.0,32.0,2023-11-28 23:05:55,5.6.2,71.0,25.0,mpcontribs-client,,27.0,27.0,https://pypi.org/project/mpcontribs-client,6493.0,6493.0,,,,1.0,,,,,,,,,,,,,,,,,
+39,AlphaFold,,biomolecules,Apache-2.0,https://github.com/google-deepmind/alphafold,Open source code for AlphaFold.,22,True,,deepmind/alphafold,https://github.com/google-deepmind/alphafold,2021-06-17 14:06:06,2023-12-01 15:03:42.000,2023-11-01 12:32:02,133.0,5.0,1905.0,214.0,91.0,186.0,581.0,11091.0,2023-04-05 09:45:53,2.3.2,13.0,19.0,,,7.0,7.0,,,,,,,1.0,,,,,,,,,,google-deepmind/alphafold,,,,,,,
+40,JAX-MD,,md,Apache-2.0,https://github.com/jax-md/jax-md,"Differentiable, Hardware Accelerated, Molecular Dynamics.",22,True,,jax-md/jax-md,https://github.com/jax-md/jax-md,2019-05-13 21:03:37,2023-10-23 08:23:20.000,2023-08-29 15:10:31,838.0,,161.0,49.0,157.0,63.0,74.0,1022.0,2022-11-27 12:42:00,jax-md-v0.2.24,7.0,28.0,jax-md,,37.0,37.0,https://pypi.org/project/jax-md,1253.0,1253.0,,,,1.0,,,,,,,,,,,,,,,,,
+41,DeepQMC,,ml-wft,MIT,https://github.com/deepqmc/deepqmc,Deep learning quantum Monte Carlo for electrons in real space.,22,True,,deepqmc/deepqmc,https://github.com/deepqmc/deepqmc,2019-12-06 14:50:59,2023-11-20 10:29:54.000,2023-11-20 10:29:53,1456.0,135.0,56.0,23.0,154.0,3.0,36.0,307.0,2023-11-20 10:09:02,1.1.2,10.0,13.0,deepqmc,,1.0,1.0,https://pypi.org/project/deepqmc,149.0,149.0,,,,1.0,,,,,,,,,,,,,,,,,
+42,NVIDIA Deep Learning Examples for Tensor Cores,,rep-learn,https://github.com/NVIDIA/DeepLearningExamples/blob/master/DGLPyTorch/DrugDiscovery/SE3Transformer/LICENSE,https://github.com/NVIDIA/DeepLearningExamples#graph-neural-networks,State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and..,21,True,"['educational', 'drug-discovery']",NVIDIA/DeepLearningExamples,https://github.com/NVIDIA/DeepLearningExamples,2018-05-02 17:04:05,2023-11-08 13:05:42.000,2023-10-06 19:54:45,1427.0,3.0,2817.0,293.0,529.0,232.0,558.0,11794.0,,,,115.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+43,DIG: Dive into Graphs,,rep-learn,GPL-3.0,https://github.com/divelab/DIG,A library for graph deep learning research.,21,True,,divelab/DIG,https://github.com/divelab/DIG,2020-10-30 03:51:15,2023-10-25 22:04:21.000,2023-10-25 22:04:21,1073.0,4.0,253.0,33.0,36.0,24.0,168.0,1664.0,2023-04-07 20:33:15,1.1.0,10.0,49.0,dive-into-graphs,,,,https://pypi.org/project/dive-into-graphs,333.0,333.0,,,,2.0,,,,,,,True,,,,,,,,,,
+44,MEGNet,,ml-iap,BSD-3-Clause,https://github.com/materialsvirtuallab/megnet,Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals.,21,True,,materialsvirtuallab/megnet,https://github.com/materialsvirtuallab/megnet,2018-12-12 21:31:28,2023-04-27 02:39:17.000,2023-04-27 02:39:17,1146.0,,142.0,24.0,314.0,17.0,57.0,461.0,2022-11-16 21:24:36,1.3.2,34.0,13.0,megnet,,71.0,71.0,https://pypi.org/project/megnet,315.0,315.0,,,,1.0,,,,,,,,,,,,,,,,,
+45,TorchANI,,ml-iap,MIT,https://github.com/aiqm/torchani,Accurate Neural Network Potential on PyTorch.,21,True,,aiqm/torchani,https://github.com/aiqm/torchani,2018-04-02 15:43:04,2023-11-30 14:35:04.000,2023-11-14 16:32:59,434.0,1.0,112.0,28.0,481.0,19.0,142.0,413.0,2023-11-14 16:38:04,2.2.4,24.0,17.0,torchani,conda-forge/torchani,29.0,29.0,https://pypi.org/project/torchani,1935.0,7597.0,https://anaconda.org/conda-forge/torchani,2023-11-16 16:26:58.145,226497.0,1.0,,,,,,,,,,,,,,,,,
+46,DScribe,,rep-eng,Apache-2.0,https://github.com/SINGROUP/dscribe,DScribe is a python package for creating machine learning descriptors for atomistic systems.,21,True,,SINGROUP/dscribe,https://github.com/SINGROUP/dscribe,2017-05-08 08:29:51,2023-09-06 07:04:16.227,2023-09-05 18:02:57,1287.0,3.0,80.0,20.0,25.0,8.0,79.0,354.0,,,14.0,18.0,dscribe,conda-forge/dscribe,162.0,162.0,https://pypi.org/project/dscribe,7955.0,9917.0,https://anaconda.org/conda-forge/dscribe,2023-09-06 07:04:16.227,80470.0,1.0,,,,,,,,,,,,,,,,,
+47,JARVIS-Tools,,general-tool,https://github.com/usnistgov/jarvis/blob/master/LICENSE.rst,https://github.com/usnistgov/jarvis,JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications:..,21,True,,usnistgov/jarvis,https://github.com/usnistgov/jarvis,2017-06-22 19:34:02,2023-10-18 14:10:33.000,2023-09-22 00:18:02,2094.0,1.0,107.0,27.0,222.0,41.0,43.0,255.0,2023-08-11 17:26:26,2023.08.01,69.0,15.0,jarvis-tools,conda-forge/jarvis-tools,66.0,66.0,https://pypi.org/project/jarvis-tools,929.0,2487.0,https://anaconda.org/conda-forge/jarvis-tools,2023-06-16 19:23:23.093,59226.0,2.0,,,,,,,,,,,,,,,,,
+48,MatGL (Materials Graph Library),,rep-learn,BSD-3-Clause,https://github.com/materialsvirtuallab/matgl,Graph deep learning library for materials.,21,True,,materialsvirtuallab/matgl,https://github.com/materialsvirtuallab/matgl,2022-08-29 18:36:05,2023-11-27 21:05:00.000,2023-11-27 21:04:36,903.0,51.0,32.0,8.0,143.0,3.0,43.0,146.0,2023-11-17 20:11:53,0.9.1,25.0,13.0,m3gnet,,17.0,17.0,https://pypi.org/project/m3gnet,487.0,487.0,,,,2.0,,,,,,,,,,,,,,,,,
+49,DM21,,ml-dft,Apache-2.0,https://github.com/deepmind/deepmind-research/tree/master/density_functional_approximation_dm21,This package provides a PySCF interface to the DM21 (DeepMind 21) family of exchange-correlation functionals described..,20,True,,deepmind/deepmind-research,https://github.com/google-deepmind/deepmind-research,2019-01-15 09:54:13,2023-11-27 23:26:48.000,2023-06-02 17:04:50,369.0,,2431.0,337.0,180.0,166.0,135.0,12311.0,,,,92.0,,,,,,,,,,,1.0,,,,,,,,,,google-deepmind/deepmind-research,,,,,,,
+50,NequIP,,ml-iap,MIT,https://github.com/mir-group/nequip,NequIP is a code for building E(3)-equivariant interatomic potentials.,20,True,,mir-group/nequip,https://github.com/mir-group/nequip,2021-03-15 23:44:39,2023-10-27 20:35:22.000,2023-03-26 21:37:08,1670.0,,105.0,18.0,149.0,14.0,52.0,471.0,2022-12-20 18:52:46,0.5.6,14.0,8.0,nequip,conda-forge/nequip,16.0,16.0,https://pypi.org/project/nequip,550.0,745.0,https://anaconda.org/conda-forge/nequip,2023-06-18 08:41:30.787,3708.0,1.0,,,,,,,,,,,,,,,,,
+51,CHGNet,,ml-iap,https://github.com/CederGroupHub/chgnet/blob/main/LICENSE,https://github.com/CederGroupHub/chgnet,Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov.,20,True,"['md', 'pre-trained', 'electrostatics', 'magnetism', 'structure-relaxation']",CederGroupHub/chgnet,https://github.com/CederGroupHub/chgnet,2023-02-24 23:44:24,2023-11-19 04:15:36.000,2023-11-19 04:10:58,345.0,62.0,29.0,4.0,61.0,3.0,26.0,143.0,2023-11-19 04:15:36,0.3.2,10.0,7.0,chgnet,,9.0,9.0,https://pypi.org/project/chgnet,6985.0,6985.0,,,,1.0,,,,,,,,,,,,,,,,,
+52,ocp,,rep-learn,MIT,https://github.com/Open-Catalyst-Project/ocp,ocp is the Open Catalyst Projects library of state-of-the-art machine learning algorithms for catalysis.,19,True,,Open-Catalyst-Project/ocp,https://github.com/Open-Catalyst-Project/ocp,2019-09-26 04:47:27,2023-11-28 00:24:43.000,2023-11-27 18:44:01,710.0,12.0,179.0,23.0,457.0,13.0,132.0,520.0,2022-10-01 03:00:41,0.1.0,4.0,32.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+53,Pre-trained OCP models,,ml-iap,MIT,https://github.com/Open-Catalyst-Project/ocp/blob/main/MODELS.md,Pre-trained models released as part of the Open Catalyst Project.,19,True,['pre-trained'],Open-Catalyst-Project/ocp,https://github.com/Open-Catalyst-Project/ocp,2019-09-26 04:47:27,2023-11-28 00:24:43.000,2023-11-27 18:44:01,710.0,12.0,179.0,23.0,457.0,13.0,132.0,520.0,2022-10-01 03:00:41,0.1.0,4.0,32.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+54,exmol,,xai,MIT,https://github.com/ur-whitelab/exmol,Explainer for black box models that predict molecule properties.,19,True,,ur-whitelab/exmol,https://github.com/ur-whitelab/exmol,2021-08-03 17:56:06,2023-11-27 18:45:16.000,2023-06-19 19:49:27,188.0,,40.0,9.0,76.0,10.0,57.0,259.0,2023-06-19 20:50:51,3.0.3,27.0,7.0,exmol,,14.0,14.0,https://pypi.org/project/exmol,784.0,784.0,,,,1.0,,,,,,,,,,,,,,,,,
+55,Metatensor,,data-structures,BSD-3-Clause,https://github.com/lab-cosmo/metatensor,Self-describing sparse tensor data format for atomistic machine learning and beyond.,19,True,"['lang-rust', 'lang-c', 'lang-cpp', 'lang-py']",lab-cosmo/metatensor,https://github.com/lab-cosmo/metatensor,2022-03-01 15:58:28,2023-12-02 19:16:48.000,2023-11-30 14:51:44,461.0,84.0,11.0,17.0,306.0,41.0,80.0,29.0,2023-10-11 15:57:13,metatensor-torch-v0.1.0,13.0,17.0,,,6.0,6.0,,,1128.0,,,,2.0,,,,,,2256.0,,,,,,,,,,,
+56,Graph-based Deep Learning Literature,,community,MIT,https://github.com/naganandy/graph-based-deep-learning-literature,links to conference publications in graph-based deep learning.,18,True,"['general-ml', 'rep-learn']",naganandy/graph-based-deep-learning-literature,https://github.com/naganandy/graph-based-deep-learning-literature,2017-12-01 14:48:35,2023-11-19 05:55:34.000,2023-11-19 05:55:28,7632.0,10.0,725.0,243.0,21.0,,13.0,4451.0,,,,12.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,
+57,Open Catalyst datasets,,datasets,CC-BY-4.0,https://github.com/Open-Catalyst-Project/ocp/blob/main/DATASET.md,"The datasets of the Open Catalyst project, OC20, OC22.",18,True,,Open-Catalyst-Project/ocp,https://github.com/Open-Catalyst-Project/ocp,2019-09-26 04:47:27,2023-11-28 00:24:43.000,2023-11-27 18:44:01,710.0,12.0,179.0,23.0,457.0,13.0,132.0,520.0,2022-10-01 03:00:41,0.1.0,4.0,32.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,
+58,ATOM3D,,datasets,MIT,https://github.com/drorlab/atom3d,ATOM3D: tasks on molecules in three dimensions.,18,True,"['biomolecules', 'benchmarking']",drorlab/atom3d,https://github.com/drorlab/atom3d,2020-04-03 22:53:11,2023-03-02 18:21:02.000,2023-03-02 18:20:29,798.0,,33.0,16.0,6.0,19.0,40.0,279.0,2022-07-20 00:56:05,0.2.6,15.0,10.0,atom3d,,27.0,27.0,https://pypi.org/project/atom3d,435.0,435.0,,,,1.0,,,,,,,True,,,,,,,,,,
+59,FLARE,,active-learning,MIT,https://github.com/mir-group/flare,An open-source Python package for creating fast and accurate interatomic potentials.,18,True,"['lang-cpp', 'ml-iap']",mir-group/flare,https://github.com/mir-group/flare,2018-08-30 23:40:56,2023-09-29 22:57:11.000,2023-05-26 02:06:09,4382.0,,59.0,18.0,185.0,23.0,164.0,253.0,2022-04-21 18:33:10,0.2.4,5.0,37.0,,,10.0,10.0,,,0.0,,,,1.0,,,,,,2.0,,,,,,,,,,,
+60,MACE,,ml-iap,MIT,https://github.com/ACEsuit/mace,MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.,18,True,,ACEsuit/mace,https://github.com/ACEsuit/mace,2022-06-21 18:44:34,2023-12-01 22:19:38.000,2023-12-01 11:29:48,373.0,70.0,77.0,18.0,99.0,18.0,86.0,218.0,2023-11-09 17:28:57,0.3.0,2.0,16.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+61,ALIGNN,,rep-learn,https://github.com/usnistgov/alignn/blob/main/LICENSE.rst,https://github.com/usnistgov/alignn,Atomistic Line Graph Neural Network.,18,True,,usnistgov/alignn,https://github.com/usnistgov/alignn,2021-04-19 20:08:09,2023-10-24 20:00:53.000,2023-10-24 20:00:52,623.0,19.0,64.0,11.0,90.0,24.0,23.0,165.0,2023-08-11 04:51:42,2023.08.01,40.0,7.0,alignn,,8.0,8.0,https://pypi.org/project/alignn,404.0,404.0,,,,2.0,,,,,,,,,,,,,,,,,
+62,e3nn-jax,,rep-learn,Apache-2.0,https://github.com/e3nn/e3nn-jax,jax library for E3 Equivariant Neural Networks.,18,True,,e3nn/e3nn-jax,https://github.com/e3nn/e3nn-jax,2021-06-08 13:21:51,2023-11-24 09:26:06.000,2023-11-24 09:26:01,951.0,57.0,14.0,9.0,30.0,,10.0,125.0,2023-11-17 11:39:54,0.20.3,39.0,5.0,e3nn-jax,,,,https://pypi.org/project/e3nn-jax,1878.0,1878.0,,,,2.0,,,,,,,,,,,,,,,,,
+63,FitSNAP,,md,GPL-2.0,https://github.com/FitSNAP/FitSNAP,Software for generating SNAP machine-learning interatomic potentials.,18,True,,FitSNAP/FitSNAP,https://github.com/FitSNAP/FitSNAP,2019-09-12 14:46:18,2023-11-07 18:49:16.000,2023-11-04 02:17:28,1366.0,62.0,45.0,7.0,174.0,8.0,55.0,125.0,2023-06-28 16:00:48,3.1.0,7.0,24.0,,conda-forge/fitsnap3,,,,,139.0,https://anaconda.org/conda-forge/fitsnap3,2023-06-16 00:19:04.615,5163.0,2.0,,,,,,7.0,,,,,,,,,,,
+64,Chemiscope,,visualization,BSD-3-Clause,https://github.com/lab-cosmo/chemiscope,An interactive structure/property explorer for materials and molecules.,18,True,['lang-js'],lab-cosmo/chemiscope,https://github.com/lab-cosmo/chemiscope,2019-10-03 09:59:42,2023-11-30 23:51:24.000,2023-11-30 23:49:13,684.0,33.0,27.0,18.0,205.0,33.0,80.0,94.0,2023-10-23 15:11:17,0.6.0,13.0,19.0,,,5.0,5.0,,,26.0,,,,1.0,,,,,,138.0,,,,,chemiscope,https://www.npmjs.com/package/chemiscope,23.0,,,,
+65,MALA,,ml-dft,BSD-3-Clause,https://github.com/mala-project/mala,Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data.,18,True,,mala-project/mala,https://github.com/mala-project/mala,2021-03-31 11:40:38,2023-11-28 10:04:41.000,2023-10-26 10:42:52,2086.0,16.0,20.0,9.0,256.0,26.0,211.0,55.0,2023-09-28 13:54:19,1.2.0,8.0,41.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,
+66,Uni-Mol,,rep-learn,MIT,https://github.com/dptech-corp/Uni-Mol,Official Repository for the Uni-Mol Series Methods.,17,True,['pre-trained'],dptech-corp/Uni-Mol,https://github.com/dptech-corp/Uni-Mol,2022-05-22 13:26:41,2023-12-03 05:26:31.000,2023-12-03 05:26:31,95.0,12.0,86.0,16.0,70.0,44.0,75.0,456.0,2023-07-07 09:02:23,0.2,2.0,13.0,,,,,,,543.0,,,,2.0,,,,,,7604.0,,,,,,,,,,,
+67,ChemCrow,,language-models,MIT,https://github.com/ur-whitelab/chemcrow-public,Chemcrow.,17,False,,ur-whitelab/chemcrow-public,https://github.com/ur-whitelab/chemcrow-public,2023-06-04 15:59:05,2023-11-27 08:20:24.000,2023-11-27 08:09:11,62.0,23.0,37.0,14.0,5.0,6.0,3.0,316.0,2023-11-27 08:20:24,0.3.17,5.0,3.0,chemcrow,,2.0,2.0,https://pypi.org/project/chemcrow,1031.0,1031.0,,,,1.0,,,,,,,True,,,,,,,,,,
+68,GT4SD,,generative,MIT,https://github.com/GT4SD/gt4sd-core,"GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process.",17,True,"['pre-trained', 'drug-discovery', 'rep-learn']",GT4SD/gt4sd-core,https://github.com/GT4SD/gt4sd-core,2022-02-11 19:06:58,2023-11-27 08:23:51.000,2023-10-16 07:22:44,283.0,3.0,56.0,16.0,136.0,2.0,92.0,280.0,2023-05-06 06:55:52,1.3.1,54.0,19.0,gt4sd,,,,https://pypi.org/project/gt4sd,817.0,817.0,,,,1.0,,,,,,,,,,,,,,,,,
+69,M3GNet,,ml-iap,BSD-3-Clause,https://github.com/materialsvirtuallab/m3gnet,Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art..,17,True,,materialsvirtuallab/m3gnet,https://github.com/materialsvirtuallab/m3gnet,2022-01-18 18:10:58,2023-06-06 23:56:08.000,2023-06-06 23:56:03,261.0,,53.0,10.0,33.0,15.0,20.0,188.0,2022-11-17 23:25:35,0.2.4,16.0,14.0,m3gnet,,18.0,18.0,https://pypi.org/project/m3gnet,487.0,487.0,,,,2.0,,,,,,,,,,,,,,,,,
+70,KFAC-JAX,,math,Apache-2.0,https://github.com/google-deepmind/kfac-jax,Second Order Optimization and Curvature Estimation with K-FAC in JAX.,17,True,,deepmind/kfac-jax,https://github.com/google-deepmind/kfac-jax,2022-03-18 10:19:24,2023-11-29 18:10:59.000,2023-11-29 18:10:53,168.0,25.0,13.0,8.0,180.0,2.0,9.0,173.0,2023-05-16 18:03:40,0.0.5,4.0,11.0,kfac-jax,,8.0,8.0,https://pypi.org/project/kfac-jax,692.0,692.0,,,,1.0,,,,,,,,,,google-deepmind/kfac-jax,,,,,,,
+71,XenonPy,,general-tool,BSD-3-Clause,https://github.com/yoshida-lab/XenonPy,XenonPy is a Python Software for Materials Informatics.,17,True,,yoshida-lab/XenonPy,https://github.com/yoshida-lab/XenonPy,2018-01-17 10:13:29,2023-11-20 14:25:49.000,2023-11-20 14:25:43,692.0,10.0,55.0,12.0,183.0,17.0,66.0,113.0,2023-05-21 15:54:32,0.6.8,45.0,10.0,xenonpy,,,,https://pypi.org/project/xenonpy,469.0,487.0,,,,2.0,,,,,,1244.0,,,,,,,,,,,
+72,MatBench,,community,MIT,https://github.com/materialsproject/matbench,Matbench: Benchmarks for materials science property prediction.,17,True,"['datasets', 'benchmarking']",materialsproject/matbench,https://github.com/materialsproject/matbench,2021-02-24 03:58:42,2023-11-14 15:38:45.000,2023-11-14 15:38:41,746.0,6.0,32.0,9.0,252.0,31.0,26.0,86.0,2022-07-27 04:40:26,0.6,5.0,23.0,matbench,,11.0,11.0,https://pypi.org/project/matbench,138.0,138.0,,,,2.0,,,,,,,,,,,,,,,,,
+73,Ultra-Fast Force Fields (UF3),,ml-iap,Apache-2.0,https://github.com/uf3/uf3,UF3: a python library for generating ultra-fast interatomic potentials.,17,True,,uf3/uf3,https://github.com/uf3/uf3,2021-10-01 13:21:44,2023-12-01 20:06:04.000,2023-10-27 16:18:56,514.0,51.0,18.0,6.0,70.0,12.0,24.0,45.0,2023-10-27 16:36:21,0.4.0,4.0,8.0,uf3,,,,https://pypi.org/project/uf3,38.0,38.0,,,,2.0,,,,,,,,,,,,,,,,,
+74,ChemDataExtractor,,language-models,MIT,https://github.com/mcs07/ChemDataExtractor,Automatically extract chemical information from scientific documents.,16,False,['literature-data'],mcs07/ChemDataExtractor,https://github.com/mcs07/ChemDataExtractor,2016-10-02 23:50:01,2023-07-27 18:05:13.000,2017-02-21 23:20:23,106.0,,105.0,19.0,16.0,17.0,9.0,267.0,2017-02-03 00:28:29,1.3.0,7.0,2.0,chemdataextractor,chemdataextractor/chemdataextractor,100.0,100.0,https://pypi.org/project/chemdataextractor,654.0,719.0,https://anaconda.org/chemdataextractor/chemdataextractor,2023-06-16 13:17:47.249,3009.0,2.0,,,,,,2723.0,True,,,,,,,,,,
+75,MoLeR,,generative,MIT,https://github.com/microsoft/molecule-generation,Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation.,16,True,,microsoft/molecule-generation,https://github.com/microsoft/molecule-generation,2022-02-17 19:16:29,2023-11-23 13:48:47.000,2023-08-09 14:17:01,63.0,,35.0,11.0,33.0,7.0,26.0,222.0,2023-06-18 21:03:46,0.4.0,4.0,5.0,molecule-generation,,,,https://pypi.org/project/molecule-generation,222.0,222.0,,,,1.0,,,,,,,,,,,,,,,,,
+76,ChemNLP project,,language-models,MIT,https://github.com/OpenBioML/chemnlp,ChemNLP project.,16,True,['datasets'],OpenBioML/chemnlp,https://github.com/OpenBioML/chemnlp,2023-02-13 16:20:23,2023-12-02 18:24:02.000,2023-12-01 17:23:50,323.0,78.0,43.0,3.0,271.0,110.0,139.0,113.0,,,,26.0,chemnlp,,,,https://pypi.org/project/chemnlp,61.0,61.0,,,,2.0,,,,,,,True,,,,,,,,,,
+77,CatLearn,,rep-eng,GPL-3.0,https://github.com/SUNCAT-Center/CatLearn,,16,True,['surface-science'],SUNCAT-Center/CatLearn,https://github.com/SUNCAT-Center/CatLearn,2018-04-20 04:16:14,2023-07-25 21:09:47.000,2023-02-07 09:31:25,1960.0,,50.0,19.0,79.0,10.0,16.0,94.0,2020-03-27 09:26:03,0.6.2,8.0,22.0,catlearn,,4.0,4.0,https://pypi.org/project/catlearn,250.0,250.0,,,,1.0,,,,,,,,,,,,,,,,,
+78,MAST-ML,,general-tool,MIT,https://github.com/uw-cmg/MAST-ML,MAterials Simulation Toolkit for Machine Learning (MAST-ML).,16,True,,uw-cmg/MAST-ML,https://github.com/uw-cmg/MAST-ML,2017-02-16 17:03:57,2023-12-01 20:57:10.000,2023-07-28 18:33:43,3162.0,,52.0,13.0,36.0,22.0,191.0,88.0,2023-05-01 21:32:25,3.1.7,6.0,19.0,,,7.0,7.0,,,2.0,,,,2.0,,,,,,84.0,,,,,,,,,,,
+79,matsciml,,rep-learn,MIT,https://github.com/IntelLabs/matsciml,Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery..,16,True,"['workflows', 'benchmarking']",IntelLabs/matsciml,https://github.com/IntelLabs/matsciml,2022-09-13 20:27:28,2023-12-01 21:58:18.000,2023-12-01 17:55:58,1501.0,168.0,12.0,4.0,58.0,5.0,10.0,83.0,2023-08-31 23:59:40,1.0.0,2.0,8.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+80,Scikit-Matter,,general-tool,BSD-3-Clause,https://github.com/scikit-learn-contrib/scikit-matter,A collection of scikit-learn compatible utilities that implement methods born out of the materials science and..,16,True,['scikit-learn'],scikit-learn-contrib/scikit-matter,https://github.com/scikit-learn-contrib/scikit-matter,2020-10-12 19:23:26,2023-11-20 15:32:27.000,2023-09-22 08:33:28,364.0,2.0,15.0,17.0,150.0,12.0,56.0,65.0,2023-08-24 17:18:49,0.2.0,7.0,13.0,skmatter,conda-forge/skmatter,7.0,7.0,https://pypi.org/project/skmatter,421.0,499.0,https://anaconda.org/conda-forge/skmatter,2023-08-24 19:08:29.551,702.0,2.0,,,,,,,,,,,,,,,,,
+81,mlcolvar,,md,MIT,https://github.com/luigibonati/mlcolvar,A unified framework for machine learning collective variables for enhanced sampling simulations.,16,True,['enhanced-sampling'],luigibonati/mlcolvar,https://github.com/luigibonati/mlcolvar,2021-09-21 21:32:04,2023-11-17 11:27:21.000,2023-11-17 11:27:16,815.0,21.0,13.0,5.0,56.0,12.0,36.0,59.0,2023-10-25 08:58:49,1.0.1,8.0,7.0,mlcolvar,,,,https://pypi.org/project/mlcolvar,80.0,80.0,,,,2.0,,,,,,,,,,,,,,,,,
+82,MatBench Discovery,,community,MIT,https://github.com/janosh/matbench-discovery,An evaluation framework for machine learning models simulating high-throughput materials discovery.,16,True,"['datasets', 'benchmarking']",janosh/matbench-discovery,https://github.com/janosh/matbench-discovery,2022-06-20 18:32:44,2023-12-03 20:35:36.000,2023-12-03 20:35:29,298.0,33.0,5.0,7.0,41.0,3.0,24.0,44.0,2023-09-13 14:29:49,1.0.0,2.0,5.0,matbench-discovery,,,,https://pypi.org/project/matbench-discovery,32.0,32.0,,,,2.0,,,,,,,,,,,,,,,,,
+83,FermiNet,,ml-wft,Apache-2.0,https://github.com/google-deepmind/ferminet,An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations.,15,True,['transformer'],deepmind/ferminet,https://github.com/google-deepmind/ferminet,2020-10-06 12:21:06,2023-11-27 07:54:15.000,2023-11-27 07:53:26,216.0,24.0,102.0,34.0,28.0,,42.0,615.0,,,,18.0,,,,,,,,,,,2.0,,,,,,,,,,google-deepmind/ferminet,,,,,,,
+84,Uni-Fold,,biomolecules,Apache-2.0,https://github.com/dptech-corp/Uni-Fold,An open-source platform for developing protein models beyond AlphaFold.,15,True,,dptech-corp/Uni-Fold,https://github.com/dptech-corp/Uni-Fold,2022-07-30 03:37:29,2023-11-30 07:37:27.000,2023-11-30 07:37:23,97.0,2.0,53.0,7.0,76.0,14.0,48.0,313.0,2022-10-19 12:44:31,2.2.0,3.0,7.0,,,,,,,187.0,,,,3.0,,,,,,2618.0,,,,,,,,,,,
+85,GT4SD - Generative Toolkit for Scientific Discovery,,community,MIT,https://huggingface.co/GT4SD,Gradio apps of generative models in GT4SD.,15,True,"['generative', 'pre-trained', 'drug-discovery']",GT4SD/gt4sd-core,https://github.com/GT4SD/gt4sd-core,2022-02-11 19:06:58,2023-11-27 08:23:51.000,2023-10-16 07:22:44,283.0,3.0,56.0,16.0,136.0,2.0,92.0,280.0,2023-05-06 06:55:52,1.3.1,54.0,19.0,,,,,,,,,,,2.0,,,,,,,True,,,,,,,,,,
+86,QML,,general-tool,MIT,https://github.com/qmlcode/qml,QML: Quantum Machine Learning.,15,False,,qmlcode/qml,https://github.com/qmlcode/qml,2017-04-22 04:48:38,2023-07-26 12:01:42.000,2018-09-10 11:14:35,75.0,,80.0,23.0,101.0,27.0,19.0,189.0,,,3.0,2.0,qml,,22.0,22.0,https://pypi.org/project/qml,294.0,294.0,,,,3.0,,,,,,,,,,,,,,,,,
+87,gpax,,math,MIT,https://github.com/ziatdinovmax/gpax,Gaussian Processes for Experimental Sciences.,15,True,"['probabilistic', 'active-learning']",ziatdinovmax/gpax,https://github.com/ziatdinovmax/gpax,2021-10-28 13:43:18,2023-12-02 18:09:54.000,2023-12-02 18:09:54,583.0,78.0,16.0,4.0,40.0,5.0,14.0,148.0,2023-10-11 22:13:25,0.1.1,10.0,2.0,gpax,,,,https://pypi.org/project/gpax,142.0,142.0,,,,1.0,,,,,,,,,,,,,,,,,
+88,openmm-torch,,md,https://github.com/openmm/openmm-torch#license,https://github.com/openmm/openmm-torch,OpenMM plugin to define forces with neural networks.,15,True,"['ml-iap', 'lang-cpp']",openmm/openmm-torch,https://github.com/openmm/openmm-torch,2019-09-27 18:15:19,2023-11-29 21:24:23.296,2023-10-03 16:21:43,61.0,5.0,22.0,13.0,56.0,16.0,54.0,137.0,2023-10-09 08:49:10,1.4,16.0,7.0,,conda-forge/openmm-torch,,,,,6504.0,https://anaconda.org/conda-forge/openmm-torch,2023-11-29 21:24:23.296,227674.0,2.0,,,,,,,,,,,,,,,,,
+89,Automatminer,,general-tool,https://github.com/hackingmaterials/automatminer/blob/main/LICENSE,https://github.com/hackingmaterials/automatminer,An automatic engine for predicting materials properties.,15,False,,hackingmaterials/automatminer,https://github.com/hackingmaterials/automatminer,2018-05-10 18:27:08,2023-11-12 10:09:39.000,2022-01-06 19:39:49,1666.0,,47.0,12.0,233.0,36.0,138.0,131.0,2020-07-28 02:19:07,1.0.3.20200727,17.0,13.0,automatminer,,7.0,7.0,https://pypi.org/project/automatminer,135.0,135.0,,,,3.0,,,,,,,,,,,,,,,,,
+90,sGDML,,ml-iap,MIT,https://github.com/stefanch/sGDML,sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model.,15,True,,stefanch/sGDML,https://github.com/stefanch/sGDML,2018-07-11 15:20:30,2023-08-31 12:58:49.000,2023-08-31 12:57:53,205.0,,35.0,8.0,12.0,6.0,11.0,127.0,2023-08-31 12:58:49,1.0.2,15.0,8.0,sgdml,,8.0,8.0,https://pypi.org/project/sgdml,111.0,111.0,,,,2.0,,,,,,,,,,,,,,,,,
+91,NNPOps,,ml-iap,MIT,https://github.com/openmm/NNPOps,High-performance operations for neural network potentials.,15,True,"['md', 'lang-cpp']",openmm/NNPOps,https://github.com/openmm/NNPOps,2020-09-10 21:02:00,2023-11-17 10:54:59.000,2023-07-25 21:23:53,94.0,,15.0,10.0,62.0,20.0,33.0,72.0,2023-07-26 11:21:58,0.6,7.0,7.0,,conda-forge/nnpops,,,,,3492.0,https://anaconda.org/conda-forge/nnpops,2023-11-06 09:36:30.898,73337.0,2.0,,,,,,,,,,,,,,,,,
+92,Open Databases Integration for Materials Design (OPTIMADE),,datasets,CC-BY-4.0,https://github.com/Materials-Consortia/OPTIMADE,Specification of a common REST API for access to materials databases.,15,True,,Materials-Consortia/OPTIMADE,https://github.com/Materials-Consortia/OPTIMADE,2018-01-08 23:32:29,2023-12-03 16:46:00.000,2023-06-22 15:32:33,1276.0,,35.0,22.0,266.0,72.0,145.0,62.0,2021-07-08 15:20:37,1.1.0,7.0,19.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+93,MODNet,,rep-eng,MIT,https://github.com/ppdebreuck/modnet,MODNet: a framework for machine learning materials properties.,15,True,"['pre-trained', 'small-data', 'transfer-learning']",ppdebreuck/modnet,https://github.com/ppdebreuck/modnet,2020-03-13 07:39:21,2023-12-01 21:36:10.000,2023-11-13 22:45:08,259.0,2.0,28.0,7.0,145.0,13.0,23.0,62.0,2023-07-29 11:00:10,0.4.1,18.0,7.0,,,5.0,5.0,,,,,,,2.0,,,,,,,,,,,,,,,,,
+94,benchmarking-gnns,,rep-learn,MIT,https://github.com/graphdeeplearning/benchmarking-gnns,Repository for benchmarking graph neural networks.,14,False,"['single-paper', 'benchmarking']",graphdeeplearning/benchmarking-gnns,https://github.com/graphdeeplearning/benchmarking-gnns,2020-03-03 03:42:50,2023-06-22 04:03:53.000,2022-05-10 13:22:20,45.0,,421.0,59.0,17.0,4.0,61.0,2300.0,,,,5.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+95,dlpack,,data-structures,Apache-2.0,https://github.com/dmlc/dlpack,common in-memory tensor structure.,14,True,['lang-cpp'],dmlc/dlpack,https://github.com/dmlc/dlpack,2017-02-24 16:56:47,2023-03-20 13:30:47.000,2023-01-24 14:10:09,68.0,,126.0,47.0,70.0,24.0,35.0,800.0,2023-01-05 18:42:00,0.8,8.0,21.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+96,escnn,,rep-learn,https://github.com/QUVA-Lab/escnn/blob/master/LICENSE,https://github.com/QUVA-Lab/escnn,Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/.,14,True,,QUVA-Lab/escnn,https://github.com/QUVA-Lab/escnn,2022-03-16 10:15:02,2023-11-27 17:43:58.000,2023-10-17 22:37:11,244.0,4.0,35.0,16.0,32.0,21.0,36.0,255.0,,,,10.0,escnn,,,,https://pypi.org/project/escnn,518.0,518.0,,,,2.0,,,,,,,,,,,,,,,,,
+97,gptchem,,language-models,MIT,https://github.com/kjappelbaum/gptchem,Use GPT-3 to solve chemistry problems.,14,True,,kjappelbaum/gptchem,https://github.com/kjappelbaum/gptchem,2023-01-06 15:34:32,2023-11-30 09:31:35.000,2023-10-04 11:27:09,147.0,29.0,30.0,8.0,4.0,15.0,2.0,165.0,2023-11-30 09:31:51,0.0.4,2.0,2.0,gptchem,,,,https://pypi.org/project/gptchem,39.0,39.0,,,,2.0,,,,,,,,,,,,,,,,,
+98,DADApy,,unsupervised,Apache-2.0,https://github.com/sissa-data-science/DADApy,Distance-based Analysis of DAta-manifolds in python.,14,True,,sissa-data-science/DADApy,https://github.com/sissa-data-science/DADApy,2021-02-16 17:45:23,2023-12-03 20:31:07.000,2023-11-16 14:28:31,654.0,11.0,11.0,8.0,77.0,11.0,15.0,77.0,2023-05-25 16:37:17,0.2.0,3.0,16.0,dadapy,,2.0,2.0,https://pypi.org/project/dadapy,80.0,80.0,,,,1.0,,,,,,,,,,,,,,,,,
+99,SpheriCart,,math,Apache-2.0,https://github.com/lab-cosmo/sphericart,Multi-language library for the calculation of spherical harmonics in Cartesian coordinates.,14,True,,lab-cosmo/sphericart,https://github.com/lab-cosmo/sphericart,2023-02-04 15:15:25,2023-11-29 14:24:27.000,2023-11-27 12:47:38,332.0,15.0,5.0,4.0,72.0,9.0,7.0,46.0,2023-04-26 12:06:09,0.3.0,1.0,9.0,sphericart,,1.0,1.0,https://pypi.org/project/sphericart,212.0,212.0,,,,2.0,,,,,,1.0,,,,,,,,,,,
+100,mp-pyrho,,data-structures,https://github.com/materialsproject/pyrho,https://github.com/materialsproject/pyrho,Tools for re-griding volumetric quantum chemistry data for machine-learning purposes.,14,True,['ml-dft'],materialsproject/pyrho,https://github.com/materialsproject/pyrho,2020-05-25 22:44:02,2023-11-28 00:13:22.000,2023-03-21 17:22:18,244.0,,6.0,9.0,104.0,1.0,3.0,29.0,2022-10-20 05:07:16,0.3.0,23.0,8.0,mp-pyrho,,18.0,18.0,https://pypi.org/project/mp-pyrho,81.0,81.0,,,,3.0,,,,,,,,,,,,,,,,,
+101,KLIFF,,ml-iap,LGPL-2.1,https://github.com/openkim/kliff,KIM-based Learning-Integrated Fitting Framework (KLIFF).,14,True,"['probabilistic', 'workflows']",openkim/kliff,https://github.com/openkim/kliff,2017-08-01 20:33:58,2023-11-28 00:02:15.000,2023-11-16 05:26:53,994.0,10.0,17.0,2.0,105.0,18.0,16.0,27.0,2022-10-07 05:16:11,0.4.1,16.0,10.0,kliff,conda-forge/kliff,,,https://pypi.org/project/kliff,45.0,1767.0,https://anaconda.org/conda-forge/kliff,2023-10-07 06:09:56.646,65461.0,2.0,,,,,,,,,,,,,,,,,
+102,Polynomials4ML.jl,,math,MIT,https://github.com/ACEsuit/Polynomials4ML.jl,"Polynomials for ML: fast evaluation, batching, differentiation.",14,True,['lang-julia'],ACEsuit/Polynomials4ML.jl,https://github.com/ACEsuit/Polynomials4ML.jl,2022-09-20 23:05:53,2023-11-25 22:48:16.000,2023-11-23 22:48:55,327.0,41.0,5.0,4.0,34.0,15.0,29.0,12.0,2023-11-15 23:52:25,0.2.7,11.0,9.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+103,n2p2,,ml-iap,GPL-3.0,https://github.com/CompPhysVienna/n2p2,n2p2 - A Neural Network Potential Package.,13,False,['lang-cpp'],CompPhysVienna/n2p2,https://github.com/CompPhysVienna/n2p2,2018-07-25 12:29:17,2023-05-11 16:26:02.000,2022-09-05 10:56:20,387.0,,69.0,12.0,53.0,57.0,85.0,195.0,2022-05-23 12:53:39,2.2.0,11.0,9.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+104,DeepH-pack,,ml-dft,LGPL-3.0,https://github.com/mzjb/DeepH-pack,Deep neural networks for density functional theory Hamiltonian.,13,True,['lang-julia'],mzjb/DeepH-pack,https://github.com/mzjb/DeepH-pack,2022-05-13 02:51:32,2023-08-03 05:36:54.000,2023-07-11 08:11:15,54.0,,27.0,5.0,13.0,5.0,31.0,155.0,2023-07-11 08:13:06,0.2.2,2.0,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+105,DMFF,,ml-iap,LGPL-3.0,https://github.com/deepmodeling/DMFF,DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable..,13,True,,deepmodeling/DMFF,https://github.com/deepmodeling/DMFF,2022-02-14 01:35:50,2023-11-22 10:04:10.000,2023-11-09 06:31:11,427.0,6.0,32.0,11.0,131.0,7.0,14.0,119.0,2023-11-09 14:32:37,1.0.0,4.0,13.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+106,SPICE,,datasets,MIT,https://github.com/openmm/spice-dataset,A collection of QM data for training potential functions.,13,True,"['ml-iap', 'md']",openmm/spice-dataset,https://github.com/openmm/spice-dataset,2021-08-31 18:52:05,2023-10-22 18:03:55.000,2023-10-22 18:03:55,34.0,3.0,5.0,16.0,38.0,12.0,37.0,104.0,2023-08-07 19:52:03,1.1.4,6.0,,,,,,,,13.0,,,,2.0,,,,,,221.0,,,,,,,,,,,
+107,PyXtalFF,,ml-iap,MIT,https://github.com/MaterSim/PyXtal_FF,Machine Learning Interatomic Potential Predictions.,13,True,,MaterSim/PyXtal_FF,https://github.com/MaterSim/PyXtal_FF,2019-01-08 08:43:35,2023-08-17 01:22:23.000,2023-08-17 01:22:18,559.0,,19.0,9.0,2.0,9.0,51.0,75.0,2023-06-09 17:17:24,0.2.3,19.0,8.0,pyxtal_ff,,,,https://pypi.org/project/pyxtal_ff,32.0,32.0,,,,2.0,,,,,,,,,,,,,,,,,
+108,aviary,,materials-discovery,MIT,https://github.com/CompRhys/aviary,The Wren sits on its Roost in the Aviary.,13,True,,CompRhys/aviary,https://github.com/CompRhys/aviary,2021-09-28 12:29:05,2023-11-10 23:21:39.000,2023-11-10 23:21:36,607.0,4.0,8.0,2.0,51.0,4.0,22.0,30.0,2023-08-10 01:55:58,0.1.1,4.0,4.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,
+109,mat2vec,,language-models,MIT,https://github.com/materialsintelligence/mat2vec,Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from materials..,12,True,['rep-learn'],materialsintelligence/mat2vec,https://github.com/materialsintelligence/mat2vec,2019-04-25 07:55:30,2023-05-06 22:45:49.000,2023-05-06 22:45:49,55.0,,174.0,39.0,7.0,7.0,17.0,598.0,,,,5.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+110,Crystal Graph Convolutional Neural Networks (CGCNN),,rep-learn,MIT,https://github.com/txie-93/cgcnn,Crystal graph convolutional neural networks for predicting material properties.,12,False,,txie-93/cgcnn,https://github.com/txie-93/cgcnn,2018-03-14 20:41:21,2021-09-06 05:23:51.000,2021-09-06 05:23:38,25.0,,253.0,23.0,7.0,16.0,20.0,535.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+111,Geometric GNN Dojo,,educational,MIT,https://github.com/chaitjo/geometric-gnn-dojo/blob/main/geometric_gnn_101.ipynb,"New to geometric GNNs: try our practical notebook, prepared for MPhil students at the University of Cambridge.",12,True,['rep-learn'],chaitjo/geometric-gnn-dojo,https://github.com/chaitjo/geometric-gnn-dojo,2023-01-21 20:08:45,2023-06-18 23:20:44.000,2023-06-18 23:17:32,26.0,,37.0,9.0,5.0,,3.0,354.0,2023-06-18 23:20:44,0.2.0,2.0,3.0,,,,,,,,,,,1.0,,,,,,,True,,,,,,,,,,
+112,AI for Science Resources,,community,GPL-3.0 license,https://github.com/divelab/AIRS/blob/main/Overview/resources.md,"List of resources for AI4Science research, including learning resources.",12,True,,divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2023-11-25 05:19:28.000,2023-11-25 05:19:28,353.0,29.0,37.0,18.0,4.0,,2.0,281.0,,,,24.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+113,QH9: A Quantum Hamiltonian Prediction Benchmark,,datasets,CC-BY-NC-SA 4.0,https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench/QH9,Artificial Intelligence for Science (AIRS).,12,True,['ml-dft'],divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2023-11-25 05:19:28.000,2023-11-25 05:19:28,353.0,29.0,37.0,18.0,4.0,,2.0,281.0,,,,24.0,,,,,,,,,,,2.0,,,,,,,True,,,,,,,,,,
+114,Artificial Intelligence for Science (AIRS),,general-tool,GPL-3.0 license,https://github.com/divelab/AIRS,Artificial Intelligence for Science (AIRS).,12,True,"['rep-learn', 'generative', 'ml-iap', 'md', 'ml-dft', 'ml-wft', 'biomolecules']",divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2023-11-25 05:19:28.000,2023-11-25 05:19:28,353.0,29.0,37.0,18.0,4.0,,2.0,281.0,,,,24.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+115,QHNet,,ml-dft,GPL-3.0,https://github.com/divelab/AIRS/tree/main/OpenDFT/QHNet,Artificial Intelligence for Science (AIRS).,12,True,['rep-learn'],divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2023-11-25 05:19:28.000,2023-11-25 05:19:28,353.0,29.0,37.0,18.0,4.0,,2.0,281.0,,,,24.0,,,,,,,,,,,2.0,,,,,,,True,,,,,,,,,,
+116,TensorMol,,ml-iap,GPL-3.0,https://github.com/jparkhill/TensorMol,Tensorflow + Molecules = TensorMol.,12,False,['single-paper'],jparkhill/TensorMol,https://github.com/jparkhill/TensorMol,2016-10-28 19:40:11,2021-02-11 00:12:00.000,2018-03-30 12:26:14,1724.0,,70.0,45.0,8.0,18.0,19.0,264.0,2017-11-08 18:05:50,0.1,1.0,12.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+117,ANI-1,,ml-iap,MIT,https://github.com/isayev/ASE_ANI,ANI-1 neural net potential with python interface (ASE).,12,False,,isayev/ASE_ANI,https://github.com/isayev/ASE_ANI,2016-12-08 05:09:32,2020-12-14 19:57:50.000,2020-06-05 22:46:43,111.0,,56.0,33.0,9.0,16.0,21.0,209.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+118,ASAP,,unsupervised,MIT,https://github.com/BingqingCheng/ASAP,ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.,12,True,,BingqingCheng/ASAP,https://github.com/BingqingCheng/ASAP,2019-08-11 12:45:14,2023-08-30 13:55:44.000,2023-08-30 13:55:44,759.0,,25.0,7.0,37.0,6.0,18.0,121.0,2023-08-30 13:54:23,1,1.0,6.0,,,5.0,5.0,,,,,,,2.0,,,,,,,,,,,,,,,,,
+119,Librascal,,rep-eng,LGPL-2.1,https://github.com/lab-cosmo/librascal,A scalable and versatile library to generate representations for atomic-scale learning.,12,True,,lab-cosmo/librascal,https://github.com/lab-cosmo/librascal,2018-02-01 08:38:51,2023-11-30 14:48:28.000,2023-11-30 14:48:28,2931.0,1.0,18.0,19.0,200.0,99.0,131.0,74.0,,,3.0,29.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+120,AMPtorch,,general-tool,GPL-3.0,https://github.com/ulissigroup/amptorch,AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch.,12,True,,ulissigroup/amptorch,https://github.com/ulissigroup/amptorch,2019-01-24 15:15:48,2023-07-16 02:11:38.000,2023-07-16 02:08:13,759.0,,32.0,9.0,99.0,5.0,26.0,57.0,2023-07-16 02:11:38,1.0,3.0,14.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+121,Neural fingerprint (nfp),,rep-learn,https://github.com/NREL/nfp/blob/master/LICENSE,https://github.com/NREL/nfp,Keras layers for end-to-end learning with rdkit and pymatgen.,12,False,,NREL/nfp,https://github.com/NREL/nfp,2018-11-20 23:55:23,2022-08-29 21:59:14.000,2022-06-14 22:18:28,143.0,,24.0,7.0,18.0,,6.0,56.0,2022-04-27 17:05:25,0.3.12,13.0,4.0,,,12.0,12.0,,,,,,,2.0,,,,,,,,,,,,,,,,,
+122,OpenMM-ML,,md,MIT,https://github.com/openmm/openmm-ml,High level API for using machine learning models in OpenMM simulations.,12,True,['ml-iap'],openmm/openmm-ml,https://github.com/openmm/openmm-ml,2021-02-10 20:55:25,2023-11-08 14:33:27.000,2023-08-21 20:11:13,28.0,,16.0,14.0,23.0,23.0,20.0,56.0,2023-08-21 23:32:24,1.1,5.0,2.0,,conda-forge/openmm-ml,,,,,165.0,https://anaconda.org/conda-forge/openmm-ml,2023-08-21 23:33:29.497,2655.0,3.0,,,,,,,,,,,,,,,,,
+123,Pacemaker,,ml-iap,https://github.com/ICAMS/python-ace/blob/master/LICENSE.md,https://cortner.github.io/ACEweb/software/,Python package for fitting atomic cluster expansion (ACE) potentials.,12,True,,ICAMS/python-ace,https://github.com/ICAMS/python-ace,2021-11-19 11:39:54,2023-11-30 11:28:12.000,2023-11-18 19:44:01,136.0,14.0,14.0,3.0,23.0,8.0,30.0,51.0,2022-10-24 19:59:33,0.2.8,2.0,5.0,python-ace,,,,https://pypi.org/project/python-ace,7.0,7.0,,,,2.0,,,,,,,,,,,,,,,,,
+124,Grad DFT,,ml-dft,Apache-2.0,https://github.com/XanaduAI/GradDFT,Grad-DFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation..,12,True,,XanaduAI/GradDFT,https://github.com/XanaduAI/GradDFT,2023-05-15 16:18:25,2023-12-03 19:01:38.000,2023-10-30 14:41:37,369.0,195.0,1.0,3.0,42.0,9.0,42.0,43.0,,,,2.0,,,,,,,,,,,2.0,,,,,,,True,,,,,,,,,,
+125,So3krates (MLFF),,ml-iap,MIT,https://github.com/thorben-frank/mlff,Build neural networks for machine learning force fields with JAX.,12,True,,thorben-frank/mlff,https://github.com/thorben-frank/mlff,2022-09-30 07:40:17,2023-11-09 18:38:08.000,2023-11-09 18:38:08,114.0,13.0,7.0,6.0,11.0,3.0,4.0,40.0,2023-07-12 08:34:56,0.2.0,1.0,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+126,Rascaline,,rep-eng,BSD-3-Clause,https://github.com/Luthaf/rascaline,Computing representations for atomistic machine learning.,12,True,"['lang-rust', 'lang-cpp']",Luthaf/rascaline,https://github.com/Luthaf/rascaline,2020-09-24 14:28:34,2023-11-30 15:04:20.000,2023-11-30 15:04:19,493.0,41.0,11.0,6.0,215.0,23.0,23.0,34.0,,,,13.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+127,GlassPy,,rep-eng,GPL-3.0,https://github.com/drcassar/glasspy,Python module for scientists working with glass materials.,12,True,,drcassar/glasspy,https://github.com/drcassar/glasspy,2019-07-18 23:15:43,2023-10-05 19:51:40.000,2023-10-05 19:31:00,324.0,5.0,6.0,5.0,8.0,1.0,4.0,18.0,2023-10-05 19:32:01,0.4.5,7.0,,glasspy,,2.0,2.0,https://pypi.org/project/glasspy,79.0,79.0,,,,2.0,,,,,,,,,,,,,,,,,
+128,Compositionally-Restricted Attention-Based Network (CrabNet),,rep-learn,MIT,https://github.com/sparks-baird/CrabNet,Predict materials properties using only the composition information!.,12,True,,sparks-baird/CrabNet,https://github.com/sparks-baird/CrabNet,2021-09-17 07:58:15,2023-06-19 09:35:52.000,2023-06-19 09:35:52,427.0,,3.0,1.0,54.0,15.0,2.0,11.0,2023-06-07 01:07:33,2.0.8,5.0,5.0,crabnet,,11.0,11.0,https://pypi.org/project/crabnet,198.0,198.0,,,,2.0,,,,,,,,,,,,,,,,,
+129,CCS_fit,,ml-iap,GPL-3.0,https://github.com/Teoroo-CMC/CCS,Curvature Constrained Splines.,12,True,,Teoroo-CMC/CCS,https://github.com/Teoroo-CMC/CCS,2021-12-13 14:29:53,2023-11-20 17:26:52.000,2023-10-04 14:41:11,760.0,3.0,9.0,3.0,12.0,8.0,6.0,6.0,2023-10-04 14:41:14,0.22.4,100.0,8.0,ccs_fit,,,,https://pypi.org/project/ccs_fit,59.0,88.0,,,,2.0,,,,,,381.0,,,,,,,,,,,
+130,OpenChem,,general-tool,MIT,https://github.com/Mariewelt/OpenChem,OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research.,11,False,,Mariewelt/OpenChem,https://github.com/Mariewelt/OpenChem,2018-07-10 01:27:33,2023-11-26 05:03:36.000,2022-04-27 19:27:40,444.0,,106.0,37.0,12.0,15.0,2.0,626.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+131,Deep Learning for Molecules and Materials Book,,educational,https://github.com/whitead/dmol-book/blob/main/LICENSE,https://dmol.pub/,Deep learning for molecules and materials book.,11,True,,whitead/dmol-book,https://github.com/whitead/dmol-book,2020-08-19 19:24:32,2023-07-02 18:02:57.000,2023-07-02 18:02:56,558.0,,101.0,17.0,92.0,27.0,128.0,541.0,,,,19.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,
+132,ReLeaSE,,reinforcement-learning,MIT,https://github.com/isayev/ReLeaSE,Deep Reinforcement Learning for de-novo Drug Design.,11,False,['drug-discovery'],isayev/ReLeaSE,https://github.com/isayev/ReLeaSE,2018-04-26 14:50:34,2021-12-08 19:49:36.000,2021-12-08 19:49:36,160.0,,126.0,19.0,9.0,27.0,8.0,318.0,,,,5.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,
+133,DeepLearningLifeSciences,,educational,MIT,https://github.com/deepchem/DeepLearningLifeSciences,Example code from the book Deep Learning for the Life Sciences.,11,False,,deepchem/DeepLearningLifeSciences,https://github.com/deepchem/DeepLearningLifeSciences,2019-02-05 17:16:18,2021-09-17 05:10:37.000,2021-09-17 05:10:37,52.0,,140.0,23.0,15.0,9.0,10.0,307.0,2019-10-28 18:46:28,1.0,1.0,10.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,
+134,SISSO,,rep-eng,Apache-2.0,https://github.com/rouyang2017/SISSO,A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models.,11,True,['lang-fortran'],rouyang2017/SISSO,https://github.com/rouyang2017/SISSO,2017-10-16 11:31:57,2023-09-12 08:50:38.000,2023-09-12 08:50:38,166.0,2.0,65.0,6.0,2.0,2.0,51.0,193.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+135,jarvis-tools-notebooks,,educational,NIST,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/.,11,True,,JARVIS-Materials-Design/jarvis-tools-notebooks,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,2020-06-27 20:22:02,2023-10-16 02:44:55.000,2023-10-16 02:44:55,542.0,25.0,21.0,4.0,38.0,,,44.0,,,,5.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,
+136,SIMPLE-NN,,ml-iap,GPL-3.0,https://github.com/MDIL-SNU/SIMPLE-NN,SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network).,11,False,,MDIL-SNU/SIMPLE-NN,https://github.com/MDIL-SNU/SIMPLE-NN,2018-03-26 23:53:35,2022-01-27 05:04:05.000,2022-01-27 05:04:05,586.0,,18.0,12.0,91.0,4.0,26.0,44.0,2021-09-23 01:41:42,1.1.1,9.0,4.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+137,nlcc,,language-models,MIT,https://github.com/whitead/nlcc,Natural language computational chemistry command line interface.,11,True,['single-paper'],whitead/nlcc,https://github.com/whitead/nlcc,2021-08-19 18:23:52,2023-02-04 03:07:56.000,2023-02-04 03:06:33,144.0,,6.0,5.0,1.0,,9.0,43.0,2023-02-04 03:07:56,0.6.0,10.0,3.0,nlcc,,,,https://pypi.org/project/nlcc,30.0,30.0,,,,3.0,,,,,,,,,,,,,,,,,
+138,cmlkit,,rep-eng,MIT,https://github.com/sirmarcel/cmlkit,tools for machine learning in condensed matter physics and quantum chemistry.,11,False,['benchmarking'],sirmarcel/cmlkit,https://github.com/sirmarcel/cmlkit,2018-05-31 07:56:52,2022-04-01 00:39:14.000,2022-03-25 22:27:04,526.0,,6.0,3.0,1.0,6.0,2.0,32.0,,,,,cmlkit,,4.0,4.0,https://pypi.org/project/cmlkit,151.0,151.0,,,,2.0,,,,,,,,,,,,,,,,,
+139,synspace,,generative,MIT,https://github.com/whitead/synspace,Synthesis generative model.,11,True,,whitead/synspace,https://github.com/whitead/synspace,2022-12-28 00:59:14,2023-04-15 22:42:57.000,2023-04-15 18:04:16,27.0,,3.0,3.0,1.0,2.0,1.0,31.0,2023-04-15 22:42:57,0.3.0,3.0,2.0,synspace,,6.0,6.0,https://pypi.org/project/synspace,664.0,664.0,,,,2.0,,,,,,,,,,,,,,,,,
+140,PACE,,md,https://github.com/ICAMS/lammps-user-pace/blob/main/LICENSE,https://github.com/ICAMS/lammps-user-pace,"The LAMMPS ML-IAP `pair_style pace`, aka Atomic Cluster Expansion (ACE), aka ML-PACE,..",11,True,,ICAMS/lammps-user-pace,https://github.com/ICAMS/lammps-user-pace,2021-02-25 10:04:48,2023-11-27 21:29:13.000,2023-11-27 21:28:13,59.0,13.0,10.0,5.0,16.0,1.0,5.0,21.0,2023-11-25 21:58:41,.2023.11.25.fix,6.0,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+141,pretrained-gnns,,others,MIT,https://github.com/snap-stanford/pretrain-gnns,Strategies for Pre-training Graph Neural Networks.,10,True,['pre-trained'],snap-stanford/pretrain-gnns,https://github.com/snap-stanford/pretrain-gnns,2020-01-30 22:12:41,2023-07-29 06:21:39.000,2023-07-29 06:21:39,13.0,,153.0,17.0,8.0,30.0,29.0,869.0,,,,2.0,,,,,,,,,,,1.0,,,,,,,True,,,,,,,,,,
+142,EDM,,generative,MIT,https://github.com/ehoogeboom/e3_diffusion_for_molecules,E(3) Equivariant Diffusion Model for Molecule Generation in 3D.,10,False,,ehoogeboom/e3_diffusion_for_molecules,https://github.com/ehoogeboom/e3_diffusion_for_molecules,2022-04-15 14:34:35,2022-07-10 17:56:18.000,2022-07-10 17:56:12,6.0,,82.0,7.0,,1.0,32.0,319.0,,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+143,Awesome Materials Informatics,,community,https://github.com/tilde-lab/awesome-materials-informatics#license,https://github.com/tilde-lab/awesome-materials-informatics,Curated list of known efforts in materials informatics = modern materials science.,10,True,,tilde-lab/awesome-materials-informatics,https://github.com/tilde-lab/awesome-materials-informatics,2018-02-15 15:14:16,2023-10-30 16:02:26.000,2023-10-30 16:02:26,134.0,4.0,74.0,15.0,54.0,,8.0,311.0,2023-03-02 19:56:59,2023.03.02,1.0,19.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+144,GDC,,rep-learn,MIT,https://github.com/gasteigerjo/gdc,"Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019).",10,True,['generative'],gasteigerjo/gdc,https://github.com/gasteigerjo/gdc,2019-10-26 16:05:11,2023-04-26 14:22:40.000,2023-04-26 14:22:40,28.0,,38.0,3.0,1.0,,10.0,229.0,,,,3.0,,,1.0,1.0,,,,,,,2.0,,,,,,,,,,,,,,,,,
+145,Neural Force Field,,ml-iap,MIT,https://github.com/learningmatter-mit/NeuralForceField,Neural Network Force Field based on PyTorch.,10,True,['pre-trained'],learningmatter-mit/NeuralForceField,https://github.com/learningmatter-mit/NeuralForceField,2020-10-04 15:17:41,2023-07-25 15:37:02.000,2023-07-25 15:37:01,122.0,,42.0,7.0,4.0,,16.0,200.0,,,,10.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+146,PiNN,,ml-iap,BSD-3-Clause,https://github.com/Teoroo-CMC/PiNN,A Python library for building atomic neural networks.,10,True,,Teoroo-CMC/PiNN,https://github.com/Teoroo-CMC/PiNN,2019-10-04 08:13:18,2023-11-15 09:09:29.000,2023-09-28 09:19:27,130.0,4.0,27.0,7.0,6.0,1.0,5.0,98.0,2019-10-09 09:21:30,0.3.0,1.0,2.0,,,,,,,4.0,,,,2.0,teoroo/pinn,https://hub.docker.com/r/teoroo/pinn,2023-10-19 13:57:40.768924,,224.0,,,,,,,,,,,,
+147,MolSkill,,language-models,MIT,https://github.com/microsoft/molskill,Extracting medicinal chemistry intuition via preference machine learning.,10,True,"['drug-discovery', 'recommender']",microsoft/molskill,https://github.com/microsoft/molskill,2023-01-12 13:48:31,2023-10-31 17:03:36.000,2023-10-31 17:03:36,81.0,1.0,7.0,8.0,8.0,2.0,3.0,88.0,2023-08-04 12:22:15,1.2b,5.0,4.0,,msr-ai4science/molskill,,,,,14.0,https://anaconda.org/msr-ai4science/molskill,2023-06-18 17:27:43.196,140.0,3.0,,,,,,,,,,,,,,,,,
+148,flare++,,active-learning,MIT,https://github.com/mir-group/flare_pp,A many-body extension of the FLARE code.,10,False,"['lang-cpp', 'ml-iap']",mir-group/flare_pp,https://github.com/mir-group/flare_pp,2019-11-20 22:46:32,2022-02-27 21:05:09.000,2022-02-24 19:00:50,989.0,,6.0,6.0,28.0,8.0,17.0,35.0,,,,10.0,flare_pp,,,,https://pypi.org/project/flare_pp,38.0,38.0,,,,2.0,,,,,,,,,,,,,,,,,
+149,Finetuna,,active-learning,MIT,https://github.com/ulissigroup/finetuna,Active Learning for Machine Learning Potentials.,10,True,,ulissigroup/finetuna,https://github.com/ulissigroup/finetuna,2020-09-22 14:39:52,2023-10-31 18:51:45.000,2023-10-03 18:18:06,1198.0,2.0,10.0,3.0,39.0,4.0,15.0,34.0,,,,11.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+150,DSECOP,,educational,CCO-1.0,https://github.com/GDS-Education-Community-of-Practice/DSECOP,This repository contains data science educational materials developed by DSECOP Fellows.,10,True,,GDS-Education-Community-of-Practice/DSECOP,https://github.com/GDS-Education-Community-of-Practice/DSECOP,2022-03-07 17:47:33,2023-12-03 19:31:17.000,2023-08-29 21:40:29,487.0,,23.0,10.0,17.0,1.0,7.0,31.0,,,,13.0,,,,,,,,,,,2.0,,,,,,,True,,,,,,,,,,
+151,SchNetPack G-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/schnetpack-gschnet,G-SchNet extension for SchNetPack.,10,True,,atomistic-machine-learning/schnetpack-gschnet,https://github.com/atomistic-machine-learning/schnetpack-gschnet,2022-04-21 12:34:13,2023-11-07 11:31:47.000,2023-11-07 11:31:47,117.0,1.0,6.0,4.0,,1.0,9.0,31.0,2023-04-25 14:09:07,1.0.0,2.0,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+152,NeuralXC,,ml-dft,BSD-3-Clause,https://github.com/semodi/neuralxc,Implementation of a machine learned density functional.,10,False,,semodi/neuralxc,https://github.com/semodi/neuralxc,2019-03-14 18:13:40,2022-11-30 11:39:22.000,2021-07-05 21:36:23,337.0,,9.0,5.0,9.0,5.0,5.0,30.0,,,3.0,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+153,OpenKIM,,datasets,LGPL-2.1,https://openkim.org/,"The Open Knowledgebase of Interatomic Models (OpenKIM) aims to be an online resource for standardized testing, long-..",10,False,"['knowledge-base', 'pre-trained']",openkim/kim-api,https://github.com/openkim/kim-api,2014-07-28 21:21:08,2023-08-16 00:09:44.000,2022-03-17 23:01:36,2371.0,,19.0,11.0,55.0,17.0,18.0,29.0,,,,23.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+154,pair_nequip,,md,MIT,https://github.com/mir-group/pair_nequip,LAMMPS pair style for NequIP.,10,False,"['ml-iap', 'rep-learn']",mir-group/pair_nequip,https://github.com/mir-group/pair_nequip,2021-04-02 15:28:02,2023-09-25 06:11:31.000,2022-06-20 22:10:11,89.0,,12.0,8.0,7.0,8.0,13.0,29.0,2022-05-20 00:39:04,0.5.2,4.0,3.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+155,NNsforMD,,ml-iap,MIT,https://github.com/aimat-lab/NNsForMD,"Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings.",10,False,,aimat-lab/NNsForMD,https://github.com/aimat-lab/NNsForMD,2020-08-31 11:14:18,2022-11-10 13:04:49.000,2022-11-10 13:04:45,265.0,,3.0,3.0,,,,10.0,2022-04-12 15:10:32,2.0.0,5.0,2.0,pyNNsMD,,,,https://pypi.org/project/pyNNsMD,39.0,39.0,,,,2.0,,,,,,,,,,,,,,,,,
+156,ACEfit,,ml-iap,MIT,https://github.com/ACEsuit/ACEfit.jl,,10,True,['lang-julia'],ACEsuit/ACEfit.jl,https://github.com/ACEsuit/ACEfit.jl,2022-01-01 00:09:17,2023-11-03 18:46:32.000,2023-08-18 16:46:11,210.0,,3.0,4.0,18.0,22.0,32.0,5.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+157,SE(3)-Transformers,,rep-learn,MIT,https://github.com/FabianFuchsML/se3-transformer-public,code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503.,9,False,"['single-paper', 'transformer']",FabianFuchsML/se3-transformer-public,https://github.com/FabianFuchsML/se3-transformer-public,2020-08-31 10:36:57,2023-07-10 05:13:25.000,2021-11-18 09:11:56,63.0,,66.0,16.0,5.0,9.0,17.0,427.0,,,,2.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+158,molecularGNN_smiles,,rep-learn,Apache-2.0,https://github.com/masashitsubaki/molecularGNN_smiles,"The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius..",9,False,,masashitsubaki/molecularGNN_smiles,https://github.com/masashitsubaki/molecularGNN_smiles,2018-11-06 00:25:26,2020-11-28 02:04:45.000,2020-11-28 02:04:45,79.0,,70.0,6.0,,6.0,1.0,255.0,,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+159,DimeNet,,ml-iap,https://github.com/gasteigerjo/dimenet/blob/master/LICENSE.md,https://github.com/gasteigerjo/dimenet,"DimeNet and DimeNet++ models, as proposed in Directional Message Passing for Molecular Graphs (ICLR 2020) and Fast and..",9,True,,gasteigerjo/dimenet,https://github.com/gasteigerjo/dimenet,2020-02-14 12:40:15,2023-10-03 09:57:19.000,2023-10-03 09:57:19,103.0,1.0,56.0,5.0,,1.0,29.0,253.0,,,,2.0,,,1.0,1.0,,,,,,,3.0,,,,,,,,,,,,,,,,,
+160,Allegro,,ml-iap,MIT,https://github.com/mir-group/allegro,Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic..,9,True,,mir-group/allegro,https://github.com/mir-group/allegro,2022-02-06 23:50:40,2023-05-08 21:16:45.000,2023-05-08 21:16:45,38.0,,36.0,18.0,2.0,8.0,13.0,247.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+161,SchNet,,ml-iap,MIT,https://github.com/atomistic-machine-learning/SchNet,SchNet - a deep learning architecture for quantum chemistry.,9,False,,atomistic-machine-learning/SchNet,https://github.com/atomistic-machine-learning/SchNet,2017-10-03 11:52:20,2018-09-04 08:42:35.000,2018-09-04 08:42:34,53.0,,58.0,16.0,,1.0,2.0,189.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+162,QDF for molecule,,ml-esm,MIT,https://github.com/masashitsubaki/QuantumDeepField_molecule,"Quantum deep field: data-driven wave function, electron density generation, and energy prediction and extrapolation..",9,False,,masashitsubaki/QuantumDeepField_molecule,https://github.com/masashitsubaki/QuantumDeepField_molecule,2020-11-11 01:06:09,2021-02-20 03:46:18.000,2021-02-20 03:46:09,20.0,,37.0,4.0,,,3.0,176.0,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,
+163,GemNet,,ml-iap,https://github.com/TUM-DAML/gemnet_pytorch/blob/master/LICENSE,https://github.com/TUM-DAML/gemnet_pytorch,"GemNet model in PyTorch, as proposed in GemNet: Universal Directional Graph Neural Networks for Molecules (NeurIPS..",9,True,,TUM-DAML/gemnet_pytorch,https://github.com/TUM-DAML/gemnet_pytorch,2021-10-11 07:30:36,2023-04-26 14:20:12.000,2023-04-26 14:20:12,36.0,,25.0,4.0,1.0,,14.0,159.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+164,DeePKS-kit,,ml-dft,LGPL-3.0,https://github.com/deepmodeling/deepks-kit,a package for developing machine learning-based chemically accurate energy and density functional models.,9,True,,deepmodeling/deepks-kit,https://github.com/deepmodeling/deepks-kit,2020-07-29 03:27:50,2023-08-24 00:29:24.000,2023-04-01 01:14:46,380.0,,31.0,13.0,39.0,2.0,9.0,95.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+165,tinker-hp,,ml-iap,https://github.com/TinkerTools/tinker-hp/blob/master/license-Tinker.pdf,https://github.com/TinkerTools/tinker-hp,Tinker-HP: High-Performance Massively Parallel Evolution of Tinker on CPUs & GPUs.,9,True,,TinkerTools/tinker-hp,https://github.com/TinkerTools/tinker-hp,2018-06-12 12:15:51,2023-10-19 11:57:35.000,2023-10-19 11:55:48,528.0,7.0,17.0,13.0,2.0,3.0,15.0,69.0,2019-11-24 16:21:50,published-version-V1,1.0,10.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+166,ACE.jl,,ml-iap,https://github.com/ACEsuit/ACE.jl/blob/main/license/mit.md,https://github.com/ACEsuit/ACE.jl,Parameterisation of Equivariant Properties of Particle Systems.,9,True,['lang-julia'],ACEsuit/ACE.jl,https://github.com/ACEsuit/ACE.jl,2019-11-30 16:22:51,2023-06-09 21:31:30.000,2023-06-09 21:29:10,912.0,,15.0,8.0,65.0,24.0,58.0,62.0,,,,12.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+167,GATGNN: Global Attention Graph Neural Network,,rep-learn,MIT,https://github.com/superlouis/GATGNN,Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials..,9,False,,superlouis/GATGNN,https://github.com/superlouis/GATGNN,2020-06-21 03:27:36,2022-10-03 21:57:33.000,2022-10-03 21:57:33,99.0,,18.0,8.0,,3.0,3.0,61.0,2021-04-05 06:49:29,0.2,2.0,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+168,PROPhet,,ml-dft,GPL-3.0,https://github.com/biklooost/PROPhet,PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches.,9,False,"['ml-iap', 'md', 'single-paper', 'lang-cpp']",biklooost/PROPhet,https://github.com/biklooost/PROPhet,2016-09-16 16:21:06,2018-04-19 02:09:46.000,2018-04-19 02:00:46,120.0,,26.0,14.0,6.0,8.0,7.0,61.0,2018-04-15 16:55:15,1.2,3.0,4.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+169,hippynn,,rep-learn,https://github.com/lanl/hippynn/blob/main/LICENSE.txt,https://github.com/lanl/hippynn,python library for atomistic machine learning.,9,True,['workflows'],lanl/hippynn,https://github.com/lanl/hippynn,2021-11-17 00:45:13,2023-11-22 21:14:51.000,2023-11-15 23:40:55,109.0,8.0,19.0,7.0,42.0,2.0,4.0,48.0,,,2.0,11.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+170,DeepErwin,,ml-wft,https://github.com/mdsunivie/deeperwin/blob/master/LICENSE,https://github.com/mdsunivie/deeperwin,DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions..,9,True,,mdsunivie/deeperwin,https://github.com/mdsunivie/deeperwin,2021-06-14 15:18:32,2023-10-16 07:46:05.000,2023-10-16 07:46:05,57.0,2.0,5.0,3.0,3.0,,11.0,36.0,2022-07-18 10:18:25,arxiv_2105.08351v2,2.0,6.0,deeperwin,,,,https://pypi.org/project/deeperwin,49.0,49.0,,,,3.0,,,,,,,,,,,,,,,,,
+171,iam-notebooks,,educational,Apache-2.0,https://github.com/ceriottm/iam-notebooks,Jupyter notebooks for the lectures of the Introduction to Atomistic Modeling.,9,True,,ceriottm/iam-notebooks,https://github.com/ceriottm/iam-notebooks,2020-11-23 21:27:41,2023-08-07 23:02:34.000,2023-08-07 23:02:34,228.0,,4.0,4.0,7.0,3.0,,22.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+172,lie-nn,,math,MIT,https://github.com/lie-nn/lie-nn,Tools for building equivariant polynomials on reductive Lie groups.,9,True,['rep-learn'],lie-nn/lie-nn,https://github.com/lie-nn/lie-nn,2022-04-01 18:02:49,2023-06-29 19:38:34.000,2023-06-20 22:30:53,249.0,,1.0,8.0,3.0,,,22.0,2023-06-20 22:31:12,0.0.0,1.0,3.0,,,,,,,,,,,2.0,,,,,,,True,,,,,,,,,,
+173,ACE1.jl,,ml-iap,https://github.com/ACEsuit/ACE1.jl/blob/main/ASL.md,https://acesuit.github.io/,Atomic Cluster Expansion for Modelling Invariant Atomic Properties.,9,True,['lang-julia'],ACEsuit/ACE1.jl,https://github.com/ACEsuit/ACE1.jl,2022-01-14 19:52:49,2023-11-13 19:43:52.000,2023-11-13 19:43:52,551.0,5.0,4.0,5.0,28.0,22.0,24.0,19.0,,,,7.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+174,wfl,,ml-iap,,https://github.com/libAtoms/workflow,Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows.,9,True,"['workflows', 'htc']",libAtoms/workflow,https://github.com/libAtoms/workflow,2021-11-04 17:03:34,2023-12-01 16:27:05.000,2023-10-30 13:46:29,939.0,20.0,14.0,9.0,142.0,62.0,68.0,18.0,,,,14.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+175,BenchML,,rep-eng,Apache-2.0,https://github.com/capoe/benchml,ML benchmarking and pipeling framework.,9,True,['benchmarking'],capoe/benchml,https://github.com/capoe/benchml,2020-04-28 13:26:29,2023-05-24 15:13:06.000,2023-05-24 15:04:57,341.0,,3.0,5.0,7.0,3.0,10.0,14.0,,,,9.0,benchml,,,,https://pypi.org/project/benchml,50.0,50.0,,,,3.0,,,,,,,,,,,,,,,,,
+176,TurboGAP,,ml-iap,https://github.com/mcaroba/turbogap/blob/master/LICENSE.md,https://github.com/mcaroba/turbogap,The TurboGAP code.,9,True,['lang-fortran'],mcaroba/turbogap,https://github.com/mcaroba/turbogap,2021-05-02 09:19:05,2023-12-01 14:54:42.000,2023-11-18 06:59:52,234.0,18.0,8.0,7.0,6.0,4.0,3.0,14.0,,,,8.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+177,RDKit Tutorials,,educational,https://github.com/rdkit/rdkit-tutorials/blob/master/LICENSE,https://github.com/rdkit/rdkit-tutorials,Tutorials to learn how to work with the RDKit.,8,True,,rdkit/rdkit-tutorials,https://github.com/rdkit/rdkit-tutorials,2016-10-07 03:34:01,2023-03-19 13:36:55.000,2023-03-19 13:36:55,68.0,,67.0,16.0,7.0,3.0,1.0,215.0,,,,5.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+178,MoLFormers UI,,community,Apache-2.0,https://molformer.res.ibm.com/,Repository for MolFormer.,8,True,"['transformer', 'language-models', 'pre-trained', 'drug-discovery']",IBM/molformer,https://github.com/IBM/molformer,2022-11-07 18:48:17,2023-10-16 16:34:25.000,2023-10-16 16:33:13,7.0,1.0,29.0,9.0,3.0,6.0,9.0,145.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+179,MoLFormer,,language-models,Apache-2.0,https://github.com/IBM/molformer,Repository for MolFormer.,8,True,"['transformer', 'pre-trained', 'drug-discovery']",IBM/molformer,https://github.com/IBM/molformer,2022-11-07 18:48:17,2023-10-16 16:34:25.000,2023-10-16 16:33:13,7.0,1.0,29.0,9.0,3.0,6.0,9.0,145.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+180,G-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/G-SchNet,G-SchNet - a generative model for 3d molecular structures.,8,True,,atomistic-machine-learning/G-SchNet,https://github.com/atomistic-machine-learning/G-SchNet,2019-10-21 13:48:59,2023-03-24 12:05:41.000,2023-03-24 12:05:41,64.0,,23.0,6.0,,,10.0,121.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+181,ANI-1 Dataset,,datasets,MIT,https://github.com/isayev/ANI1_dataset,A data set of 20 million calculated off-equilibrium conformations for organic molecules.,8,False,,isayev/ANI1_dataset,https://github.com/isayev/ANI1_dataset,2017-08-07 20:08:46,2022-08-08 15:56:17.000,2022-08-08 15:56:17,25.0,,19.0,12.0,2.0,6.0,3.0,91.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+182,MoleculeNet Leaderboard,,datasets,MIT,https://github.com/deepchem/moleculenet,,8,False,['benchmarking'],deepchem/moleculenet,https://github.com/deepchem/moleculenet,2020-02-24 18:14:05,2021-04-29 19:51:06.000,2021-04-29 19:51:06,78.0,,19.0,5.0,15.0,23.0,5.0,77.0,,,,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+183,cG-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/cG-SchNet,cG-SchNet - a conditional generative neural network for 3d molecular structures.,8,True,,atomistic-machine-learning/cG-SchNet,https://github.com/atomistic-machine-learning/cG-SchNet,2021-12-02 15:35:18,2023-03-24 12:09:56.000,2023-03-24 12:09:56,28.0,,14.0,4.0,,,3.0,43.0,2022-02-21 13:36:41,1.0,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+184,Sketchmap,,unsupervised,GPL-3.0,https://github.com/lab-cosmo/sketchmap,Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular.,8,True,['lang-cpp'],lab-cosmo/sketchmap,https://github.com/lab-cosmo/sketchmap,2014-05-20 09:33:32,2023-05-24 22:56:06.000,2023-05-24 22:47:50,64.0,,10.0,29.0,1.0,3.0,5.0,40.0,,,,8.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+185,DeeperGATGNN,,rep-learn,MIT,https://github.com/usccolumbia/deeperGATGNN,Scalable graph neural networks for materials property prediction.,8,True,,usccolumbia/deeperGATGNN,https://github.com/usccolumbia/deeperGATGNN,2021-09-29 17:31:02,2023-04-19 18:59:51.000,2023-04-19 18:59:31,24.0,,7.0,3.0,1.0,,7.0,35.0,2022-03-08 02:14:28,1.0,1.0,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+186,GAP,,ml-iap,https://github.com/libAtoms/GAP/blob/main/LICENSE.md,https://libatoms.github.io/,Gaussian Approximation Potential (GAP).,8,True,,libAtoms/GAP,https://github.com/libAtoms/GAP,2021-03-22 14:48:56,2023-10-15 17:47:46.000,2023-10-15 17:42:33,200.0,1.0,19.0,10.0,63.0,,,34.0,,,,12.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+187,SNAP,,ml-iap,BSD-3-Clause,https://github.com/materialsvirtuallab/snap,Repository for spectral neighbor analysis potential (SNAP) model development.,8,False,,materialsvirtuallab/snap,https://github.com/materialsvirtuallab/snap,2017-06-26 21:56:00,2020-06-30 05:20:37.000,2020-06-30 05:20:37,38.0,,16.0,10.0,1.0,1.0,3.0,32.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+188,SIMPLE-NN v2,,ml-iap,GPL-3.0,https://github.com/MDIL-SNU/SIMPLE-NN_v2,,8,False,,MDIL-SNU/SIMPLE-NN_v2,https://github.com/MDIL-SNU/SIMPLE-NN_v2,2021-03-02 09:36:49,2023-10-11 05:12:51.000,2023-10-11 05:12:51,502.0,4.0,16.0,5.0,87.0,3.0,7.0,31.0,,,,12.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+189,pair_allegro,,md,MIT,https://github.com/mir-group/pair_allegro,LAMMPS pair style for Allegro deep learning interatomic potentials with parallelization support.,8,True,"['ml-iap', 'rep-learn']",mir-group/pair_allegro,https://github.com/mir-group/pair_allegro,2021-08-09 17:26:51,2023-10-27 17:27:05.000,2023-06-27 14:16:21,52.0,,6.0,10.0,2.0,4.0,16.0,26.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+190,Atomistic Adversarial Attacks,,ml-iap,MIT,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,Code for performing adversarial attacks on atomistic systems using NN potentials.,8,False,['probabilistic'],learningmatter-mit/Atomistic-Adversarial-Attacks,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,2021-03-28 17:39:52,2022-10-03 16:19:31.000,2022-10-03 16:19:29,33.0,,6.0,5.0,1.0,,1.0,25.0,2021-07-19 18:09:36,1.0.1,1.0,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+191,CGAT,,rep-learn,MIT,https://github.com/hyllios/CGAT,Crystal graph attention neural networks for materials prediction.,8,True,,hyllios/CGAT,https://github.com/hyllios/CGAT,2021-03-28 09:51:15,2023-07-18 12:04:35.000,2023-01-10 22:31:07,153.0,,7.0,3.0,1.0,,1.0,17.0,2023-07-18 12:04:35,0.1,1.0,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+192,GElib,,math,MPL-2.0,https://github.com/risi-kondor/GElib,C++/CUDA library for SO(3) equivariant operations.,8,True,['lang-cpp'],risi-kondor/GElib,https://github.com/risi-kondor/GElib,2021-08-24 20:56:40,2023-11-20 05:48:53.000,2023-10-25 18:07:13,597.0,17.0,2.0,2.0,2.0,2.0,3.0,16.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+193,SALTED,,ml-dft,GPL-3.0,https://github.com/andreagrisafi/SALTED,Symmetry-Adapted Learning of Three-dimensional Electron Densities.,8,True,,andreagrisafi/SALTED,https://github.com/andreagrisafi/SALTED,2020-01-22 10:24:29,2023-11-30 15:42:02.000,2023-11-30 15:42:02,242.0,74.0,2.0,1.0,10.0,,2.0,14.0,2023-04-10 16:25:44,2.0.0,1.0,13.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+194,UVVisML,,rep-learn,MIT,https://github.com/learningmatter-mit/uvvisml,Predict optical properties of molecules with machine learning.,8,True,"['optical-properties', 'single-paper', 'probabilistic']",learningmatter-mit/uvvisml,https://github.com/learningmatter-mit/uvvisml,2021-10-13 05:58:48,2023-05-26 22:35:14.000,2023-05-26 22:35:14,17.0,,5.0,4.0,1.0,,,14.0,2022-02-06 18:14:14,0.0.2,2.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+195,chemlift,,language-models,MIT,https://github.com/lamalab-org/chemlift,Language-interfaced fine-tuning for chemistry.,8,True,,lamalab-org/chemlift,https://github.com/lamalab-org/chemlift,2023-07-10 06:54:07,2023-11-30 10:47:50.000,2023-10-14 16:50:14,36.0,36.0,1.0,1.0,1.0,10.0,7.0,10.0,2023-11-30 19:42:07,0.0.1,1.0,,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+196,T-e3nn,,rep-learn,MIT,https://github.com/Hongyu-yu/T-e3nn,Time-reversal Euclidean neural networks based on e3nn.,8,True,['magnetism'],Hongyu-yu/T-e3nn,https://github.com/Hongyu-yu/T-e3nn,2022-11-21 14:49:45,2023-02-21 16:36:26.000,2023-02-21 16:36:25,2145.0,,,2.0,,,,6.0,,,,26.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+197,Equisolve,,general-tool,BSD-3-Clause,https://github.com/lab-cosmo/equisolve,A ML toolkit package utilizing the metatensor data format to build models for the prediction of equivariant properties..,8,False,['ml-iap'],lab-cosmo/equisolve,https://github.com/lab-cosmo/equisolve,2022-10-04 15:29:19,2023-10-27 10:03:59.000,2023-10-27 09:55:17,55.0,18.0,2.0,14.0,43.0,19.0,4.0,4.0,,,,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+198,MAChINE,,educational,MIT,https://github.com/aimat-lab/MAChINE,Client-Server Web App to introduce usage of ML in materials science to beginners.,8,False,,aimat-lab/MAChINE,https://github.com/aimat-lab/MAChINE,2023-04-17 14:29:06,2023-09-29 14:20:12.000,2023-09-29 10:20:31,1026.0,20.0,,,7.0,9.0,23.0,1.0,,,,7.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+199,Awesome Neural Geometry,,community,,https://github.com/neurreps/awesome-neural-geometry,"A curated collection of resources and research related to the geometry of representations in the brain, deep networks,..",7,True,"['educational', 'rep-learn']",neurreps/awesome-neural-geometry,https://github.com/neurreps/awesome-neural-geometry,2022-07-31 01:19:57,2023-09-21 00:09:30.000,2023-09-21 00:09:30,118.0,2.0,46.0,26.0,11.0,,1.0,776.0,,,,10.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+200,Equiformer,,rep-learn,MIT,https://github.com/atomicarchitects/equiformer,[ICLR23 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs.,7,True,['transformer'],atomicarchitects/equiformer,https://github.com/atomicarchitects/equiformer,2023-02-28 00:21:30,2023-06-21 08:04:30.000,2023-06-21 08:03:53,3.0,,29.0,5.0,1.0,5.0,7.0,143.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+201,BestPractices,,educational,MIT,https://github.com/anthony-wang/BestPractices,Things that you should (and should not) do in your Materials Informatics research.,7,True,,anthony-wang/BestPractices,https://github.com/anthony-wang/BestPractices,2020-05-05 19:41:25,2023-11-17 02:58:25.000,2023-11-17 02:58:25,17.0,2.0,66.0,7.0,8.0,5.0,2.0,141.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+202,A Highly Opinionated List of Open-Source Materials Informatics Resources,,community,MIT,https://github.com/ncfrey/resources,A Highly Opinionated List of Open Source Materials Informatics Resources.,7,False,,ncfrey/resources,https://github.com/ncfrey/resources,2020-11-17 23:47:07,2022-02-18 13:37:51.000,2022-02-18 13:37:51,8.0,,19.0,9.0,,,,96.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+203,PhysNet,,ml-iap,MIT,https://github.com/MMunibas/PhysNet,Code for training PhysNet models.,7,False,['electrostatics'],MMunibas/PhysNet,https://github.com/MMunibas/PhysNet,2019-03-28 09:05:22,2022-10-16 17:45:42.000,2020-12-07 11:09:20,4.0,,26.0,9.0,1.0,5.0,,85.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+204,AIMNet,,ml-iap,MIT,https://github.com/aiqm/aimnet,Atoms In Molecules Neural Network Potential.,7,False,['single-paper'],aiqm/aimnet,https://github.com/aiqm/aimnet,2018-09-26 17:28:37,2019-11-21 23:49:01.000,2019-11-21 23:49:00,7.0,,20.0,10.0,2.0,4.0,,79.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+205,DTNN,,rep-learn,MIT,https://github.com/atomistic-machine-learning/dtnn,Deep Tensor Neural Network.,7,False,,atomistic-machine-learning/dtnn,https://github.com/atomistic-machine-learning/dtnn,2017-03-10 14:40:05,2017-07-11 08:26:15.000,2017-07-11 08:25:39,9.0,,30.0,15.0,,,3.0,76.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+206,JAXChem,,general-tool,MIT,https://github.com/deepchem/jaxchem,JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling.,7,False,,deepchem/jaxchem,https://github.com/deepchem/jaxchem,2020-05-11 18:54:41,2020-07-15 05:02:21.000,2020-07-15 04:55:41,96.0,,9.0,7.0,13.0,1.0,1.0,74.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+207,Cormorant,,rep-learn,https://github.com/risilab/cormorant/blob/master/LICENSE,https://github.com/risilab/cormorant,Codebase for Cormorant Neural Networks.,7,False,,risilab/cormorant,https://github.com/risilab/cormorant,2019-10-27 18:22:07,2022-05-11 12:49:05.000,2020-03-11 15:25:51,160.0,,10.0,6.0,1.0,3.0,,55.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+208,uncertainty_benchmarking,,general-tool,,https://github.com/ulissigroup/uncertainty_benchmarking,Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions.,7,False,"['benchmarking', 'probabilistic']",ulissigroup/uncertainty_benchmarking,https://github.com/ulissigroup/uncertainty_benchmarking,2019-08-28 19:39:28,2021-06-07 23:29:39.000,2021-06-07 23:27:19,265.0,,6.0,6.0,1.0,,,35.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+209,torchchem,,general-tool,MIT,https://github.com/deepchem/torchchem,An experimental repo for experimenting with PyTorch models.,7,False,,deepchem/torchchem,https://github.com/deepchem/torchchem,2020-03-07 17:06:44,2023-03-24 23:13:19.000,2020-05-01 20:12:23,49.0,,14.0,8.0,27.0,2.0,1.0,34.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+210,graphite,,rep-learn,MIT,https://github.com/LLNL/graphite,A repository for implementing graph network models based on atomic structures.,7,True,,llnl/graphite,https://github.com/LLNL/graphite,2022-06-27 19:15:27,2023-10-04 21:51:25.000,2023-10-04 21:51:05,23.0,1.0,6.0,5.0,4.0,2.0,1.0,29.0,,,,2.0,,,7.0,7.0,,,,,,,3.0,,,,,,,,,,,,,,,,,
+211,AdsorbML,,rep-learn,MIT,https://github.com/Open-Catalyst-Project/AdsorbML,,7,True,"['surface-science', 'single-paper']",Open-Catalyst-Project/AdsorbML,https://github.com/Open-Catalyst-Project/AdsorbML,2022-11-30 01:38:20,2023-07-31 16:28:14.000,2023-07-31 16:28:09,56.0,,5.0,6.0,10.0,,1.0,25.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+212,SkipAtom,,rep-eng,MIT,https://github.com/lantunes/skipatom,"Distributed representations of atoms, inspired by the Skip-gram model.",7,False,,lantunes/skipatom,https://github.com/lantunes/skipatom,2021-06-19 13:09:13,2023-07-16 19:28:39.000,2022-05-04 13:18:30,46.0,,3.0,2.0,7.0,3.0,1.0,22.0,,,1.0,,skipatom,conda-forge/skipatom,,,https://pypi.org/project/skipatom,28.0,98.0,https://anaconda.org/conda-forge/skipatom,2023-06-18 08:42:05.505,1194.0,3.0,,,,,,,,,,,,,,,,,
+213,escnn_jax,,rep-learn,https://github.com/emilemathieu/escnn_jax/blob/master/LICENSE,https://github.com/emilemathieu/escnn_jax,Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/.,7,True,,emilemathieu/escnn_jax,https://github.com/emilemathieu/escnn_jax,2023-06-15 09:45:45,2023-06-28 14:40:32.000,2023-06-28 14:39:56,203.0,,2.0,,,,,21.0,,,,8.0,escnn_jax,,,,https://pypi.org/project/escnn_jax,,,,,,3.0,,,,,,,,,,,,,,,,,
+214,BOSS,,materials-discovery,,https://gitlab.com/cest-group/boss,Bayesian Optimization Structure Search (BOSS).,7,False,['probabilistic'],,,2020-02-12 08:48:33,2020-02-12 08:48:33.000,,,,10.0,,,2.0,24.0,18.0,,,14.0,,aalto-boss,,,,https://pypi.org/project/aalto-boss,233.0,233.0,,,,2.0,,,,,,,,,,,,,,cest-group/boss,https://gitlab.com/cest-group/boss,,
+215,CBFV,,rep-eng,,https://github.com/Kaaiian/CBFV,Tool to quickly create a composition-based feature vector.,7,False,,kaaiian/CBFV,https://github.com/Kaaiian/CBFV,2019-09-05 23:07:46,2022-03-30 05:47:53.000,2021-10-24 17:10:17,49.0,,6.0,4.0,7.0,5.0,5.0,15.0,,,,3.0,CBFV,,4.0,4.0,https://pypi.org/project/CBFV,129.0,129.0,,,,3.0,,,,,,,,,,,,,,,,,
+216,Libnxc,,ml-dft,MPL-2.0,https://github.com/semodi/libnxc,A library for using machine-learned exchange-correlation functionals for density-functional theory.,7,False,"['lang-cpp', 'lang-fortran']",semodi/libnxc/,https://github.com/semodi/libnxc,2020-07-01 18:21:50,2021-09-18 14:53:52.000,2021-08-14 16:26:32,100.0,,4.0,2.0,3.0,13.0,3.0,15.0,,,2.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+217,ACEhamiltonians,,ml-dft,MIT,https://github.com/ACEsuit/ACEhamiltonians.jl,"Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-..",7,True,['lang-julia'],ACEsuit/ACEhamiltonians.jl,https://github.com/ACEsuit/ACEhamiltonians.jl,2022-01-17 20:54:22,2023-04-12 15:11:09.000,2023-04-12 15:04:14,33.0,,3.0,4.0,41.0,1.0,3.0,9.0,2022-05-20 17:07:42,arXiv.2111.13736,1.0,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+218,bVAE-IM,,generative,MIT,https://github.com/tsudalab/bVAE-IM,Implementation of Chemical Design with GPU-based Ising Machine.,7,True,"['qml', 'single-paper']",tsudalab/bVAE-IM,https://github.com/tsudalab/bVAE-IM,2023-03-01 08:26:56,2023-07-11 04:39:24.000,2023-07-11 04:39:24,39.0,,2.0,8.0,,,,9.0,2023-03-01 14:26:13,1.0.0,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+219,COSMO Software Cookbook,,educational,BSD-3-Clause,https://github.com/lab-cosmo/software-cookbook,The COSMO cookbook contains recipes for atomic-scale modelling for materials and molecules.,7,False,,lab-cosmo/software-cookbook,https://github.com/lab-cosmo/software-cookbook,2023-05-23 10:33:47,2023-10-27 06:42:00.000,2023-10-27 06:30:34,35.0,12.0,1.0,13.0,27.0,2.0,1.0,3.0,,,,5.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+220,MEGAN: Multi Explanation Graph Attention Student,,xai,MIT,https://github.com/aimat-lab/graph_attention_student,Minimal implementation of graph attention student model architecture.,7,False,,aimat-lab/graph_attention_student,https://github.com/aimat-lab/graph_attention_student,2022-07-28 06:22:50,2023-11-20 09:38:47.000,2023-11-20 09:38:42,34.0,11.0,1.0,3.0,1.0,,,3.0,,,,2.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+221,MEGAN,,xai,MIT,https://github.com/aimat-lab/graph_attention_student,Minimal implementation of graph attention student model architecture.,7,False,"['xai', 'rep-learn']",aimat-lab/graph_attention_student,https://github.com/aimat-lab/graph_attention_student,2022-07-28 06:22:50,2023-11-20 09:38:47.000,2023-11-20 09:38:42,34.0,11.0,1.0,3.0,1.0,,,3.0,,,,2.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+222,2DMD dataset,,datasets,Apache-2.0,https://github.com/HSE-LAMBDA/ai4material_design/blob/main/docs/DATA.md,"Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of..",7,False,['material-defect'],HSE-LAMBDA/ai4material_design,https://github.com/HSE-LAMBDA/ai4material_design,2021-03-25 10:06:20,2023-11-21 11:30:42.000,2023-11-21 11:30:33,1118.0,2.0,1.0,7.0,28.0,,12.0,2.0,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+223,ai4material_design,,rep-learn,Apache-2.0,https://github.com/HSE-LAMBDA/ai4material_design,"Code for Kazeev, N., Al-Maeeni, A.R., Romanov, I. et al. Sparse representation for machine learning the properties of..",7,False,"['pre-trained', 'material-defect']",HSE-LAMBDA/ai4material_design,https://github.com/HSE-LAMBDA/ai4material_design,2021-03-25 10:06:20,2023-11-21 11:30:42.000,2023-11-21 11:30:33,1118.0,2.0,1.0,7.0,28.0,,12.0,2.0,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+224,ML4pXRDs,,rep-learn,MIT,https://github.com/aimat-lab/ML4pXRDs,Contains code to train neural networks based on simulated powder XRDs from synthetic crystals.,7,True,"['xrd', 'single-paper']",aimat-lab/ML4pXRDs,https://github.com/aimat-lab/ML4pXRDs,2022-12-01 16:24:29,2023-07-14 08:17:06.000,2023-07-14 08:17:04,1320.0,,,3.0,,,,,2023-03-22 11:04:31,1.0,1.0,,,,,,,,0.0,,,,3.0,,,,,,2.0,,,,,,,,,,,
+225,The Collection of Database and Dataset Resources in Materials Science,,community,,https://github.com/sedaoturak/data-resources-for-materials-science,"A list of databases, datasets and books/handbooks where you can find materials properties for machine learning..",6,True,['datasets'],sedaoturak/data-resources-for-materials-science,https://github.com/sedaoturak/data-resources-for-materials-science,2021-02-20 06:38:45,2023-11-03 12:16:07.000,2023-11-03 12:16:07,28.0,2.0,28.0,10.0,1.0,,1.0,187.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+226,GEOM,,datasets,,https://github.com/learningmatter-mit/geom,GEOM: Energy-annotated molecular conformations.,6,False,['drug-discovery'],learningmatter-mit/geom,https://github.com/learningmatter-mit/geom,2020-06-03 17:58:37,2022-04-24 18:57:39.000,2022-04-24 18:57:39,95.0,,18.0,9.0,,1.0,10.0,154.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+227,EquiformerV2,,rep-learn,MIT,https://github.com/atomicarchitects/equiformer_v2,[arXiv23] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations.,6,True,,atomicarchitects/equiformer_v2,https://github.com/atomicarchitects/equiformer_v2,2023-06-21 07:09:58,2023-12-02 16:53:28.000,2023-12-02 16:53:11,9.0,1.0,14.0,4.0,,7.0,,90.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+228,COATI,,generative,Apache 2.0,https://github.com/terraytherapeutics/COATI,COATI: multi-modal contrastive pre-training for representing and traversing chemical space.,6,True,"['drug-discovery', 'pre-trained', 'rep-learn']",terraytherapeutics/COATI,https://github.com/terraytherapeutics/COATI,2023-08-11 14:56:39,2023-10-27 16:55:39.000,2023-10-27 16:55:39,13.0,3.0,5.0,2.0,3.0,,,59.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+229,Applied AI for Materials,,educational,,https://github.com/WardLT/applied-ai-for-materials,Course materials for Applied AI for Materials Science and Engineering.,6,False,,WardLT/applied-ai-for-materials,https://github.com/WardLT/applied-ai-for-materials,2020-10-12 19:39:06,2022-03-12 02:26:58.000,2022-03-12 02:26:41,107.0,,29.0,4.0,13.0,5.0,,50.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+230,ANI-1x Datasets,,datasets,MIT,https://github.com/aiqm/ANI1x_datasets,"The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules.",6,False,,aiqm/ANI1x_datasets,https://github.com/aiqm/ANI1x_datasets,2019-09-17 18:19:28,2022-04-11 17:25:55.000,2022-04-11 17:25:55,12.0,,5.0,6.0,,2.0,3.0,47.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+231,DeepDFT,,ml-dft,MIT,https://github.com/peterbjorgensen/DeepDFT,Official implementation of DeepDFT model.,6,True,,peterbjorgensen/DeepDFT,https://github.com/peterbjorgensen/DeepDFT,2020-11-03 11:51:15,2023-02-28 15:37:49.000,2023-02-28 15:37:37,128.0,,7.0,1.0,,,3.0,42.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+232,MACE-Jax,,ml-iap,MIT,https://github.com/ACEsuit/mace-jax,Equivariant machine learning interatomic potentials in JAX.,6,True,,ACEsuit/mace-jax,https://github.com/ACEsuit/mace-jax,2023-02-06 12:10:16,2023-10-04 08:07:35.000,2023-10-04 08:07:35,207.0,1.0,1.0,10.0,1.0,2.0,1.0,37.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+233,COMP6 Benchmark dataset,,datasets,MIT,https://github.com/isayev/COMP6,COMP6 Benchmark dataset for ML potentials.,6,False,,isayev/COMP6,https://github.com/isayev/COMP6,2017-12-29 16:58:35,2018-07-09 23:56:35.000,2018-07-09 23:56:34,27.0,,4.0,5.0,,2.0,1.0,36.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+234,DeepH-E3,,ml-dft,MIT,https://github.com/Xiaoxun-Gong/DeepH-E3,General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian.,6,True,['magnetism'],Xiaoxun-Gong/DeepH-E3,https://github.com/Xiaoxun-Gong/DeepH-E3,2023-03-16 11:25:58,2023-04-04 13:27:01.000,2023-04-04 13:26:27,16.0,,9.0,5.0,,3.0,6.0,36.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+235,milad,,rep-eng,GPL-3.0,https://github.com/muhrin/milad,Moment Invariants Local Atomic Descriptor.,6,True,['generative'],muhrin/milad,https://github.com/muhrin/milad,2020-04-23 09:14:24,2022-12-03 10:40:05.000,2022-12-03 10:39:59,110.0,,1.0,4.0,,,,27.0,,,,,,,1.0,1.0,,,,,,,3.0,,,,,,,,,,,,,,,,,
+236,MACE-Layer,,rep-learn,MIT,https://github.com/ACEsuit/mace-layer,Higher order equivariant graph neural networks for 3D point clouds.,6,True,,ACEsuit/mace-layer,https://github.com/ACEsuit/mace-layer,2022-11-09 17:03:41,2023-06-27 15:32:49.000,2023-06-06 10:09:58,19.0,,4.0,5.0,2.0,1.0,,26.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+237,charge_transfer_nnp,,rep-learn,MIT,https://github.com/pfnet-research/charge_transfer_nnp,Graph neural network potential with charge transfer.,6,False,['electrostatics'],pfnet-research/charge_transfer_nnp,https://github.com/pfnet-research/charge_transfer_nnp,2022-04-06 01:48:18,2022-04-06 01:53:35.000,2022-04-06 01:53:22,1.0,,6.0,13.0,,,,24.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+238,Mat2Spec,,ml-dft,MIT,https://github.com/gomes-lab/Mat2Spec,,6,False,['spectroscopy'],gomes-lab/Mat2Spec,https://github.com/gomes-lab/Mat2Spec,2022-01-17 11:45:57,2022-04-17 17:12:29.000,2022-04-17 17:12:29,8.0,,9.0,,,,,24.0,,,,,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+239,ALF,,ml-iap,https://github.com/lanl/ALF/blob/main/LICENSE,https://github.com/lanl/ALF,A framework for performing active learning for training machine-learned interatomic potentials.,6,True,['active-learning'],lanl/alf,https://github.com/lanl/ALF,2023-01-04 23:13:24,2023-09-26 19:47:46.000,2023-08-04 15:53:59,139.0,,9.0,7.0,25.0,,,20.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+240,GLAMOUR,,rep-learn,MIT,https://github.com/learningmatter-mit/GLAMOUR,Graph Learning over Macromolecule Representations.,6,True,['single-paper'],learningmatter-mit/GLAMOUR,https://github.com/learningmatter-mit/GLAMOUR,2021-08-20 18:16:40,2022-12-31 17:56:21.000,2022-12-31 17:56:21,14.0,,6.0,3.0,,,8.0,18.0,2021-08-23 18:58:52,0.1,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+241,SA-GPR,,rep-eng,LGPL-3.0,https://github.com/dilkins/TENSOAP,Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR).,6,False,['lang-c'],dilkins/TENSOAP,https://github.com/dilkins/TENSOAP,2020-05-04 14:19:01,2023-04-07 09:58:08.000,2022-09-29 09:30:45,25.0,,9.0,3.0,10.0,2.0,5.0,14.0,2020-12-17 16:51:47,2020.0,1.0,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+242,NICE,,rep-eng,MIT,https://github.com/lab-cosmo/nice,NICE (N-body Iteratively Contracted Equivariants) is a set of tools designed for the calculation of invariant and..,6,True,,lab-cosmo/nice,https://github.com/lab-cosmo/nice,2020-07-03 08:47:41,2023-05-01 09:22:21.000,2023-05-01 09:21:56,231.0,,2.0,6.0,7.0,2.0,1.0,12.0,,,1.0,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+243,testing-framework,,ml-iap,,https://github.com/libAtoms/testing-framework,The purpose of this repository is to aid the testing of a large number of interatomic potentials for a variety of..,6,False,['benchmarking'],libAtoms/testing-framework,https://github.com/libAtoms/testing-framework,2020-03-04 11:43:15,2022-02-10 17:23:46.000,2022-02-10 17:23:46,225.0,,6.0,16.0,10.0,5.0,3.0,11.0,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+244,CatGym,,reinforcement-learning,GPL,https://github.com/ulissigroup/catgym,Surface segregation using Deep Reinforcement Learning.,6,False,,ulissigroup/catgym,https://github.com/ulissigroup/catgym,2019-08-06 19:25:27,2021-08-30 17:05:36.000,2021-08-30 17:05:32,162.0,,2.0,4.0,,1.0,,10.0,,,,7.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+245,AGOX,,materials-discovery,GPL-3.0,https://gitlab.com/agox/agox,AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional..,6,False,['structure-optimization'],,,2022-03-08 09:08:13,2022-03-08 09:08:13.000,,,,4.0,,,10.0,7.0,10.0,,,2.0,,agox,,,,https://pypi.org/project/agox,49.0,49.0,,,,2.0,,,,,,,,,,,,,,agox/agox,https://gitlab.com/agox/agox,,
+246,ACEHAL,,active-learning,,https://github.com/ACEsuit/ACEHAL,Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials.,6,True,['lang-julia'],ACEsuit/ACEHAL,https://github.com/ACEsuit/ACEHAL,2023-02-24 17:33:47,2023-10-01 12:19:41.000,2023-09-21 21:50:43,121.0,14.0,2.0,5.0,15.0,4.0,6.0,9.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+247,PANNA,,ml-iap,MIT,https://gitlab.com/PANNAdevs/panna,A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic..,6,False,['benchmarking'],,,2018-11-09 10:47:48,2018-11-09 10:47:48.000,,,,10.0,,,,,7.0,,,2.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,PANNAdevs/panna,https://gitlab.com/PANNAdevs/panna,,
+248,fplib,,rep-eng,MIT,https://github.com/zhuligs/fplib,a fingerprint library.,6,False,"['lang-c', 'single-paper']",zhuligs/fplib,https://github.com/zhuligs/fplib,2015-09-07 08:18:27,2022-02-09 05:31:21.000,2022-02-09 05:31:12,37.0,,2.0,3.0,,,3.0,7.0,2021-02-03 21:40:23,pub,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+249,SOAPxx,,rep-eng,GPL-2.0,https://github.com/capoe/soapxx,A SOAP implementation.,6,False,['lang-cpp'],capoe/soapxx,https://github.com/capoe/soapxx,2016-03-29 10:00:00,2020-03-27 13:47:44.000,2020-03-27 13:47:36,289.0,,3.0,3.0,1.0,,2.0,7.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+250,SciGlass,,datasets,MIT,https://github.com/drcassar/SciGlass,The database contains a vast set of data on the properties of glass materials.,6,True,,drcassar/SciGlass,https://github.com/drcassar/SciGlass,2019-06-19 19:36:32,2023-08-27 13:46:44.000,2023-08-27 13:46:44,28.0,,3.0,1.0,,,,6.0,2023-08-27 13:48:09,2.0.1,1.0,2.0,,,,,,,0.0,,,,3.0,,,,,,3.0,,,,,,,,,,,
+251,COSMO Toolbox,,math,,https://github.com/lab-cosmo/toolbox,Assorted libraries and utilities for atomistic simulation analysis.,6,True,['lang-cpp'],lab-cosmo/toolbox,https://github.com/lab-cosmo/toolbox,2014-05-20 11:23:13,2023-06-23 15:07:59.000,2023-06-23 15:07:29,106.0,,5.0,26.0,1.0,,,6.0,,,,9.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+252,pyLODE,,rep-eng,Apache-2.0,https://github.com/ceriottm/lode,Pythonic implementation of LOng Distance Equivariants.,6,False,['electrostatics'],ceriottm/lode,https://github.com/ceriottm/lode,2022-01-19 17:01:38,2023-07-05 09:57:29.000,2023-07-05 09:57:14,241.0,,1.0,3.0,,1.0,,2.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+253,Visual Graph Datasets,,datasets,MIT,https://github.com/aimat-lab/visual_graph_datasets,Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations..,6,False,,aimat-lab/visual_graph_datasets,https://github.com/aimat-lab/visual_graph_datasets,2023-06-01 11:33:18,2023-11-15 13:38:38.000,2023-11-15 13:38:25,32.0,28.0,1.0,3.0,,,,1.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+254,Computational Autonomy for Materials Discovery (CAMD),,materials-discovery,Apache-2.0,https://github.com/ulissigroup/CAMD,Agent-based sequential learning software for materials discovery.,6,False,,ulissigroup/CAMD,https://github.com/ulissigroup/CAMD,2023-01-10 19:42:57,2023-01-10 19:49:35.000,2023-01-10 19:49:13,1336.0,,,1.0,,,,1.0,,,,17.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,
+255,"Data Handling, DoE and Statistical Analysis for Material Chemists",,educational,GPL-3.0,https://github.com/Teoroo-CMC/DoE_Course_Material,"Notebooks for workshops of DoE course, hosted by the Computational Materials Chemistry group at Uppsala University.",6,True,,Teoroo-CMC/DoE_Course_Material,https://github.com/Teoroo-CMC/DoE_Course_Material,2023-05-22 08:11:41,2023-06-26 12:48:17.000,2023-06-26 12:48:15,157.0,,13.0,2.0,1.0,,,,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+256,Per-site PAiNN,,rep-learn,MIT,https://github.com/learningmatter-mit/per-site_painn,Fork of PaiNN for PerovskiteOrderingGCNNs.,6,True,"['probabilistic', 'pre-trained', 'single-paper']",learningmatter-mit/per-site_painn,https://github.com/learningmatter-mit/per-site_painn,2023-06-04 14:23:49,2023-06-05 17:35:19.000,2023-06-05 17:30:34,123.0,,,,,,,,,,,10.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+257,tensorfieldnetworks,,rep-learn,MIT,https://github.com/tensorfieldnetworks/tensorfieldnetworks,,5,False,,tensorfieldnetworks/tensorfieldnetworks,https://github.com/tensorfieldnetworks/tensorfieldnetworks,2018-02-09 23:18:13,2020-01-07 17:22:16.000,2020-01-07 17:22:15,10.0,,28.0,9.0,2.0,,2.0,144.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+258,AI4Science101,,educational,,https://github.com/deepmodeling/AI4Science101,AI for Science.,5,False,,deepmodeling/AI4Science101,https://github.com/deepmodeling/AI4Science101,2022-06-19 02:26:48,2022-09-04 02:06:18.000,2022-09-04 02:06:18,139.0,,12.0,10.0,28.0,,1.0,72.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+259,SchNOrb,,ml-wft,MIT,https://github.com/atomistic-machine-learning/SchNOrb,Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions.,5,False,,atomistic-machine-learning/SchNOrb,https://github.com/atomistic-machine-learning/SchNOrb,2019-09-17 12:41:48,2019-09-17 14:31:47.000,2019-09-17 14:31:19,2.0,,17.0,5.0,,1.0,,55.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+260,Machine Learning for Materials Hard and Soft,,educational,,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,ESI-DCAFM-TACO-VDSP Summer School on Machine Learning for Materials Hard and Soft.,5,False,,CompPhysVienna/MLSummerSchoolVienna2022,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,2022-07-01 08:42:41,2022-07-22 08:10:24.000,2022-07-22 08:10:24,49.0,,17.0,1.0,14.0,,,33.0,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+261,Autobahn,,rep-learn,MIT,https://github.com/risilab/Autobahn,Repository for Autobahn: Automorphism Based Graph Neural Networks.,5,False,,risilab/Autobahn,https://github.com/risilab/Autobahn,2021-03-02 01:14:40,2022-03-01 21:04:09.000,2022-03-01 21:04:04,11.0,,2.0,5.0,,,,28.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+262,MLIP-3,,ml-iap,BSD-2-Clause,https://gitlab.com/ashapeev/mlip-3,MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP).,5,True,['lang-cpp'],,,2023-04-24 14:05:53,2023-04-24 14:05:53.000,,,,3.0,,,15.0,5.0,18.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,ashapeev/mlip-3,https://gitlab.com/ashapeev/mlip-3,,
+263,EquivariantOperators.jl,,math,MIT,https://github.com/aced-differentiate/EquivariantOperators.jl,,5,False,['lang-julia'],aced-differentiate/EquivariantOperators.jl,https://github.com/aced-differentiate/EquivariantOperators.jl,2021-11-29 03:36:21,2023-09-27 18:34:44.000,2023-09-27 18:34:44,62.0,4.0,,4.0,,,,17.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+264,SCFNN,,rep-learn,MIT,https://github.com/andy90/SCFNN,Self-consistent determination of long-range electrostatics in neural network potentials.,5,False,"['lang-cpp', 'electrostatics', 'single-paper']",andy90/SCFNN,https://github.com/andy90/SCFNN,2021-09-22 12:02:00,2022-01-30 02:29:03.000,2022-01-24 09:40:40,10.0,,8.0,2.0,,,,15.0,2022-01-30 02:29:04,1.0.0,1.0,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+265,rxngenerator,,generative,MIT,https://github.com/tsudalab/rxngenerator,A generative model for molecular generation via multi-step chemical reactions.,5,False,,tsudalab/rxngenerator,https://github.com/tsudalab/rxngenerator,2021-06-18 07:44:53,2022-08-09 07:21:44.000,2022-08-09 07:21:05,16.0,,2.0,9.0,2.0,1.0,,12.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+266,FieldSchNet,,rep-learn,MIT,https://github.com/atomistic-machine-learning/field_schnet,,5,False,,atomistic-machine-learning/field_schnet,https://github.com/atomistic-machine-learning/field_schnet,2020-11-18 10:26:59,2022-05-19 09:28:38.000,2022-05-19 09:28:38,26.0,,4.0,3.0,1.0,1.0,,11.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+267,NequIP-JAX,,ml-iap,,https://github.com/mariogeiger/nequip-jax,JAX implementation of the NequIP interatomic potential.,5,True,,mariogeiger/nequip-jax,https://github.com/mariogeiger/nequip-jax,2023-03-08 04:18:28,2023-11-01 20:35:48.000,2023-11-01 20:35:44,39.0,1.0,1.0,1.0,2.0,,,10.0,2023-06-22 22:36:36,1.1.0,3.0,3.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+268,charge-density-models,,ml-dft,MIT,https://github.com/ulissigroup/charge-density-models,Tools to build charge density models using ocpmodels.,5,True,,ulissigroup/charge-density-models,https://github.com/ulissigroup/charge-density-models,2022-06-22 13:47:53,2023-11-29 15:07:42.000,2023-11-29 15:07:42,96.0,1.0,3.0,2.0,16.0,,2.0,8.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+269,CraTENet,,rep-learn,MIT,https://github.com/lantunes/CraTENet,An attention-based deep neural network for thermoelectric transport properties.,5,True,['transport-phenomena'],lantunes/CraTENet,https://github.com/lantunes/CraTENet,2022-06-30 10:40:06,2023-04-05 01:13:22.000,2023-04-05 01:13:11,24.0,,1.0,1.0,,,,8.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+270,MolSLEPA,,generative,MIT,https://github.com/tsudalab/MolSLEPA,Interpretable Fragment-based Molecule Design with Self-learning Entropic Population Annealing.,5,True,['xai'],tsudalab/MolSLEPA,https://github.com/tsudalab/MolSLEPA,2023-04-10 15:04:55,2023-04-13 12:48:49.000,2023-04-13 12:48:49,11.0,,1.0,8.0,2.0,,,5.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+271,MAPI_LLM,,language-models,MIT,https://github.com/maykcaldas/MAPI_LLM,A LLM application developed during the LLM March MADNESS Hackathon https://doi.org/10.1039/D3DD00113J.,5,False,['dataset'],maykcaldas/MAPI_LLM,https://github.com/maykcaldas/MAPI_LLM,2023-03-30 04:24:54,2023-06-29 18:48:44.000,2023-06-08 18:46:04,21.0,,1.0,1.0,,,,4.0,2023-06-29 18:48:44,0.0.1,1.0,2.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+272,soap_turbo,,rep-eng,https://github.com/libAtoms/soap_turbo/blob/master/LICENSE.md,https://github.com/libAtoms/soap_turbo,soap_turbo comprises a series of libraries to be used in combination with QUIP/GAP and TurboGAP.,5,False,['lang-fortran'],libAtoms/soap_turbo,https://github.com/libAtoms/soap_turbo,2021-03-19 15:20:25,2023-05-24 09:42:07.000,2023-05-24 09:42:00,36.0,,5.0,7.0,,5.0,3.0,4.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+273,MACE-tutorials,,educational,MIT,https://github.com/ilyes319/mace-tutorials,Another set of tutorials for the MACE interatomic potential by one of the authors.,5,False,"['ml-iap', 'rep-learn', 'md']",ilyes319/mace-tutorials,https://github.com/ilyes319/mace-tutorials,2023-09-11 18:09:18,2023-10-10 11:09:34.000,2023-10-10 11:09:34,5.0,5.0,3.0,1.0,,,,3.0,,,,,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+274,COSMO tools,,others,,https://github.com/lab-cosmo/cosmo-tools,"Scripts, jupyter nbs, and general helpful stuff from COSMO by COSMO.",5,False,,lab-cosmo/cosmo-tools,https://github.com/lab-cosmo/cosmo-tools,2018-11-06 09:40:00,2023-11-13 06:28:17.000,2023-11-13 06:28:17,60.0,1.0,3.0,22.0,,,,2.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,True,
+275,Alchemical learning,,ml-iap,BSD-3-Clause,https://github.com/Luthaf/alchemical-learning,Code for the Modeling high-entropy transition metal alloys with alchemical compression article.,5,False,,Luthaf/alchemical-learning,https://github.com/Luthaf/alchemical-learning,2021-12-02 17:02:00,2023-04-24 18:35:45.000,2023-04-07 10:19:10,120.0,,1.0,6.0,1.0,,4.0,2.0,,,,10.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+276,linear-regression-benchmarks,,datasets,MIT,https://github.com/BingqingCheng/linear-regression-benchmarks,Data sets used for linear regression benchmarks.,5,False,"['benchmarking', 'single-paper']",BingqingCheng/linear-regression-benchmarks,https://github.com/BingqingCheng/linear-regression-benchmarks,2020-04-16 20:48:28,2022-01-26 08:29:46.000,2022-01-26 08:29:46,24.0,,,3.0,2.0,,,1.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+277,Per-Site CGCNN,,rep-learn,MIT,https://github.com/learningmatter-mit/per-site_cgcnn,Crystal graph convolutional neural networks for predicting material properties.,5,False,"['pre-trained', 'single-paper']",learningmatter-mit/per-site_cgcnn,https://github.com/learningmatter-mit/per-site_cgcnn,2023-05-30 18:59:03,2023-06-05 17:38:46.000,2023-06-05 17:38:41,28.0,,,,,,,1.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+278,ACE1Pack.jl,,ml-iap,MIT,https://github.com/ACEsuit/ACE1pack.jl,"Provides convenience functionality for the usage of ACE1.jl, ACEfit.jl, JuLIP.jl for fitting interatomic potentials..",5,True,['lang-julia'],ACEsuit/ACE1pack.jl,https://github.com/ACEsuit/ACE1pack.jl,2023-08-21 16:25:00,2023-08-21 16:30:19.000,2023-08-21 15:48:54,547.0,,,1.0,,,,,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,https://acesuit.github.io/ACE1pack.jl
+279,ACE Workflows,,ml-iap,,https://github.com/ACEsuit/ACEworkflows,Workflow Examples for ACE Models.,5,True,"['lang-julia', 'workflows']",ACEsuit/ACEworkflows,https://github.com/ACEsuit/ACEworkflows,2023-04-04 16:57:36,2023-10-12 18:01:00.000,2023-10-12 18:00:39,45.0,9.0,1.0,3.0,7.0,1.0,,,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+280,Point Edge Transformer (PET),,ml-iap,,https://github.com/serfg/pet,Point Edge Transformer.,5,True,"['rep-learn', 'transformer']",serfg/pet,https://github.com/serfg/pet,2023-02-08 18:36:10,2023-12-02 02:27:42.000,2023-12-02 02:27:29,79.0,4.0,,3.0,,,,,,,,3.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+281,ML-in-chemistry-101,,educational,,https://github.com/BingqingCheng/ML-in-chemistry-101,The course materials for Machine Learning in Chemistry 101.,4,False,,BingqingCheng/ML-in-chemistry-101,https://github.com/BingqingCheng/ML-in-chemistry-101,2020-02-09 17:47:07,2020-10-19 08:10:31.000,2020-10-19 08:10:30,13.0,,15.0,2.0,,,,57.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+282,Atom2Vec,,rep-learn,,https://github.com/idocx/Atom2Vec,Atom2Vec: a simple way to describe atoms for machine learning.,4,False,,idocx/Atom2Vec,https://github.com/idocx/Atom2Vec,2020-01-18 23:31:47,2022-10-16 05:43:31.000,2021-06-21 16:31:09,3.0,,8.0,1.0,1.0,1.0,1.0,26.0,,,,,atom2vec,,1.0,1.0,https://pypi.org/project/atom2vec,33.0,33.0,,,,3.0,,,,,,,,,,,,,,,,,
+283,Graph Transport Network,,rep-learn,https://github.com/gasteigerjo/gtn/blob/main/LICENSE.md,https://github.com/gasteigerjo/gtn,"Graph transport network (GTN), as proposed in Scalable Optimal Transport in High Dimensions for Graph Distances,..",4,False,['transport-phenomena'],gasteigerjo/gtn,https://github.com/gasteigerjo/gtn,2021-07-11 23:36:22,2023-04-26 14:22:00.000,2023-04-26 14:22:00,9.0,,3.0,2.0,,,,15.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+284,glp,,ml-iap,MIT,https://github.com/sirmarcel/glp,tools for graph-based machine-learning potentials in jax.,4,False,,sirmarcel/glp,https://github.com/sirmarcel/glp,2023-03-27 15:19:40,2023-08-07 14:41:10.000,2023-08-07 14:41:05,10.0,,,1.0,2.0,,,13.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+285,3DSC Database,,datasets,https://github.com/aimat-lab/3DSC/blob/main/LICENSE.md,https://github.com/aimat-lab/3DSC,Repo for the paper publishing the superconductor database with 3D crystal structures.,4,False,"['superconductors', 'materials-discovery']",aimat-lab/3DSC,https://github.com/aimat-lab/3DSC,2021-11-02 09:07:57,2023-07-21 09:28:43.000,2023-07-21 09:26:12,52.0,,2.0,2.0,,,,9.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+286,ChemDataWriter,,language-models,MIT,https://github.com/ShuHuang/chemdatawriter,ChemDataWriter is a transformer-based library for automatically generating research books in the chemistry area.,4,False,['literature-data'],ShuHuang/chemdatawriter,https://github.com/ShuHuang/chemdatawriter,2023-09-22 10:05:25,2023-10-07 04:23:47.000,2023-10-07 04:07:59,9.0,9.0,1.0,2.0,,1.0,,8.0,,,,,chemdatawriter,,,,https://pypi.org/project/chemdatawriter,,,,,,3.0,,,,,,,True,,,,,,,,,,
+287,chemrev-gpr,,educational,,https://github.com/gabor1/chemrev-gpr,Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020.,4,False,,gabor1/chemrev-gpr,https://github.com/gabor1/chemrev-gpr,2020-12-18 23:48:06,2021-05-04 19:21:34.000,2021-05-04 19:21:30,10.0,,6.0,4.0,,,,6.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+288,TensorPotential,,ml-iap,https://github.com/ICAMS/TensorPotential/blob/main/LICENSE.md,https://cortner.github.io/ACEweb/software/,"Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic..",4,False,,ICAMS/TensorPotential,https://github.com/ICAMS/TensorPotential,2021-12-08 12:10:04,2023-07-10 16:37:18.000,2023-07-10 16:37:18,18.0,,4.0,2.0,2.0,,,5.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+289,interface-lammps-mlip-3,,md,GPL-2.0,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,An interface between LAMMPS and MLIP (version 3).,4,False,,,,2023-04-24 12:48:51,2023-04-24 12:48:51.000,,,,3.0,,,4.0,,5.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,ivannovikov/interface-lammps-mlip-3,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,,
+290,ACEatoms,,general-tool,https://github.com/ACEsuit/ACEatoms.jl/blob/main/ASL.md,https://github.com/ACEsuit/ACEatoms.jl,Generic code for modelling atomic properties using ACE.,4,False,['lang-julia'],ACEsuit/ACEatoms.jl,https://github.com/ACEsuit/ACEatoms.jl,2021-03-23 23:50:03,2023-01-13 21:35:06.000,2023-01-13 21:28:08,134.0,,1.0,3.0,14.0,4.0,3.0,2.0,,,,10.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+291,atom_by_atom,,rep-learn,,https://github.com/learningmatter-mit/atom_by_atom,Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with Machine Learning.,4,False,"['surface-science', 'single-paper']",learningmatter-mit/atom_by_atom,https://github.com/learningmatter-mit/atom_by_atom,2023-05-30 20:18:00,2023-10-19 15:59:08.000,2023-10-19 15:35:49,74.0,10.0,,2.0,,,,2.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+292,SISSO++,,rep-eng,Apache-2.0,https://gitlab.com/sissopp_developers/sissopp,C++ Implementation of SISSO with python bindings.,4,False,['lang-cpp'],,,2021-04-30 14:20:59,2021-04-30 14:20:59.000,,,,3.0,,,,12.0,2.0,,,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,sissopp_developers/sissopp,https://gitlab.com/sissopp_developers/sissopp,,
+293,cnine,,math,,https://github.com/risi-kondor/cnine,Cnine tensor library.,4,False,['lang-cpp'],risi-kondor/cnine,https://github.com/risi-kondor/cnine,2022-10-07 20:54:54,2023-12-01 09:48:24.000,2023-12-01 09:48:18,257.0,67.0,1.0,1.0,1.0,,1.0,2.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,https://risi-kondor.github.io/cnine/
+294,magnetism-prediction,,rep-eng,Apache-2.0,https://github.com/dppant/magnetism-prediction,DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides.,4,False,"['magnetism', 'single-paper']",dppant/magnetism-prediction,https://github.com/dppant/magnetism-prediction,2022-09-13 03:58:10,2023-07-19 13:25:49.000,2023-07-19 13:25:49,46.0,,,3.0,,,,1.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+295,MLatom,,general-tool,https://creativecommons.org/licenses/by-nc-nd/4.0/,http://mlatom.com/,Machine learning for atomistic simulations.,4,False,,,,,,,,,,,,,,,,,,,MLatom,,,,https://pypi.org/project/MLatom,280.0,280.0,,,,3.0,,,,,,,,,,,,,,,,,http://mlatom.com/manual/
+296,gprep,,ml-dft,MIT,https://gitlab.com/jmargraf/gprep,Fitting DFTB repulsive potentials with GPR.,4,False,['single-paper'],,,2019-09-30 09:15:04,2019-09-30 09:15:04.000,,,,0.0,,,,,,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,jmargraf/gprep,https://gitlab.com/jmargraf/gprep,,
+297,closed-loop-acceleration-benchmarks,,materials-discovery,MIT,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational..,4,False,"['materials-discovery', 'active-learning', 'single-paper']",aced-differentiate/closed-loop-acceleration-benchmarks,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,2022-11-10 20:22:30,2023-07-25 21:25:42.000,2023-05-02 17:07:48,17.0,,1.0,4.0,3.0,,,,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+298,ML-for-CurieTemp-Predictions,,rep-eng,MIT,https://github.com/msg-byu/ML-for-CurieTemp-Predictions,Machine Learning Predictions of High-Curie-Temperature Materials.,4,False,"['single-paper', 'magnetism']",msg-byu/ML-for-CurieTemp-Predictions,https://github.com/msg-byu/ML-for-CurieTemp-Predictions,2023-06-05 22:46:47,2023-06-14 19:05:50.000,2023-06-14 19:05:47,25.0,,,1.0,,,,,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+299,torch_spex,,math,,https://github.com/lab-cosmo/torch_spex,Spherical expansions in PyTorch.,4,False,,lab-cosmo/torch_spex,https://github.com/lab-cosmo/torch_spex,2023-03-28 09:48:36,2023-11-02 06:19:26.000,2023-11-02 06:19:26,61.0,4.0,2.0,4.0,30.0,7.0,,,,,,2.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+300,PeriodicPotentials,,ml-iap,MIT,https://github.com/AaltoRSE/PeriodicPotentials,A Periodic table app that displays potentials based on the selected elements.,4,False,"['community', 'visualization', 'lang-js']",AaltoRSE/PeriodicPotentials,https://github.com/AaltoRSE/PeriodicPotentials,2022-10-14 09:03:59,2022-10-18 17:10:22.000,2022-10-18 17:10:22,17.0,,1.0,3.0,3.0,,,,,,,2.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+301,xDeepH,,ml-dft,LGPL-3.0,https://github.com/mzjb/xDeepH,Extended DeepH (xDeepH) method for magnetic materials.,3,False,"['magnetism', 'lang-julia']",mzjb/xDeepH,https://github.com/mzjb/xDeepH,2023-02-23 12:56:49,2023-06-14 11:44:53.000,2023-06-14 11:44:46,4.0,,1.0,2.0,,,,23.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+302,Coarse-Graining-Auto-encoders,,unsupervised,,https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders,,3,False,['single-paper'],learningmatter-mit/Coarse-Graining-Auto-encoders,https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders,2019-09-16 15:27:57,2019-08-16 21:39:34.000,2019-08-16 21:39:33,14.0,,7.0,6.0,,,,20.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+303,ML-DFT,,ml-dft,MIT,https://github.com/MihailBogojeski/ml-dft,A package for density functional approximation using machine learning.,3,False,,MihailBogojeski/ml-dft,https://github.com/MihailBogojeski/ml-dft,2020-09-14 22:15:56,2020-09-18 16:36:30.000,2020-09-18 16:36:30,9.0,,6.0,2.0,,1.0,1.0,19.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+304,SciBot,,language-models,,https://github.com/CFN-softbio/SciBot,SciBot is a simple demo of building a domain-specific chatbot for science.,3,False,,CFN-softbio/SciBot,https://github.com/CFN-softbio/SciBot,2023-06-12 12:41:44,2023-10-27 13:45:22.000,2023-10-27 13:44:54,11.0,1.0,1.0,6.0,,,,14.0,,,,,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+305,CatBERTa,,language-models,,https://github.com/hoon-ock/CatBERTa,Large Language Model for Catalyst Property Prediction.,3,False,"['transformer', 'catalysis']",hoon-ock/CatBERTa,https://github.com/hoon-ock/CatBERTa,2023-05-19 18:23:17,2023-09-13 20:25:40.000,2023-09-13 20:25:40,91.0,3.0,1.0,1.0,,,,13.0,,,,,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+306,SPINNER,,materials-discovery,GPL-3.0,https://github.com/MDIL-SNU/SPINNER,SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random..,3,False,"['lang-cpp', 'structure-prediction']",MDIL-SNU/SPINNER,https://github.com/MDIL-SNU/SPINNER,2021-07-15 02:10:58,2021-11-25 07:58:15.000,2021-11-25 07:58:15,102.0,,2.0,1.0,,1.0,,9.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+307,sl_discovery,,materials-discovery,Apache-2.0,https://github.com/CitrineInformatics-ERD-public/sl_discovery,Data processing and models related to Quantifying the performance of machine learning models in materials discovery.,3,False,"['materials-discovery', 'single-paper']",CitrineInformatics-ERD-public/sl_discovery,https://github.com/CitrineInformatics-ERD-public/sl_discovery,2022-10-24 18:10:14,2022-12-20 23:46:05.000,2022-12-20 23:45:57,5.0,,2.0,2.0,,,,5.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+308,Element encoder,,rep-learn,GPL-3.0,https://github.com/jeherr/element-encoder,Autoencoder neural network to compress properties of atomic species into a vector representation.,3,False,['single-paper'],jeherr/element-encoder,https://github.com/jeherr/element-encoder,2019-03-27 17:11:30,2020-01-09 15:54:27.000,2020-01-09 15:54:26,8.0,,1.0,4.0,,,1.0,5.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+309,KmdPlus,,unsupervised,,https://github.com/Minoru938/KmdPlus,"This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with..",3,False,,Minoru938/KmdPlus,https://github.com/Minoru938/KmdPlus,2023-03-26 10:06:34,2023-10-17 08:28:01.000,2023-10-17 08:28:01,7.0,3.0,,1.0,,,,3.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+310,DeepCDP,,ml-dft,,https://github.com/siddarthachar/deepcdp,DeepCDP: Deep learning Charge Density Prediction.,3,False,,siddarthachar/deepcdp,https://github.com/siddarthachar/deepcdp,2021-12-18 14:26:56,2023-06-16 20:38:23.000,2023-06-16 20:38:23,96.0,,,2.0,27.0,,,3.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+311,BERT-PSIE-TC,,language-models,MIT,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,A dataset of Curie temperatures automatically extracted from scientific literature with the use of the BERT-PSIE..,3,False,['magnetism'],StefanoSanvitoGroup/BERT-PSIE-TC,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,2023-01-25 10:27:26,2023-08-18 11:47:45.000,2023-08-18 12:48:31,36.0,,2.0,1.0,,,,2.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+312,Linear vs blackbox,,xai,MIT,https://github.com/CitrineInformatics-ERD-public/linear-vs-blackbox,Code and data related to the publication: Interpretable models for extrapolation in scientific machine learning.,3,False,"['xai', 'single-paper', 'rep-eng']",CitrineInformatics-ERD-public/linear-vs-blackbox,https://github.com/CitrineInformatics-ERD-public/linear-vs-blackbox,2022-12-02 20:32:53,2022-12-16 18:48:12.000,2022-12-16 18:48:12,4.0,,,1.0,,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+313,CSNN,,ml-dft,BSD-3-Clause,https://github.com/foxjas/CSNN,Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning.,3,False,,foxjas/CSNN,https://github.com/foxjas/CSNN,2022-05-19 15:40:49,2022-10-11 04:27:40.000,2022-10-11 04:27:40,6.0,,,1.0,,,,1.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+314,Magpie,,general-tool,MIT,https://bitbucket.org/wolverton/magpie/,Materials Agnostic Platform for Informatics and Exploration (Magpie).,3,False,['lang-java'],,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+315,MALADA,,ml-dft,BSD-3-Clause,https://github.com/mala-project/malada,MALA Data Acquisition: Helpful tools to build data for MALA.,3,False,,mala-project/malada,https://github.com/mala-project/malada,2021-07-26 05:46:08,2023-05-24 09:18:24.000,2023-05-24 09:18:24,111.0,,1.0,2.0,4.0,17.0,2.0,,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+316,CSPML (crystal structure prediction with machine learning-based element substitution),,materials-discovery,,https://github.com/Minoru938/CSPML,Original implementation of CSPML.,2,False,['structure-prediction'],minoru938/cspml,https://github.com/Minoru938/CSPML,2022-01-15 10:59:27,2022-06-02 23:26:26.000,2022-06-02 23:26:26,7.0,,8.0,2.0,,2.0,1.0,14.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+317,SingleNN,,ml-iap,,https://github.com/lmj1029123/SingleNN,An efficient package for training and executing neural-network interatomic potentials.,2,False,['lang-cpp'],lmj1029123/SingleNN,https://github.com/lmj1029123/SingleNN,2020-03-11 18:36:16,2021-11-09 00:40:18.000,2021-11-09 00:40:10,17.0,,1.0,1.0,,1.0,,7.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+318,MLDensity_tutorial,,educational,,https://github.com/bfocassio/MLDensity_tutorial,Tutorial files to work with ML for the charge density in molecules and solids.,2,False,,bfocassio/MLDensity_tutorial,https://github.com/bfocassio/MLDensity_tutorial,2023-01-31 10:33:23,2023-02-22 19:20:32.000,2023-02-22 19:20:32,8.0,,1.0,1.0,,,,6.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+319,A3MD,,ml-dft,,https://github.com/brunocuevas/a3md,MPNN-like + Analytic Density Model = Accurate electron densities.,2,False,"['representation-learning', 'single-paper']",brunocuevas/a3md,https://github.com/brunocuevas/a3md,2021-06-02 07:23:17,2021-12-02 17:10:39.000,2021-12-02 17:10:34,4.0,,1.0,2.0,,,,5.0,,,,,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+320,LAMMPS-style pair potentials with GAP,,educational,,https://github.com/victorprincipe/pair_potentials,A tutorial on how to create LAMMPS-style pair potentials and use them in combination with GAP potentials to run MD..,2,False,"['ml-iap', 'md', 'rep-eng']",victorprincipe/pair_potentials,https://github.com/victorprincipe/pair_potentials,2022-09-21 09:45:03,2022-10-03 08:06:22.000,2022-10-03 08:05:53,36.0,,,1.0,1.0,,,3.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+321,MALA Tutorial,,educational,,https://github.com/mala-project/mala_tutorial,A full MALA hands-on tutorial.,2,False,,mala-project/mala_tutorial,https://github.com/mala-project/mala_tutorial,2023-03-09 14:01:54,2023-11-28 11:20:39.000,2023-11-28 11:17:01,24.0,1.0,,2.0,,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+322,PiNN Lab,,educational,GPL-3.0,https://github.com/Teoroo-CMC/PiNN_lab,,2,False,,Teoroo-CMC/PiNN_lab,https://github.com/Teoroo-CMC/PiNN_lab,2019-03-17 22:09:30,2023-05-01 15:59:56.000,2023-05-01 15:59:22,9.0,,1.0,3.0,1.0,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+323,gkx: Green-Kubo Method in JAX,,rep-learn,MIT,https://github.com/sirmarcel/gkx,Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast.,2,False,['transport-phenomena'],sirmarcel/gkx,https://github.com/sirmarcel/gkx,2023-04-30 12:25:16,2023-04-30 14:14:57.000,2023-04-30 14:14:46,2.0,,,1.0,,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+324,e3psi,,ml-esm,LGPL-3.0,https://github.com/muhrin/e3psi,Equivariant machine learning library for learning from electronic structures.,2,False,,muhrin/e3psi,https://github.com/muhrin/e3psi,2022-08-08 10:48:30,2023-08-09 17:04:49.000,2023-04-10 17:04:33,14.0,,,2.0,,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+325,Wigner Kernels,,math,,https://github.com/lab-cosmo/wigner_kernels,Collection of programs to benchmark Wigner kernels.,2,False,['benchmarking'],lab-cosmo/wigner_kernels,https://github.com/lab-cosmo/wigner_kernels,2022-12-08 12:28:26,2023-07-08 15:48:41.000,2023-07-08 15:48:37,109.0,,,1.0,,,,1.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+326,quantum-structure-ml,,general-tool,,https://github.com/hgheiberger/quantum-structure-ml,Multi-class classification model for predicting the magnetic order of magnetic structures and a binary classification..,2,False,"['magnetism', 'benchmarking']",hgheiberger/quantum-structure-ml,https://github.com/hgheiberger/quantum-structure-ml,2020-10-05 01:11:01,2022-12-22 21:45:40.000,2022-12-22 21:45:40,19.0,,,2.0,,,,1.0,2022-08-18 05:25:24,1.0.0,1.0,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+327,MEGNetSparse,,ml-iap,MIT,https://github.com/HSE-LAMBDA/MEGNetSparse,"A library imlementing a graph neural network with sparse representation from Code for Kazeev, N., Al-Maeeni, A.R.,..",2,False,['material-defect'],HSE-LAMBDA/MEGNetSparse,https://github.com/HSE-LAMBDA/MEGNetSparse,2023-07-19 08:17:42,2023-08-21 17:11:34.000,2023-08-21 17:11:25,19.0,,,2.0,,,,1.0,,,,2.0,MEGNetSparse,,,,https://pypi.org/project/MEGNetSparse,40.0,40.0,,,,3.0,,,,,,,True,,,,,,,,,,
+328,AMP,,rep-eng,,https://bitbucket.org/andrewpeterson/amp/,Amp is an open-source package designed to easily bring machine-learning to atomistic calculations.,2,False,,,,,,,,,,,,,,,,,,,amp-atomistics,,,,https://pypi.org/project/amp-atomistics,87.0,87.0,,,,3.0,,,,,,,,,,,,,,,,,https://amp.readthedocs.io/
+329,q-pac,,ml-esm,MIT,,,2,False,['electrostatics'],,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,jmargraf/kqeq,,,
+330,RuNNer,,ml-iap,GPL-3.0,https://www.uni-goettingen.de/de/560580.html,The RuNNer Neural Network Energy Representation is a Fortran-based framework for the construction of Behler-..,2,False,['lang-fortran'],,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,https://theochemgoettingen.gitlab.io/RuNNer/
+331,Point Edge Transformer,,rep-learn,CC-BY-4.0,https://zenodo.org/record/7967079,"Smooth, exact rotational symmetrization for deep learning on point clouds.",2,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+332,Allegro-JAX,,ml-iap,,https://github.com/mariogeiger/allegro-jax,JAX implementation of the Allegro interatomic potential.,1,False,,mariogeiger/allegro-jax,https://github.com/mariogeiger/allegro-jax,2023-07-02 19:00:00,2023-07-02 21:45:07.000,2023-07-02 21:45:04,4.0,,,2.0,,1.0,,11.0,,,,,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+333,SphericalNet,,rep-learn,,https://github.com/risilab/SphericalNet,Implementation of Clebsch-Gordan Networks (CGnet: https://arxiv.org/pdf/1806.09231.pdf) by GElib & cnine libraries in..,1,False,,risilab/SphericalNet,https://github.com/risilab/SphericalNet,2022-05-31 14:39:05,2022-06-07 03:57:10.000,2022-06-07 03:53:49,1.0,,,2.0,,,,3.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,
+334,kdft,,ml-dft,,https://gitlab.com/jmargraf/kdf,The Kernel Density Functional (KDF) code allows generating ML based DFT functionals.,1,False,,,,2020-11-07 21:50:22,2020-11-07 21:50:22.000,,,,0.0,,,,,2.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,jmargraf/kdf,https://gitlab.com/jmargraf/kdf,,
+335,APET,,ml-dft,GPL-3.0,https://github.com/emotionor/APET,Atomic Positional Embedding-based Transformer.,1,False,"['density-of-states', 'transformer']",emotionor/APET,https://github.com/emotionor/APET,2023-03-06 01:53:16,2023-09-28 03:16:11.000,2023-09-28 03:16:11,11.0,1.0,,2.0,,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+336,mlp,,ml-iap,,https://github.com/cesmix-mit/MLP,Proper orthogonal descriptors for efficient and accurate interatomic potentials...,1,False,['lang-julia'],cesmix-mit/mlp,https://github.com/cesmix-mit/MLP,2022-02-25 23:03:09,2022-10-22 19:01:45.000,2022-10-22 19:01:42,12.0,,1.0,2.0,,,,1.0,,,,,,,,,,,,,,,3.0,,,,,,,True,,,,,,,,,,
+337,Descriptor Embedding and Clustering for Atomisitic-environment Framework (DECAF),,unsupervised,,https://gitlab.mpcdf.mpg.de/klai/decaf,Provides a workflow to obtain clustering of local environments in dataset of structures.,0,False,,,,,,,41.0,,,,,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+338,GitHub topic materials-informatics,,community,,https://github.com/topics/materials-informatics,,0,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+339,MateriApps,,community,,https://ma.issp.u-tokyo.ac.jp/en/,,0,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+340,MLDensity,,ml-dft,,{},Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure..,0,False,,StefanoSanvitoGroup/MLdensity,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/latest-changes.md b/latest-changes.md
index f4397e2..f8491ac 100644
--- a/latest-changes.md
+++ b/latest-changes.md
@@ -1 +1,47 @@
-Nothing changed from last update.
\ No newline at end of file
+## ➕ Added Projects
+
+_Projects that were recently added to this best-of list._
+
+- JAX-DFT (🥇25 · ⭐ 32K · ➕) - Google Research. Apache-2
+- DIG: Dive into Graphs (🥈21 · ⭐ 1.7K · ➕) - A library for graph deep learning research. GPL-3.0
+- ATOM3D (🥇18 · ⭐ 280 · 💤) - ATOM3D: tasks on molecules in three dimensions. MIT biomolecules benchmarking
+- ChemCrow (🥇17 · ⭐ 320 · 🐣) - Chemcrow. MIT
+- ChemDataExtractor (🥈16 · ⭐ 270 · 💀) - Automatically extract chemical information from scientific documents. MIT literature-data
+- ChemNLP project (🥈16 · ⭐ 110 · ➕) - ChemNLP project. MIT datasets
+- GT4SD - Generative Toolkit for Scientific Discovery (🥈15 · ⭐ 280 · ➕) - Gradio apps of generative models in GT4SD. MIT generative pre-trained drug-discovery
+- dlpack (🥉14 · ⭐ 800 · 💤) - common in-memory tensor structure. Apache-2 C++
+- Geometric GNN Dojo (🥇12 · ⭐ 350 · ➕) - New to geometric GNNs: try our practical notebook, prepared for MPhil students at the University of Cambridge. MIT rep-learn
+- QH9: A Quantum Hamiltonian Prediction Benchmark (🥈12 · ⭐ 280 · ➕) - Artificial Intelligence for Science (AIRS). CC-BY-NC-SA 4.0 ML-DFT
+- QHNet (🥈12 · ⭐ 280 · ➕) - Artificial Intelligence for Science (AIRS). GPL-3.0 rep-learn
+- Grad DFT (🥈12 · ⭐ 43 · ➕) - Grad-DFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation.. Apache-2
+- pretrained-gnns (🥇10 · ⭐ 870 · ➕) - Strategies for Pre-training Graph Neural Networks. MIT pre-trained
+- DSECOP (🥈10 · ⭐ 31 · ➕) - This repository contains data science educational materials developed by DSECOP Fellows. CCO-1.0
+- pair_nequip (🥉10 · ⭐ 29 · 💀) - LAMMPS pair style for NequIP. MIT ML-IAP rep-learn
+- tinker-hp (🥉9 · ⭐ 69 · ➕) - Tinker-HP: High-Performance Massively Parallel Evolution of Tinker on CPUs & GPUs. Custom
+- lie-nn (🥈9 · ⭐ 22 · ➕) - Tools for building equivariant polynomials on reductive Lie groups. MIT rep-learn
+- TurboGAP (🥉9 · ⭐ 14 · ➕) - The TurboGAP code. Custom Fortran
+- MoLFormers UI (🥉8 · ⭐ 140 · ➕) - Repository for MolFormer. Apache-2 transformer Language models pre-trained drug-discovery
+- MoLFormer (🥉8 · ⭐ 140 · ➕) - Repository for MolFormer. Apache-2 transformer pre-trained drug-discovery
+- pair_allegro (🥉8 · ⭐ 26 · ➕) - LAMMPS pair style for Allegro deep learning interatomic potentials with parallelization support. MIT ML-IAP rep-learn
+- chemlift (🥉8 · ⭐ 10 · 🐣) - Language-interfaced fine-tuning for chemistry. MIT
+- T-e3nn (🥉8 · ⭐ 6 · 💤) - Time-reversal Euclidean neural networks based on e3nn. MIT magnetism
+- Awesome Neural Geometry (🥉7 · ⭐ 780 · ➕) - A curated collection of resources and research related to the geometry of representations in the brain, deep networks,.. Unlicensed educational rep-learn
+- COATI (🥉6 · ⭐ 59 · 🐣) - COATI: multi-modal contrastive pre-training for representing and traversing chemical space. Apache-2 drug-discovery pre-trained rep-learn
+- Mat2Spec (🥉6 · ⭐ 24 · 💀) - MIT spectroscopy
+- NequIP-JAX (🥉5 · ⭐ 10 · ➕) - JAX implementation of the NequIP interatomic potential. Unlicensed
+- MAPI_LLM (🥉5 · ⭐ 4 · ➕) - A LLM application developed during the LLM March MADNESS Hackathon https://doi.org/10.1039/D3DD00113J. MIT dataset
+- soap_turbo (🥉5 · ⭐ 4 · 💤) - soap_turbo comprises a series of libraries to be used in combination with QUIP/GAP and TurboGAP. Custom Fortran
+- MACE-tutorials (🥉5 · ⭐ 3 · 🐣) - Another set of tutorials for the MACE interatomic potential by one of the authors. MIT ML-IAP rep-learn MD
+- Point Edge Transformer (PET) (🥉5 · ➕) - Point Edge Transformer. Unlicensed rep-learn transformer
+- ChemDataWriter (🥉4 · ⭐ 8 · 🐣) - ChemDataWriter is a transformer-based library for automatically generating research books in the chemistry area. MIT literature-data
+- torch_spex (🥉4 · ➕) - Spherical expansions in PyTorch. Unlicensed
+- PeriodicPotentials (🥉4 · 💀) - A Periodic table app that displays potentials based on the selected elements. MIT community-resource viz JavaScript
+- SciBot (🥉3 · ⭐ 14 · 🐣) - SciBot is a simple demo of building a domain-specific chatbot for science. Unlicensed
+- CatBERTa (🥉3 · ⭐ 13 · ➕) - Large Language Model for Catalyst Property Prediction. Unlicensed transformer catalysis
+- A3MD (🥉2 · ⭐ 5 · 💀) - MPNN-like + Analytic Density Model = Accurate electron densities. Unlicensed representation-learning single-paper
+- LAMMPS-style pair potentials with GAP (🥉2 · ⭐ 3 · 💀) - A tutorial on how to create LAMMPS-style pair potentials and use them in combination with GAP potentials to run MD.. Unlicensed ML-IAP MD rep-eng
+- MEGNetSparse (🥉2 · ⭐ 1 · 🐣) - A library imlementing a graph neural network with sparse representation from Code for Kazeev, N., Al-Maeeni, A.R.,.. MIT material-defect
+- Allegro-JAX (🥉1 · ⭐ 11 · 🐣) - JAX implementation of the Allegro interatomic potential. Unlicensed
+- APET (🥉1 · ⭐ 2 · ➕) - Atomic Positional Embedding-based Transformer. GPL-3.0 density-of-states transformer
+- mlp (🥉1 · ⭐ 1 · 💀) - Proper orthogonal descriptors for efficient and accurate interatomic potentials... Unlicensed Julia
+