+ 
-
+
EPL-2.0 ML-IAP MD workflows HTC FAIREPL-2.0 ML-IAP MD workflows HTC FAIRMITMITMIT C++ ML-IAP
+- flare++ (🥈10 · ⭐ 35 · 💀) - A many-body extension of the FLARE code. MIT C++ ML-IAP
+- ALEBREW (🥉3 · ⭐ 9 · 🐣) - Official repository for the paper Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic.. Custom ML-IAP MD
Apache-2Apache-2language-models generative pretrained transformer
+🔗 GAP-ML.org community homepage ML-IAP
+
🔗 matsci.org - A community forum for the discussion of anything materials science, with a focus on computational materials science..
🔗 Matter Modeling Stack Exchange - Machine Learning - Forum StackExchange, site Matter Modeling, ML-tagged questions.
-CC-BY-4.0 general-ml PythonCC-BY-4.0 general-ml PythonMIT datasets benchmarking model-repositoryMIT datasets benchmarking model-repositoryBSD-3 datasetsMIT generative pretrained drug-discovery model-repositoryGPL-3.0 licenseMIT datasets benchmarking model-repositoryGPL-3.0 licenseApache-2 datasets materials-discoveryUnlicensed rep-learnApache-2 datasets materials-discoveryCustomCustomApache-2MIT datasetsUnlicensed educational rep-learnUnlicensed educational rep-learnUnlicensed datasetsUnlicensed rep-learnMIT active-learningMITUnlicensed datasetsMIT datasets
- A Highly Opinionated List of Open-Source Materials Informatics Resources (🥉8 · ⭐ 120 · 💀) - A Highly Opinionated List of Open Source Materials Informatics Resources. MIT
+- Geometric-GNNs (🥉4 · ⭐ 85 · ➕) - List of Geometric GNNs for 3D atomic systems. Unlicensed datasets educational rep-learn
+- GitHub topic materials-informatics (🥉1) - GitHub topic materials-informatics. Unlicensed
- MateriApps (🥉1) - A Portal Site of Materials Science Simulation. Unlicensed
-- GitHub topic materials-informatics - Unlicensed
model-repository pretrained language-models
+🔗 Graphs of Materials Project 20190401 - The dataset used to train the MEGNet interatomic potential. ML-IAP
+
+🔗 HME21 Dataset - High-temperature multi-element 2021 dataset for the PreFerred Potential (PFP).. UIP
+
🔗 JARVIS-Leaderboard ( ⭐ 55) - Explore State-of-the-Art Materials Design Methods: https://www.nature.com/articles/s41524-024-01259-w. model-repository benchmarking community-resource educational
🔗 Materials Project - Charge Densities - Materials Project has started offering charge density information available for download via their public API.
+🔗 Materials Project Trajectory (MPtrj) Dataset - The dataset used to train the CHGNet universal potential. UIP
+
🔗 matterverse.ai - Database of yet-to-be-sythesized materials predicted using state-of-the-art machine learning algorithms.
+🔗 MPF.2021.2.8 - The dataset used to train the M3GNet universal potential. UIP
+
🔗 NRELMatDB - Computational materials database with the specific focus on materials for renewable energy applications including, but..
🔗 Quantum-Machine.org Datasets - Collection of datasets, including QM7, QM9, etc. MD, DFT. Small organic molecules, mostly.
@@ -318,23 +381,23 @@ _Datasets, databases and trained models for atomistic ML._
MITMITMIT catalysisMIT catalysisCC-BY-4.0CC-BY-4.0CC-BY-NC-SA 4.0 ML-DFTCC-BY-NC-SA-4.0 ML-DFTMIT data-structuresApache-2LGPL-3.0 community-resource model-repositoryUnlicensed community-resourceCustom superconductors materials-discoveryUnlicensedMIT biomolecules benchmarking
- OpenKIM (🥈10 · ⭐ 31 · 💀) - The Open Knowledgebase of Interatomic Models (OpenKIM) aims to be an online resource for standardized testing, long-.. LGPL-2.1 model-repository knowledge-base pretrained
-- ANI-1 Dataset (🥉8 · ⭐ 95 · 💀) - A data set of 20 million calculated off-equilibrium conformations for organic molecules. MIT
+- ANI-1 Dataset (🥉8 · ⭐ 96 · 💀) - A data set of 20 million calculated off-equilibrium conformations for organic molecules. MIT
- MoleculeNet Leaderboard (🥉8 · ⭐ 87 · 💀) - MIT benchmarking
- GEOM (🥉7 · ⭐ 190 · 💀) - GEOM: Energy-annotated molecular conformations. Unlicensed drug-discovery
- ANI-1x Datasets (🥉6 · ⭐ 53 · 💀) - The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules. MIT
- COMP6 Benchmark dataset (🥉6 · ⭐ 39 · 💀) - COMP6 Benchmark dataset for ML potentials. MIT
- OPTIMADE providers dashboard (🥉6 · ⭐ 1) - A dashboard of known providers. Unlicensed
-- Visual Graph Datasets (🥉5 · ⭐ 1) - Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations.. MIT
+- Visual Graph Datasets (🥉5 · ⭐ 1) - Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations.. MIT XAI rep-learn
- linear-regression-benchmarks (🥉5 · ⭐ 1 · 💀) - Data sets used for linear regression benchmarks. MIT benchmarking single-paper
- paper-data-redundancy (🥉4 · ⭐ 7) - Repo for the paper Exploiting redundancy in large materials datasets for efficient machine learning with less data. BSD-3 small-data single-paper
- nep-data (🥉2 · ⭐ 12 · 💀) - Data related to the NEP machine-learned potential of GPUMD. Unlicensed ML-IAP MD transport-phenomena
+- tmQM_wB97MV Dataset (🥉2 · ⭐ 5 · ➕) - Code for Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV.. Unlicensed catalysis rep-learn
LGPL-3.0LGPL-3.0BSD-3 Rust C-lang C++ Pythonneural-operator pinn datasets single-paper
+
Apache-2BSD-3GPL-3.0 rep-learnGPL-3.0 rep-learnLGPL-3.0GPL-3.0 rep-learn magnetism C-langMIT excited-states general-toolApache-2
+- [GitHub](https://github.com/lcmd-epfl/Q-stack) (👨💻 7 · 🔀 5 · 📋 29 - 31% open · ⏱️ 19.07.2024):
+
+ ```
+ git clone https://github.com/lcmd-epfl/Q-stack
+ ```
+MIT rep-learnMIT rep-learnApache-2
- NeuralXC (🥈11 · ⭐ 33 · 💀) - Implementation of a machine learned density functional. BSD-3
- ACEhamiltonians (🥈10 · ⭐ 12 · 💀) - Provides tools for constructing, fitting, and predicting self-consistent Hamiltonian and overlap matrices in solid-.. MIT Julia
-- PROPhet (🥉9 · ⭐ 62 · 💀) - PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches. GPL-3.0 ML-IAP MD single-paper C++
+- PROPhet (🥈9 · ⭐ 62 · 💀) - PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches. GPL-3.0 ML-IAP MD single-paper C++
+- Mat2Spec (🥉7 · ⭐ 27 · 💀) - Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings. MIT spectroscopy
- Libnxc (🥉7 · ⭐ 16 · 💀) - A library for using machine-learned exchange-correlation functionals for density-functional theory. MPL-2.0 C++ Fortran
+- rho_learn (🥉7 · ⭐ 3 · ➕) - A proof-of-concept framework for torch-based learning of the electron density and related scalar fields. MIT
- DeepH-E3 (🥉6 · ⭐ 60 · 💀) - General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian. MIT magnetism
- DeepDFT (🥉6 · ⭐ 54 · 💀) - Official implementation of DeepDFT model. MIT
-- Mat2Spec (🥉6 · ⭐ 27 · 💀) - MIT spectroscopy
- xDeepH (🥉5 · ⭐ 31 · 💀) - Extended DeepH (xDeepH) method for magnetic materials. LGPL-3.0 magnetism Julia
- ML-DFT (🥉5 · ⭐ 23 · 💀) - A package for density functional approximation using machine learning. MIT
- charge-density-models (🥉4 · ⭐ 10 · 💤) - Tools to build charge density models using [fairchem](https://github.com/FAIR-Chem/fairchem). MIT rep-learn
@@ -557,7 +692,7 @@ _Projects and models that focus on quantities of DFT, such as density functional
- APET (🥉3 · ⭐ 4 · 💤) - Atomic Positional Embedding-based Transformer. GPL-3.0 density-of-states transformer
- CSNN (🥉3 · ⭐ 2 · 💀) - Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning. BSD-3
- MALADA (🥉3 · ⭐ 1 · 💀) - MALA Data Acquisition: Helpful tools to build data for MALA. BSD-3
-- A3MD (🥉2 · ⭐ 8 · 💀) - MPNN-like + Analytic Density Model = Accurate electron densities. Unlicensed representation-learning single-paper
+- A3MD (🥉2 · ⭐ 8 · 💀) - MPNN-like + Analytic Density Model = Accurate electron densities. Unlicensed rep-learn single-paper
- kdft (🥉1 · ⭐ 2 · 💀) - The Kernel Density Functional (KDF) code allows generating ML based DFT functionals. Unlicensed
- MLDensity ( ⭐ 2 · 💀) - Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure.. Unlicensed
community-resource rep-learn
🔗 AL4MS 2023 workshop tutorials active-learning
-NISTNISTMIT chemistryCCO-1.0MIT ML-IAP rep-learn MDBSD-3BSD-3MIT ML-IAP rep-learn MDMIT rep-learn
- Deep Learning for Molecules and Materials Book (🥇11 · ⭐ 600 · 💀) - Deep learning for molecules and materials book. Custom
@@ -637,15 +782,17 @@ _Tutorials, guides, cookbooks, recipes, etc._
- RDKit Tutorials (🥈8 · ⭐ 250 · 💀) - Tutorials to learn how to work with the RDKit. Custom
- MAChINE (🥉7 · ⭐ 1 · 💤) - Client-Server Web App to introduce usage of ML in materials science to beginners. MIT
- Applied AI for Materials (🥉6 · ⭐ 58 · 💀) - Course materials for Applied AI for Materials Science and Engineering. Unlicensed
+- ML for catalysis tutorials (🥉6 · ⭐ 8 · 💀) - A jupyter book repo for tutorial on how to use OCP ML models for catalysis. MIT
- AI4Science101 (🥉5 · ⭐ 83 · 💀) - AI for Science. Unlicensed
- Machine Learning for Materials Hard and Soft (🥉5 · ⭐ 34 · 💀) - ESI-DCAFM-TACO-VDSP Summer School on Machine Learning for Materials Hard and Soft. Unlicensed
- Data Handling, DoE and Statistical Analysis for Material Chemists (🥉5 · ⭐ 1 · 💀) - Notebooks for workshops of DoE course, hosted by the Computational Materials Chemistry group at Uppsala University. GPL-3.0
- ML-in-chemistry-101 (🥉4 · ⭐ 67 · 💀) - 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
+- AI4ChemMat Hands-On Series (🥉4 · ⭐ 1 · ➕) - Hands-On Series organized by Chemistry and Materials working group at Argonne Nat Lab. MPL-2.0
+- PiNN Lab (🥉3 · ⭐ 2 · 💀) - Material for running a lab session on atomic neural networks. GPL-3.0
- MLDensity_tutorial (🥉2 · ⭐ 7 · 💀) - Tutorial files to work with ML for the charge density in molecules and solids. Unlicensed
- LAMMPS-style pair potentials with GAP (🥉2 · ⭐ 4 · 💀) - 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
MIT
-- halex (🥈4 · ⭐ 2) - Hamiltonian Learning for Excited States https://doi.org/10.48550/arXiv.2311.00844. Unlicensed excited-states
+- QMLearn (🥈5 · ⭐ 11 · 💀) - Quantum Machine Learning by learning one-body reduced density matrices in the AO basis... MIT
+- q-pac (🥈5 · ⭐ 4 · 💀) - Kernel charge equilibration method. MIT electrostatics
+- halex (🥉4 · ⭐ 2) - Hamiltonian Learning for Excited States https://doi.org/10.48550/arXiv.2311.00844. Unlicensed excited-states
- e3psi (🥉3 · ⭐ 3 · 💤) - Equivariant machine learning library for learning from electronic structures. LGPL-3.0
MITBSD-3 C++GPL-2.0 MD ML-IAP rep-eng FortranGPL-3.0 license rep-learn generative ML-IAP MD ML-DFT ML-WFT biomoleculesGPL-3.0 license rep-learn generative ML-IAP MD ML-DFT ML-WFT biomoleculesMIT
-- Automatminer (🥉15 · ⭐ 130 · 💀) - An automatic engine for predicting materials properties. Custom
+- Automatminer (🥉15 · ⭐ 130 · 💀) - An automatic engine for predicting materials properties. Custom autoML
- AMPtorch (🥉11 · ⭐ 58 · 💀) - AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch. GPL-3.0
- OpenChem (🥉10 · ⭐ 670 · 💀) - OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research. MIT
- JAXChem (🥉7 · ⭐ 76 · 💀) - JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling. MIT
@@ -886,19 +1035,31 @@ _Projects that implement generative models for atomistic ML._
```
git clone https://github.com/GT4SD/gt4sd-core
```
-- [PyPi](https://pypi.org/project/gt4sd) (📥 620 / month):
+- [PyPi](https://pypi.org/project/gt4sd) (📥 660 / month):
```
pip install gt4sd
```
MITMIT transfer-learning pretrained transformerMITMIT vizApache-2 drug-discovery multimodal pretrained rep-learnMIT
- EDM (🥈10 · ⭐ 420 · 💀) - E(3) Equivariant Diffusion Model for Molecule Generation in 3D. MIT
- G-SchNet (🥉8 · ⭐ 130 · 💀) - G-SchNet - a generative model for 3d molecular structures. MIT
-- cG-SchNet (🥉8 · ⭐ 49 · 💀) - cG-SchNet - a conditional generative neural network for 3d molecular structures. MIT
+- cG-SchNet (🥉8 · ⭐ 50 · 💀) - cG-SchNet - a conditional generative neural network for 3d molecular structures. MIT
- bVAE-IM (🥉8 · ⭐ 11 · 💀) - Implementation of Chemical Design with GPU-based Ising Machine. MIT QML single-paper
- rxngenerator (🥉7 · ⭐ 11 · 💀) - A generative model for molecular generation via multi-step chemical reactions. MIT
- MolSLEPA (🥉5 · ⭐ 5 · 💀) - Interpretable Fragment-based Molecule Design with Self-learning Entropic Population Annealing. MIT XAI
+- Mapping out phase diagrams with generative classifiers (🥉4 · ⭐ 7 · 💀) - Repository for our ``Mapping out phase diagrams with generative models paper. MIT phase-transition
LGPL-3.0 C++MIT MD rep-learn transformer pretrainedMIT MD rep-learn transformer pretrainedMITGPL-3.0 MD C++ electrostaticsGPL-3.0 MD C++ electrostaticsUnlicensed pretrained rep-learn catalysisUnlicensed pretrained rep-learn catalysisMITMITMITMITMITCustomCustomMIT rep-learn transformerMIT rep-learn transformerMIT JuliaCustomCustom active-learningCustom active-learningMIT JuliaMITGPL-3.0Custom JuliaBSD-3 multifidelity
- n2p2 (🥈13 · ⭐ 220 · 💀) - n2p2 - A Neural Network Potential Package. GPL-3.0 C++
@@ -1316,23 +1498,28 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
- SchNet (🥉9 · ⭐ 210 · 💀) - SchNet - a deep learning architecture for quantum chemistry. MIT
- GemNet (🥉9 · ⭐ 180 · 💀) - GemNet model in PyTorch, as proposed in GemNet: Universal Directional Graph Neural Networks for Molecules (NeurIPS.. Custom
- ACE.jl (🥉9 · ⭐ 66 · 💀) - Parameterisation of Equivariant Properties of Particle Systems. Custom Julia
+- calorine (🥉9 · ⭐ 12 · 💀) - A Python package for constructing and sampling neuroevolution potential models. https://doi.org/10.21105/joss.06264. Custom
- AIMNet (🥉8 · ⭐ 94 · 💀) - Atoms In Molecules Neural Network Potential. MIT single-paper
- SNAP (🥉8 · ⭐ 36 · 💀) - Repository for spectral neighbor analysis potential (SNAP) model development. BSD-3
- Atomistic Adversarial Attacks (🥉8 · ⭐ 30 · 💀) - Code for performing adversarial attacks on atomistic systems using NN potentials. MIT probabilistic
-- calorine (🥉8 · ⭐ 12 · 💀) - A Python package for constructing and sampling neuroevolution potential models. https://doi.org/10.21105/joss.06264. Custom
-- PhysNet (🥉7 · ⭐ 89 · 💀) - Code for training PhysNet models. MIT electrostatics
-- SIMPLE-NN v2 (🥉7 · ⭐ 39 · 💤) - GPL-3.0
+- PhysNet (🥉7 · ⭐ 88 · 💀) - Code for training PhysNet models. MIT electrostatics
- MLIP-3 (🥉6 · ⭐ 25 · 💀) - MLIP-3: Active learning on atomic environments with Moment Tensor Potentials (MTP). BSD-2 C++
- 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 · ⭐ 9 · 💀) - A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic.. MIT benchmarking
+- GN-MM (🥉5 · ⭐ 10 · 💀) - The Gaussian Moment Neural Network (GM-NN) package developed for large-scale atomistic simulations employing atomistic.. MIT active-learning MD rep-eng magnetism
- Alchemical learning (🥉5 · ⭐ 2 · 💀) - Code for the Modeling high-entropy transition metal alloys with alchemical compression article. BSD-3
+- Allegro-Legato (🥉4 · ⭐ 19 · 💤) - An extension of Allegro with enhanced robustness and time-to-failure. MIT MD
- glp (🥉4 · ⭐ 17) - tools for graph-based machine-learning potentials in jax. MIT
- TensorPotential (🥉4 · ⭐ 7 · 💀) - Tensorpotential is a TensorFlow based tool for development, fitting ML interatomic potentials from electronic.. Custom
- ACE Workflows (🥉4 · 💤) - Workflow Examples for ACE Models. Unlicensed Julia workflows
- PeriodicPotentials (🥉4 · 💀) - A Periodic table app that displays potentials based on the selected elements. MIT community-resource viz JavaScript
+- MEGNetSparse (🥉3 · ⭐ 1 · 💤) - A library imlementing a graph neural network with sparse representation from Code for Kazeev, N., Al-Maeeni, A.R.,.. MIT material-defect
+- PyFLAME (🥉3 · 💀) - An automated approach for developing neural network interatomic potentials with FLAME.. Unlicensed active-learning structure-prediction structure-optimization rep-eng Fortran
- SingleNN (🥉2 · ⭐ 8 · 💀) - 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
+- AisNet (🥉2 · ⭐ 3 · 💀) - A Universal Interatomic Potential Neural Network with Encoded Local Environment Features.. MIT
- RuNNer (🥉2) - The RuNNer Neural Network Energy Representation is a Fortran-based framework for the construction of Behler-.. GPL-3.0 Fortran
+- nnp-pre-training (🥉1 · ⭐ 6 · 💤) - Synthetic pre-training for neural-network interatomic potentials. Unlicensed pretrained MD
+- mag-ace (🥉1 · ⭐ 2 · 💤) - Magnetic ACE potential. FORTRAN interface for LAMMPS SPIN package. Unlicensed magnetism MD Fortran
- mlp (🥉1 · ⭐ 1 · 💀) - Proper orthogonal descriptors for efficient and accurate interatomic potentials... Unlicensed Julia
Apache-2 ai-agentApache-2 ai-agentMIT ai-agentMIT ai-agentMIT datasetsMIT datasetsMIT generativeMITBSD-3 materials-discovery cheminformatics generative MD multimodal language-models Python general-toolBSD-3 materials-discovery cheminformatics generative MD multimodal language-models Python general-toolApache-2 generative multimodal pretrainedCC-BY-NC-4.0 materials-discoveryMIT magnetismgenerative proprietary
+
MITApache-2 UIP datasets rep-learn proprietaryApache-2 UIP datasets rep-learn proprietaryUnlicensed probabilistic
- AGOX (🥈7 · ⭐ 13 · 💀) - AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional.. GPL-3.0 structure-optimization
-- Computational Autonomy for Materials Discovery (CAMD) (🥉6 · ⭐ 1 · 💀) - Agent-based sequential learning software for materials discovery. Apache-2
+- Computational Autonomy for Materials Discovery (CAMD) (🥈6 · ⭐ 1 · 💀) - Agent-based sequential learning software for materials discovery. Apache-2
+- MAGUS (🥉4 · ⭐ 56 · 💀) - Machine learning And Graph theory assisted Universal structure Searcher. Unlicensed structure-prediction active-learning
- SPINNER (🥉4 · ⭐ 12 · 💀) - SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random.. GPL-3.0 C++ structure-prediction
+- ML-atomate (🥉4 · ⭐ 3 · 💤) - Machine learning-assisted Atomate code for autonomous computational materials screening. GPL-3.0 active-learning workflows
- closed-loop-acceleration-benchmarks (🥉4 · 💀) - Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational.. MIT materials-discovery active-learning single-paper
- sl_discovery (🥉3 · ⭐ 5 · 💀) - Data processing and models related to Quantifying the performance of machine learning models in materials discovery. Apache-2 materials-discovery single-paper
Apache-2MIT probabilistic active-learningMIT probabilistic active-learningMITMIT JuliaUnlicensed C++MIT rep-learn
-- cnine (🥉6 · ⭐ 4 · 📈) - Cnine tensor library. Unlicensed C++
-- EquivariantOperators.jl (🥉5 · ⭐ 18 · 💤) - MIT Julia
+- cnine (🥉6 · ⭐ 4) - Cnine tensor library. Unlicensed C++
- torch_spex (🥉3 · ⭐ 3) - Spherical expansions in PyTorch. Unlicensed
- Wigner Kernels (🥉2 · ⭐ 2 · 💀) - Collection of programs to benchmark Wigner kernels. Unlicensed benchmarking
MIT enhanced-samplingMIT enhanced-samplingCustom ML-IAP C++GPL-2.0GPL-2.0Custom ML-IAP C++MIT ML-IAPMIT ML-IAPApache-2Apache-2MIT pretrained small-data transfer-learningMIT pretrained small-data transfer-learningGPL-3.0Apache-2 FortranGPL-3.0BSD-3 Rust C++LGPL-3.0 C-langLGPL-3.0 C-langGPL-3.0 surface-science
+- CatLearn (🥇17 · ⭐ 99 · 💀) - GPL-3.0 surface-science
- cmlkit (🥈10 · ⭐ 34 · 💀) - tools for machine learning in condensed matter physics and quantum chemistry. MIT benchmarking
-- CBFV (🥉9 · ⭐ 23 · 💀) - Tool to quickly create a composition-based feature vector. Unlicensed
-- BenchML (🥉9 · ⭐ 15 · 💀) - ML benchmarking and pipeling framework. Apache-2 benchmarking
-- SkipAtom (🥉7 · ⭐ 23 · 💀) - Distributed representations of atoms, inspired by the Skip-gram model. MIT
+- CBFV (🥈9 · ⭐ 23 · 💀) - Tool to quickly create a composition-based feature vector. Unlicensed
+- BenchML (🥈9 · ⭐ 15 · 💀) - ML benchmarking and pipeling framework. Apache-2 benchmarking
+- SkipAtom (🥉8 · ⭐ 23 · 💀) - Distributed representations of atoms, inspired by the Skip-gram model. MIT
- milad (🥉6 · ⭐ 29 · 💀) - Moment Invariants Local Atomic Descriptor. GPL-3.0 generative
- fplib (🥉6 · ⭐ 7 · 💀) - a fingerprint library. MIT C-lang single-paper
- SOAPxx (🥉6 · ⭐ 7 · 💀) - A SOAP implementation. GPL-2.0 C++
- soap_turbo (🥉6 · ⭐ 4 · 💀) - soap_turbo comprises a series of libraries to be used in combination with QUIP/GAP and TurboGAP. Custom Fortran
- pyLODE (🥉6 · ⭐ 3 · 💀) - Pythonic implementation of LOng Distance Equivariants. Apache-2 electrostatics
+- MXenes4HER (🥉5 · ⭐ 5 · 💀) - Predicting hydrogen evolution (HER) activity over 4500 MXene materials https://doi.org/10.1039/D3TA00344B. GPL-3.0 materials-discovery catalysis scikit-learn single-paper
- SISSO++ (🥉5 · ⭐ 3 · 💀) - C++ Implementation of SISSO with python bindings. Apache-2 C++
+- automl-materials (🥉4 · ⭐ 5 · 💀) - AutoML for Regression Tasks on Small Tabular Data in Materials Design. MIT autoML benchmarking single-paper
- 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 (🥉3 · ⭐ 1 · 💀) - Machine Learning Predictions of High-Curie-Temperature Materials. MIT single-paper magnetism
- AMP (🥉2) - Amp is an open-source package designed to easily bring machine-learning to atomistic calculations. Unlicensed
@@ -1821,7 +2049,7 @@ _General models that learn a representations aka embeddings of atomistic systems
Apache-2MIT general-mlMITBSD-3 multifidelityCustomMIT pretrainedApache-2Apache-2MIT pretrainedMITMIT workflows benchmarkingMIT transformer pretrainedCustomBSD-3Custom workflowsMIT transformerMIT transformerApache-2 pretrained material-defectMITApache-2 pretrained material-defectMITMITMITApache-2
- benchmarking-gnns (🥈14 · ⭐ 2.5K · 💀) - Repository for benchmarking graph neural networks. MIT single-paper benchmarking
- Crystal Graph Convolutional Neural Networks (CGCNN) (🥈12 · ⭐ 620 · 💀) - Crystal graph convolutional neural networks for predicting material properties. MIT
- Neural fingerprint (nfp) (🥈12 · ⭐ 57 · 💀) - Keras layers for end-to-end learning with rdkit and pymatgen. Custom
- Compositionally-Restricted Attention-Based Network (CrabNet) (🥈12 · ⭐ 12 · 💀) - Predict materials properties using only the composition information!. MIT
+- pretrained-gnns (🥈10 · ⭐ 950 · 💀) - Strategies for Pre-training Graph Neural Networks. MIT pretrained
- GDC (🥈10 · ⭐ 260 · 💀) - Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019). MIT generative
-- SE(3)-Transformers (🥈9 · ⭐ 480 · 💀) - code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503. MIT single-paper transformer
-- molecularGNN_smiles (🥈9 · ⭐ 290 · 💀) - The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius.. Apache-2
-- GATGNN: Global Attention Graph Neural Network (🥈9 · ⭐ 67 · 💀) - Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials.. MIT
-- UVVisML (🥈9 · ⭐ 21 · 💀) - Predict optical properties of molecules with machine learning. MIT optical-properties single-paper probabilistic
+- SE(3)-Transformers (🥉9 · ⭐ 480 · 💀) - code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503. MIT single-paper transformer
+- molecularGNN_smiles (🥉9 · ⭐ 290 · 💀) - The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius.. Apache-2
+- GATGNN: Global Attention Graph Neural Network (🥉9 · ⭐ 67 · 💀) - Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials.. MIT
+- UVVisML (🥉9 · ⭐ 21 · 💀) - Predict optical properties of molecules with machine learning. MIT optical-properties single-paper probabilistic
- CGAT (🥉8 · ⭐ 25 · 💀) - Crystal graph attention neural networks for materials prediction. MIT
-- FAENet (🥉8 · ⭐ 25 · 💤) - MIT
- T-e3nn (🥉8 · ⭐ 8 · 💀) - Time-reversal Euclidean neural networks based on e3nn. MIT magnetism
+- tensorfieldnetworks (🥉7 · ⭐ 150 · 💀) - Rotation- and translation-equivariant neural networks for 3D point clouds. MIT
- DTNN (🥉7 · ⭐ 76 · 💀) - Deep Tensor Neural Network. MIT
- Cormorant (🥉7 · ⭐ 59 · 💀) - Codebase for Cormorant Neural Networks. Custom
- AdsorbML (🥉7 · ⭐ 35 · 💀) - MIT surface-science single-paper
- escnn_jax (🥉7 · ⭐ 26 · 💀) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. Custom
- ML4pXRDs (🥉7 · 💀) - Contains code to train neural networks based on simulated powder XRDs from synthetic crystals. MIT XRD single-paper
-- tensorfieldnetworks (🥉6 · ⭐ 150 · 💀) - MIT
- MACE-Layer (🥉6 · ⭐ 33 · 💀) - Higher order equivariant graph neural networks for 3D point clouds. MIT
- charge_transfer_nnp (🥉6 · ⭐ 28 · 💀) - Graph neural network potential with charge transfer. MIT electrostatics
- GLAMOUR (🥉6 · ⭐ 20 · 💀) - Graph Learning over Macromolecule Representations. MIT single-paper
- Autobahn (🥉5 · ⭐ 30 · 💀) - Repository for Autobahn: Automorphism Based Graph Neural Networks. MIT
+- FieldSchNet (🥉5 · ⭐ 16 · 💀) - Deep neural network for molecules in external fields. MIT
- SCFNN (🥉5 · ⭐ 15 · 💀) - Self-consistent determination of long-range electrostatics in neural network potentials. MIT C++ electrostatics single-paper
- CraTENet (🥉5 · ⭐ 13 · 💀) - An attention-based deep neural network for thermoelectric transport properties. MIT transport-phenomena
+- EGraFFBench (🥉5 · ⭐ 8 · 💤) - Unlicensed single-paper benchmarking ML-IAP
- Per-Site CGCNN (🥉5 · ⭐ 1 · 💀) - Crystal graph convolutional neural networks for predicting material properties. MIT pretrained single-paper
- Per-site PAiNN (🥉5 · ⭐ 1 · 💀) - Fork of PaiNN for PerovskiteOrderingGCNNs. MIT probabilistic pretrained single-paper
-- Atom2Vec (🥉4 · ⭐ 31) - Atom2Vec: a simple way to describe atoms for machine learning. Unlicensed
-- FieldSchNet (🥉4 · ⭐ 16 · 💀) - MIT
+- Atom2Vec (🥉4 · ⭐ 32) - Atom2Vec: a simple way to describe atoms for machine learning. Unlicensed
- Graph Transport Network (🥉4 · ⭐ 16 · 💀) - Graph transport network (GTN), as proposed in Scalable Optimal Transport in High Dimensions for Graph Distances,.. Custom transport-phenomena
- gkx: Green-Kubo Method in JAX (🥉4 · ⭐ 3) - Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast. MIT transport-phenomena
- atom_by_atom (🥉3 · ⭐ 6 · 💤) - Atom-by-atom design of metal oxide catalysts for the oxygen evolution reaction with Machine Learning. Unlicensed surface-science single-paper
@@ -2061,9 +2318,23 @@ _General models that learn a representations aka embeddings of atomistic systems
_Machine-learned interatomic potentials (ML-IAP) that have been trained on large, chemically and structural diverse datasets. For materials, this means e.g. datasets that include a majority of the periodic table._
-Custom ML-IAP MD pretrained electrostatics magnetism structure-relaxationML-IAP
+
+🔗 PreFerred Potential (PFP) - Universal neural network potential for material discovery https://doi.org/10.1038/s41467-022-30687-9. ML-IAP proprietary
+
+🔗 MatterSim - A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures https://doi.org/10.48550/arXiv.2405.04967. ML-IAP active-learning proprietary
+
+LGPL-3.0 ML-IAP pretrained workflows datasetsCustom ML-IAP MD pretrained electrostatics magnetism structure-relaxationGPL-3.0 ML-IAP MD pretrainedMIT ML-IAP pretrained rep-learn MDCC-BY-NC-4.0 pretrained ML-IAP general-toolBSD-3 ML-IAP pretrained
+- M3GNet (🥈17 · ⭐ 220 · 💀) - Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art.. BSD-3 ML-IAP pretrained
GPL-3.0 C++
-- Coarse-Graining-Auto-encoders (🥉4 · ⭐ 21 · 💀) - Unlicensed single-paper
-- paper-ml-robustness-material-property (🥉4 · ⭐ 4 · 💀) - BSD-3 datasets single-paper
+- Coarse-Graining-Auto-encoders (🥉5 · ⭐ 21 · 💀) - Implementation of coarse-graining Autoencoders. Unlicensed single-paper
+- paper-ml-robustness-material-property (🥉5 · ⭐ 4 · 💀) - A critical examination of robustness and generalizability of machine learning prediction of materials properties. BSD-3 datasets single-paper
- KmdPlus (🥉2 · ⭐ 3 · 💤) - This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with.. Unlicensed
- Descriptor Embedding and Clustering for Atomisitic-environment Framework (DECAF) ( ⭐ 2) - Provides a workflow to obtain clustering of local environments in dataset of structures. Unlicensed
MIT general-tool probabilisticMIT general-tool probabilisticBSD-3 JavaScriptEPL-2.0 MD generative JavaScriptEPL-2.0 MD generative JavaScriptMITCustomMIT rep-eng Julia
- SchNOrb (🥉5 · ⭐ 58 · 💀) - Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. MIT
MIT pretrained
+- KSR-DFT (🥇6 · ⭐ 4 · 💀) - Kohn-Sham regularizer for machine-learned DFT functionals. Apache-2
Apache-2 ai-agent
+- DScribe (🥇23 · ⭐ 390 · 📈) - DScribe is a python package for creating machine learning descriptors for atomistic systems. Apache-2
+- pymatviz (🥇21 · ⭐ 150 · 📈) - A toolkit for visualizations in materials informatics. MIT general-tool probabilistic
+- e3nn-jax (🥈20 · ⭐ 170 · 📈) - jax library for E3 Equivariant Neural Networks. Apache-2
+
+## 📉 Trending Down
+
+_Projects that have a lower project-quality score compared to the last update. There might be a variety of reasons such as decreased downloads or code activity._
+
+- dpdata (🥇23 · ⭐ 200 · 📉) - Manipulating multiple atomic simulation data formats, including DeePMD-kit, VASP, LAMMPS, ABACUS, etc. LGPL-3.0
+- Best-of Machine Learning with Python (🥇21 · ⭐ 16K · 📉) - A ranked list of awesome machine learning Python libraries. Updated weekly. CC-BY-4.0 general-ml Python
+- Open Databases Integration for Materials Design (OPTIMADE) (🥈17 · ⭐ 76 · 📉) - Specification of a common REST API for access to materials databases. CC-BY-4.0
+- openmm-torch (🥈16 · ⭐ 180 · 📉) - OpenMM plugin to define forces with neural networks. Custom ML-IAP C++
+- MatBench Discovery (🥈16 · ⭐ 82 · 📉) - An evaluation framework for machine learning models simulating high-throughput materials discovery. MIT datasets benchmarking model-repository
+
+## ➕ Added Projects
+
+_Projects that were recently added to this best-of list._
+
+- DPA-2 (🥇26 · ⭐ 1.4K · ➕) - Towards a universal large atomic model for molecular and material simulation https://doi.org/10.48550/arXiv.2312.15492. LGPL-3.0 ML-IAP pretrained workflows datasets
+- Graphormer (🥈16 · ⭐ 2K · ➕) - Graphormer is a general-purpose deep learning backbone for molecular modeling. MIT transformer pretrained
+- OpenML (🥈16 · ⭐ 660 · 💤) - Open Machine Learning. BSD-3 datasets
+- PMTransformer (🥇16 · ⭐ 82 · ➕) - Universal Transfer Learning in Porous Materials, including MOFs. MIT transfer-learning pretrained transformer
+- SevenNet (🥉14 · ⭐ 86 · ➕) - SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that.. GPL-3.0 ML-IAP MD pretrained
+- HydraGNN (🥈14 · ⭐ 56 · ➕) - Distributed PyTorch implementation of multi-headed graph convolutional neural networks. BSD-3
+- ChatMOF (🥈13 · ⭐ 53 · ➕) - Predict and Inverse design for metal-organic framework with large-language models (llms). MIT generative
+- MACE-MP (🥉12 · ⭐ 33 · ➕) - Pretrained foundation models for materials chemistry. MIT ML-IAP pretrained rep-learn MD
+- Neural-Network-Models-for-Chemistry (🥈11 · ⭐ 59 · ➕) - A collection of Nerual Network Models for chemistry. Unlicensed rep-learn
+- load-atoms (🥈11 · ⭐ 37 · ➕) - download and manipulate atomistic datasets. MIT data-structures
+- AI4Chemistry course (🥈10 · ⭐ 130 · ➕) - EPFL AI for chemistry course, Spring 2023. https://schwallergroup.github.io/ai4chem_course. MIT chemistry
+- HamGNN (🥈9 · ⭐ 49 · ➕) - An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix. GPL-3.0 rep-learn magnetism C-lang
+- AI for Science paper collection (🥉9 · ⭐ 43 · 🐣) - List the AI for Science papers accepted by top conferences. Apache-2
+- Q-stack (🥈9 · ⭐ 14 · ➕) - Stack of codes for dedicated pre- and post-processing tasks for Quantum Machine Learning (QML). MIT excited-states general-tool
+- MADICES Awesome Interoperability (🥉9 · ⭐ 1 · ➕) - Linked data interoperability resources of the Machine-actionable data interoperability for the chemical sciences.. MIT datasets
+- Awesome-Graph-Generation (🥉8 · ⭐ 260 · ➕) - A curated list of up-to-date graph generation papers and resources. Unlicensed rep-learn
+- Awesome Neural SBI (🥉8 · ⭐ 80 · ➕) - Community-sourced list of papers and resources on neural simulation-based inference. MIT active-learning
+- SiMGen (🥉8 · ⭐ 11 · ➕) - Zero Shot Molecular Generation via Similarity Kernels. MIT viz
+- Awesome-Crystal-GNNs (🥉7 · ⭐ 54 · ➕) - This repository contains a collection of resources and papers on GNN Models on Crystal Solid State Materials. MIT
+- AIS Square (🥉7 · ⭐ 10 · 💤) - A collaborative and open-source platform for sharing AI for Science datasets, models, and workflows. Home of the.. LGPL-3.0 community-resource model-repository
+- rho_learn (🥉7 · ⭐ 3 · ➕) - A proof-of-concept framework for torch-based learning of the electron density and related scalar fields. MIT
+- ChargE3Net (🥉6 · ⭐ 28 · ➕) - Higher-order equivariant neural networks for charge density prediction in materials. MIT rep-learn
+- ML for catalysis tutorials (🥉6 · ⭐ 8 · 💀) - A jupyter book repo for tutorial on how to use OCP ML models for catalysis. MIT
+- Cephalo (🥉6 · ⭐ 5 · 🐣) - Multimodal Vision-Language Models for Bio-Inspired Materials Analysis and Design. Apache-2 generative multimodal pretrained
+- KSR-DFT (🥇6 · ⭐ 4 · 💀) - Kohn-Sham regularizer for machine-learned DFT functionals. Apache-2
+- ACEpsi.jl (🥉6 · ⭐ 2 · 💤) - ACE wave function parameterizations. MIT rep-eng Julia
+- crystal-text-llm (🥉5 · ⭐ 63 · 🐣) - Large language models to generate stable crystals. CC-BY-NC-4.0 materials-discovery
+- The Perovskite Database Project (🥉5 · ⭐ 58 · ➕) - Perovskite Database Project aims at making all perovskite device data, both past and future, available in a form.. Unlicensed community-resource
+- Joint Multidomain Pre-Training (JMP) (🥉5 · ⭐ 32 · 🐣) - Code for From Molecules to Materials Pre-training Large Generalizable Models for Atomic Property Prediction. CC-BY-NC-4.0 pretrained ML-IAP general-tool
+- QMLearn (🥈5 · ⭐ 11 · 💀) - Quantum Machine Learning by learning one-body reduced density matrices in the AO basis... MIT
+- InfGCN for Electron Density Estimation (🥉5 · ⭐ 10 · 💤) - Official implementation of the NeurIPS 23 spotlight paper of InfGCN. MIT rep-learn
+- GN-MM (🥉5 · ⭐ 10 · 💀) - The Gaussian Moment Neural Network (GM-NN) package developed for large-scale atomistic simulations employing atomistic.. MIT active-learning MD rep-eng magnetism
+- EGraFFBench (🥉5 · ⭐ 8 · 💤) - Unlicensed single-paper benchmarking ML-IAP
+- GDB-9-Ex9 and ORNL_AISD-Ex (🥉5 · ⭐ 6 · 💤) - Distributed computing workflow for generation and analysis of large scale molecular datasets obtained running multi-.. Unlicensed
+- MXenes4HER (🥉5 · ⭐ 5 · 💀) - Predicting hydrogen evolution (HER) activity over 4500 MXene materials https://doi.org/10.1039/D3TA00344B. GPL-3.0 materials-discovery catalysis scikit-learn single-paper
+- Geometric-GNNs (🥉4 · ⭐ 85 · ➕) - List of Geometric GNNs for 3D atomic systems. Unlicensed datasets educational rep-learn
+- MAGUS (🥉4 · ⭐ 56 · 💀) - Machine learning And Graph theory assisted Universal structure Searcher. Unlicensed structure-prediction active-learning
+- Allegro-Legato (🥉4 · ⭐ 19 · 💤) - An extension of Allegro with enhanced robustness and time-to-failure. MIT MD
+- Mapping out phase diagrams with generative classifiers (🥉4 · ⭐ 7 · 💀) - Repository for our ``Mapping out phase diagrams with generative models paper. MIT phase-transition
+- automl-materials (🥉4 · ⭐ 5 · 💀) - AutoML for Regression Tasks on Small Tabular Data in Materials Design. MIT autoML benchmarking single-paper
+- ML-atomate (🥉4 · ⭐ 3 · 💤) - Machine learning-assisted Atomate code for autonomous computational materials screening. GPL-3.0 active-learning workflows
+- AI4ChemMat Hands-On Series (🥉4 · ⭐ 1 · ➕) - Hands-On Series organized by Chemistry and Materials working group at Argonne Nat Lab. MPL-2.0
+- ALEBREW (🥉3 · ⭐ 9 · 🐣) - Official repository for the paper Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic.. Custom ML-IAP MD
+- PyFLAME (🥉3 · 💀) - An automated approach for developing neural network interatomic potentials with FLAME.. Unlicensed active-learning structure-prediction structure-optimization rep-eng Fortran
+- tmQM_wB97MV Dataset (🥉2 · ⭐ 5 · ➕) - Code for Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV.. Unlicensed catalysis rep-learn
+- AisNet (🥉2 · ⭐ 3 · 💀) - A Universal Interatomic Potential Neural Network with Encoded Local Environment Features.. MIT
+- nnp-pre-training (🥉1 · ⭐ 6 · 💤) - Synthetic pre-training for neural-network interatomic potentials. Unlicensed pretrained MD
+- mag-ace (🥉1 · ⭐ 2 · 💤) - Magnetic ACE potential. FORTRAN interface for LAMMPS SPIN package. Unlicensed magnetism MD Fortran
+
diff --git a/history/2024-08-15_projects.csv b/history/2024-08-15_projects.csv
new file mode 100644
index 0000000..8e68f5e
--- /dev/null
+++ b/history/2024-08-15_projects.csv
@@ -0,0 +1,426 @@
+,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,updated_github_id,trending,new_addition,maven_id,maven_url,npm_id,npm_url,npm_monthly_downloads,gitlab_id,gitlab_url,docs_url,ignore
+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', 'pretrained', 'transformer']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3,GAP-ML.org community homepage,True,community,,https://gap-ml.org/,,0,True,['ml-iap'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4,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,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+5,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,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+6,Alexandria Materials Database,True,datasets,CC-BY-4.0,https://alexandria.icams.rub.de/,"A database of millions of theoretical crystal structures (3D, 2D and 1D) discovered by machine learning accelerated..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+7,Catalysis Hub,True,datasets,,https://www.catalysis-hub.org/,A web-platform for sharing data and software for computational catalysis research!.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+8,Citrination Datasets,True,datasets,MIT,https://citrination.com/,AI-Powered Materials Data Platform. Open Citrination has been decommissioned.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+9,crystals.ai,True,datasets,,https://crystals.ai/,Curated datasets for reproducible AI in materials science.,0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+10,DeepChem Models,True,datasets,,https://huggingface.co/DeepChem,DeepChem models on HuggingFace.,0,True,"['model-repository', 'pretrained', 'language-models']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+11,Graphs of Materials Project 20190401,True,datasets,MIT,https://figshare.com/articles/dataset/Graphs_of_Materials_Project_20190401/8097992,The dataset used to train the MEGNet interatomic potential.,0,True,['ml-iap'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+12,HME21 Dataset,True,datasets,,https://doi.org/10.6084/m9.figshare.19658538,High-temperature multi-element 2021 dataset for the PreFerred Potential (PFP)..,0,True,['uip'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+13,JARVIS-Leaderboard,True,datasets,https://github.com/usnistgov/jarvis_leaderboard/blob/main/LICENSE.rst,https://pages.nist.gov/jarvis_leaderboard/,Explore State-of-the-Art Materials Design Methods: https://www.nature.com/articles/s41524-024-01259-w.,0,True,"['model-repository', 'benchmarking', 'community', 'educational']",usnistgov/jarvis_leaderboard,https://github.com/usnistgov/jarvis_leaderboard,2022-07-15 16:48:33,2024-08-14 17:30:42,2024-08-14 04:18:28,823.0,11.0,42.0,5.0,320.0,6.0,2.0,55.0,2024-05-16 16:20:41,2024.4.26,28.0,33.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+14,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,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+15,Materials Project Trajectory (MPtrj) Dataset,True,datasets,MIT,https://figshare.com/articles/dataset/Materials_Project_Trjectory_MPtrj_Dataset/23713842,The dataset used to train the CHGNet universal potential.,0,True,['uip'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+16,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,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+17,MPF.2021.2.8,True,datasets,,https://figshare.com/articles/dataset/MPF_2021_2_8/19470599,The dataset used to train the M3GNet universal potential.,0,True,['uip'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+18,NRELMatDB,True,datasets,,https://materials.nrel.gov/,"Computational materials database with the specific focus on materials for renewable energy applications including, but..",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+19,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,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+20,sGDML Datasets,True,datasets,,http://sgdml.org/#datasets,"MD17, MD22, DFT datasets.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+21,MoleculeNet,True,datasets,,https://moleculenet.org/,A Benchmark for Molecular Machine Learning.,0,True,['benchmarking'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+22,ZINC15,True,datasets,,https://zinc15.docking.org/,A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable..,0,True,"['graph', 'biomolecules']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+23,ZINC20,True,datasets,,https://zinc.docking.org/,A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable..,0,True,"['graph', 'biomolecules']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+24,AI for Science 101,True,educational,,https://ai4science101.github.io/,,0,True,"['community', 'rep-learn']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+25,AL4MS 2023 workshop tutorials,True,educational,,https://sites.utu.fi/al4ms2023/media-and-tutorials/,,0,True,['active-learning'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+26,Quantum Chemistry in the Age of Machine Learning,True,educational,,https://www.elsevier.com/books-and-journals/book-companion/9780323900492,"Book, 2022.",0,True,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+27,MatterGen,True,materials-discovery,,https://www.microsoft.com/en-us/research/blog/mattergen-property-guided-materials-design/,A generative model for inorganic materials design https://doi.org/10.48550/arXiv.2312.03687.,0,True,"['generative', 'proprietary']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+28,IKS-PIML,True,ml-dft,,https://rodare.hzdr.de/record/2720,Code and generated data for the paper Inverting the Kohn-Sham equations with physics-informed machine learning..,0,True,"['neural-operator', 'pinn', 'datasets', 'single-paper']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+29,TeaNet,True,uip,,https://doi.org/10.24433/CO.0749085.v1,Universal neural network interatomic potential inspired by iterative electronic relaxations..,0,True,['ml-iap'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+30,PreFerred Potential (PFP),True,uip,,https://www.nature.com/articles/s41467-022-30687-9#code-availability,Universal neural network potential for material discovery https://doi.org/10.1038/s41467-022-30687-9.,0,True,"['ml-iap', 'proprietary']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+31,MatterSim,True,uip,,https://www.microsoft.com/en-us/research/blog/mattersim-a-deep-learning-model-for-materials-under-real-world-conditions/,"A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures https://doi.org/10.48550/arXiv.2405.04967.",0,True,"['ml-iap', 'active-learning', 'proprietary']",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+32,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.",39,True,,dmlc/dgl,https://github.com/dmlc/dgl,2018-04-20 14:49:09,2024-08-15 08:19:13,2024-08-15 08:19:13,4328.0,221.0,2920.0,172.0,4948.0,402.0,2340.0,13294.0,2024-06-28 00:16:49,2.3.0,106.0,295.0,dgl,dglteam/dgl,282.0,282.0,https://pypi.org/project/dgl,124311.0,129676.0,https://anaconda.org/dglteam/dgl,2024-06-28 00:12:10.120,364859.0,1.0,,,,,,,,,,,,,,,,,,
+33,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 23:20:28,2024-08-15 01:10:20,2024-08-12 17:31:56,10519.0,53.0,1596.0,145.0,2404.0,460.0,1236.0,5346.0,2024-04-03 16:21:23,2.8.0,20.0,247.0,deepchem,conda-forge/deepchem,415.0,415.0,https://pypi.org/project/deepchem,44420.0,46660.0,https://anaconda.org/conda-forge/deepchem,2024-04-05 16:46:45.105,108565.0,1.0,deepchemio/deepchem,https://hub.docker.com/r/deepchemio/deepchem,2024-08-12 21:19:09.208650,5.0,7413.0,,,,,,,,,,,,,
+34,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.,35,True,['general-ml'],pyg-team/pytorch_geometric,https://github.com/pyg-team/pytorch_geometric,2017-10-06 16:03:03,2024-08-15 09:07:02,2024-08-14 13:04:40,7594.0,74.0,3511.0,252.0,3085.0,850.0,2644.0,20787.0,2024-04-19 11:37:44,2.5.3,40.0,513.0,,,6260.0,6260.0,,,,,,,1.0,,,,,,,,,,,,,,,,,,
+35,RDKit,,general-tool,BSD-3-Clause,https://github.com/rdkit/rdkit,,32,True,['lang-cpp'],rdkit/rdkit,https://github.com/rdkit/rdkit,2013-05-12 06:19:15,2024-08-15 04:49:59,2024-08-13 13:39:06,7813.0,77.0,831.0,82.0,3189.0,923.0,2290.0,2565.0,2024-07-19 09:34:37,Release_2024_03_5,100.0,228.0,rdkit,rdkit/rdkit,3.0,3.0,https://pypi.org/project/rdkit,949635.0,970706.0,https://anaconda.org/rdkit/rdkit,2023-06-16 12:54:07.547,2569473.0,1.0,,,,,,1247.0,,,,,,,,,,,,
+36,e3nn,,rep-learn,MIT,https://github.com/e3nn/e3nn,A modular framework for neural networks with Euclidean symmetry.,28,True,,e3nn/e3nn,https://github.com/e3nn/e3nn,2020-01-31 13:06:42,2024-08-15 07:18:36,2024-07-26 23:00:39,2173.0,12.0,132.0,19.0,222.0,19.0,133.0,923.0,2024-07-27 03:01:58,0.5.2,29.0,31.0,e3nn,conda-forge/e3nn,281.0,281.0,https://pypi.org/project/e3nn,316367.0,317108.0,https://anaconda.org/conda-forge/e3nn,2023-06-18 08:41:30.723,20014.0,1.0,,,,,,,,,,,,,,,,,,
+37,paper-qa,,language-models,Apache-2.0,https://github.com/Future-House/paper-qa,LLM Chain for answering questions from documents with citations.,27,True,['ai-agent'],whitead/paper-qa,https://github.com/Future-House/paper-qa,2023-02-05 01:07:25,2024-08-14 21:02:09,2024-08-14 21:02:03,249.0,17.0,354.0,40.0,149.0,67.0,74.0,3824.0,2024-06-28 07:40:54,4.9.0,100.0,18.0,paper-qa,,65.0,65.0,https://pypi.org/project/paper-qa,8283.0,8283.0,,,,1.0,,,,,,,Future-House/paper-qa,1.0,,,,,,,,,,
+38,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.,27,True,['lang-cpp'],deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2024-08-14 02:32:59,2024-07-06 15:29:41,2533.0,32.0,476.0,47.0,1984.0,79.0,667.0,1429.0,2024-07-03 19:22:15,2.2.11,49.0,69.0,deepmd-kit,deepmodeling/deepmd-kit,16.0,16.0,https://pypi.org/project/deepmd-kit,1372.0,2095.0,https://anaconda.org/deepmodeling/deepmd-kit,2024-04-06 21:22:08.456,1208.0,1.0,deepmodeling/deepmd-kit,https://hub.docker.com/r/deepmodeling/deepmd-kit,2024-07-27 08:24:51.741318,1.0,2714.0,38192.0,,,,,,,,,,,,
+39,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 20:37:00,2024-08-12 08:05:29,2024-07-30 12:11:47,4157.0,2.0,184.0,29.0,721.0,23.0,197.0,457.0,2024-03-27 14:45:47,0.9.2,68.0,54.0,matminer,conda-forge/matminer,305.0,305.0,https://pypi.org/project/matminer,11837.0,13289.0,https://anaconda.org/conda-forge/matminer,2024-03-28 11:24:38.014,66804.0,1.0,,,,,,,,,,,,,,,,,,
+40,DPA-2,,uip,LGPL-3.0,https://github.com/deepmodeling/deepmd-kit,Towards a universal large atomic model for molecular and material simulation https://doi.org/10.48550/arXiv.2312.15492.,26,True,"['ml-iap', 'pretrained', 'workflows', 'datasets']",deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2024-08-14 02:32:59,2024-07-06 15:29:41,2533.0,32.0,476.0,47.0,1984.0,79.0,667.0,1428.0,2024-07-03 19:22:15,2.2.11,49.0,69.0,,,16.0,16.0,,,670.0,,,,1.0,,,,,,38192.0,,,True,,,,,,,,,
+41,SchNetPack,,rep-learn,MIT,https://github.com/atomistic-machine-learning/schnetpack,SchNetPack - Deep Neural Networks for Atomistic Systems.,26,True,,atomistic-machine-learning/schnetpack,https://github.com/atomistic-machine-learning/schnetpack,2018-09-03 15:44:35,2024-08-15 09:45:25,2024-08-15 09:45:22,1677.0,21.0,205.0,32.0,409.0,2.0,241.0,758.0,2023-09-29 14:31:19,2.0.4,8.0,35.0,schnetpack,,84.0,84.0,https://pypi.org/project/schnetpack,830.0,830.0,,,,1.0,,,,,,,,,,,,,,,,,,
+42,OPTIMADE Python tools,,datasets,MIT,https://github.com/Materials-Consortia/optimade-python-tools,Tools for implementing and consuming OPTIMADE APIs in Python.,26,True,,Materials-Consortia/optimade-python-tools,https://github.com/Materials-Consortia/optimade-python-tools,2018-06-05 21:00:07,2024-08-14 16:22:48,2024-08-12 10:25:07,1647.0,40.0,41.0,7.0,1692.0,88.0,348.0,64.0,2024-07-20 12:06:05,1.1.1,86.0,28.0,optimade,conda-forge/optimade,48.0,48.0,https://pypi.org/project/optimade,6388.0,8250.0,https://anaconda.org/conda-forge/optimade,2024-07-20 16:43:01.881,83813.0,1.0,,,,,,,,,,,,,,,,,,
+43,JAX-DFT,,ml-dft,Apache-2.0,https://github.com/google-research/google-research/tree/master/jax_dft,This library provides basic building blocks that can construct DFT calculations as a differentiable program.,25,True,,google-research/google-research,https://github.com/google-research/google-research,2018-10-04 18:42:48,2024-08-14 16:02:31,2024-08-14 16:02:23,4620.0,108.0,7712.0,748.0,860.0,902.0,324.0,33616.0,,,,796.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,
+44,JAX-MD,,md,Apache-2.0,https://github.com/jax-md/jax-md,"Differentiable, Hardware Accelerated, Molecular Dynamics.",25,True,,jax-md/jax-md,https://github.com/jax-md/jax-md,2019-05-13 21:03:37,2024-08-02 20:06:09,2024-08-02 20:06:09,920.0,30.0,182.0,48.0,169.0,69.0,79.0,1146.0,2022-11-27 12:42:00,jax-md-v0.2.24,7.0,33.0,jax-md,,53.0,53.0,https://pypi.org/project/jax-md,3223.0,3223.0,,,,1.0,,,,,,,,,,,,,,,,,,
+45,QUIP,,general-tool,GPL-2.0,https://github.com/libAtoms/QUIP,libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io.,25,True,"['md', 'ml-iap', 'rep-eng', 'lang-fortran']",libAtoms/QUIP,https://github.com/libAtoms/QUIP,2013-07-02 15:21:59,2024-08-15 08:19:16,2024-08-15 08:10:51,10864.0,25.0,124.0,26.0,176.0,103.0,358.0,350.0,2023-06-15 19:11:24,0.9.14,15.0,85.0,quippy-ase,,41.0,41.0,https://pypi.org/project/quippy-ase,4383.0,4465.0,,,,2.0,libatomsquip/quip,https://hub.docker.com/r/libatomsquip/quip,2023-04-24 21:25:17.345957,4.0,9905.0,532.0,,,,,,,,,,,,
+46,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.",25,True,,materialsvirtuallab/maml,https://github.com/materialsvirtuallab/maml,2020-01-25 15:04:21,2024-08-12 13:32:30,2024-07-03 18:08:37,1747.0,48.0,74.0,21.0,580.0,7.0,61.0,345.0,2024-06-13 15:24:45,2024.6.13,14.0,32.0,maml,,10.0,10.0,https://pypi.org/project/maml,175.0,175.0,,,,2.0,,,,,,,,,,,,,,,,,,
+47,NequIP,,ml-iap,MIT,https://github.com/mir-group/nequip,NequIP is a code for building E(3)-equivariant interatomic potentials.,24,True,,mir-group/nequip,https://github.com/mir-group/nequip,2021-03-15 23:44:39,2024-07-25 22:18:07,2024-07-09 15:58:45,1873.0,32.0,127.0,23.0,161.0,22.0,66.0,579.0,2024-07-09 16:05:06,0.6.1,16.0,11.0,nequip,conda-forge/nequip,23.0,23.0,https://pypi.org/project/nequip,3045.0,3240.0,https://anaconda.org/conda-forge/nequip,2024-07-10 05:13:00.157,5276.0,1.0,,,,,,,,,,,,,,,,,,
+48,cdk,,rep-eng,LGPL-2.1,https://github.com/cdk/cdk,The Chemistry Development Kit.,24,True,"['cheminformatics', 'lang-java']",cdk/cdk,https://github.com/cdk/cdk,2010-05-11 08:30:07,2024-08-09 05:46:33,2024-08-07 15:06:18,17638.0,51.0,154.0,40.0,813.0,31.0,255.0,485.0,2023-08-21 19:50:47,cdk-2.9,20.0,165.0,,,,,,,178.0,,,,1.0,,,,,,21228.0,,,,org.openscience.cdk:cdk-bundle,https://search.maven.org/artifact/org.openscience.cdk/cdk-bundle,,,,,,,
+49,AlphaFold,,biomolecules,Apache-2.0,https://github.com/google-deepmind/alphafold,Open source code for AlphaFold.,23,True,,google-deepmind/alphafold,https://github.com/google-deepmind/alphafold,2021-06-17 14:06:06,2024-06-27 04:19:33,2024-05-08 14:04:54,143.0,,2089.0,223.0,104.0,245.0,601.0,12204.0,2023-04-05 09:45:53,2.3.2,13.0,20.0,,,14.0,14.0,,,,,,,1.0,,,,,,,,,,,,,,,,,,
+50,dgl-lifesci,,rep-learn,Apache-2.0,https://github.com/awslabs/dgl-lifesci,Python package for graph neural networks in chemistry and biology.,23,False,,awslabs/dgl-lifesci,https://github.com/awslabs/dgl-lifesci,2020-04-23 07:14:21,2023-11-01 19:32:07,2023-04-16 03:55:52,236.0,,141.0,17.0,141.0,26.0,57.0,703.0,2023-02-13 08:45:17,0.3.2,8.0,22.0,dgllife,,225.0,225.0,https://pypi.org/project/dgllife,34603.0,34603.0,,,,1.0,,,,,,,,,,,,,,,,,,
+51,DScribe,,rep-eng,Apache-2.0,https://github.com/SINGROUP/dscribe,DScribe is a python package for creating machine learning descriptors for atomistic systems.,23,True,,SINGROUP/dscribe,https://github.com/SINGROUP/dscribe,2017-05-08 08:29:51,2024-08-13 20:50:00,2024-05-28 18:24:28,1288.0,1.0,86.0,20.0,27.0,6.0,92.0,388.0,,,15.0,18.0,dscribe,conda-forge/dscribe,193.0,193.0,https://pypi.org/project/dscribe,194120.0,196546.0,https://anaconda.org/conda-forge/dscribe,2024-05-28 23:16:49.298,118919.0,1.0,,,,,,,,1.0,,,,,,,,,,
+52,TorchMD-NET,,ml-iap,MIT,https://github.com/torchmd/torchmd-net,Training neural network potentials.,23,True,"['md', 'rep-learn', 'transformer', 'pretrained']",torchmd/torchmd-net,https://github.com/torchmd/torchmd-net,2021-04-09 16:16:32,2024-08-12 11:10:08,2024-07-29 09:38:50,1269.0,55.0,69.0,11.0,229.0,20.0,84.0,308.0,2024-07-09 09:20:46,2.3.0,25.0,16.0,,conda-forge/torchmd-net,,,,,10387.0,https://anaconda.org/conda-forge/torchmd-net,2024-07-09 18:53:56.674,103877.0,1.0,,,,,,,,,,,,,,,,,,
+53,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:..,23,True,,usnistgov/jarvis,https://github.com/usnistgov/jarvis,2017-06-22 19:34:02,2024-08-04 13:38:34,2024-06-28 03:35:23,2106.0,1.0,120.0,26.0,235.0,44.0,45.0,292.0,2024-06-28 03:36:55,2024.5.10,75.0,15.0,jarvis-tools,conda-forge/jarvis-tools,92.0,92.0,https://pypi.org/project/jarvis-tools,24147.0,25738.0,https://anaconda.org/conda-forge/jarvis-tools,2024-06-28 03:50:37.135,73186.0,2.0,,,,,,,,,,,,,,,,,,
+54,MatGL (Materials Graph Library),,rep-learn,BSD-3-Clause,https://github.com/materialsvirtuallab/matgl,Graph deep learning library for materials.,23,True,['multifidelity'],materialsvirtuallab/matgl,https://github.com/materialsvirtuallab/matgl,2022-08-29 18:36:05,2024-08-15 05:01:44,2024-08-15 05:01:44,1027.0,66.0,58.0,12.0,216.0,2.0,85.0,249.0,2024-08-07 12:24:58,1.1.3,31.0,17.0,m3gnet,,44.0,44.0,https://pypi.org/project/m3gnet,667.0,667.0,,,,1.0,,,,,,,,,,,,,,,,,,
+55,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 13:24:23,2024-08-13 00:50:09,2024-06-06 00:03:38,724.0,6.0,122.0,9.0,477.0,16.0,80.0,195.0,2024-06-06 00:06:28,0.2.19,26.0,60.0,dpdata,deepmodeling/dpdata,122.0,122.0,https://pypi.org/project/dpdata,30991.0,30997.0,https://anaconda.org/deepmodeling/dpdata,2023-09-27 20:07:36.945,218.0,1.0,,,,,,,,-1.0,,,,,,,,,,
+56,MEGNet,,ml-iap,BSD-3-Clause,https://github.com/materialsvirtuallab/megnet,Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals.,22,False,['multifidelity'],materialsvirtuallab/megnet,https://github.com/materialsvirtuallab/megnet,2018-12-12 21:31:28,2023-04-27 02:39:17,2023-04-27 02:39:17,1146.0,,152.0,25.0,314.0,17.0,57.0,494.0,2022-11-16 21:24:36,1.3.2,34.0,14.0,megnet,,80.0,80.0,https://pypi.org/project/megnet,389.0,389.0,,,,1.0,,,,,,,,,,,,,,,,,,
+57,TorchANI,,ml-iap,MIT,https://github.com/aiqm/torchani,Accurate Neural Network Potential on PyTorch.,22,True,,aiqm/torchani,https://github.com/aiqm/torchani,2018-04-02 15:43:04,2024-07-19 17:54:52,2023-11-14 16:32:59,434.0,,124.0,31.0,483.0,24.0,144.0,455.0,2023-11-14 16:38:04,2.2.4,24.0,17.0,torchani,conda-forge/torchani,39.0,39.0,https://pypi.org/project/torchani,3920.0,12326.0,https://anaconda.org/conda-forge/torchani,2024-05-31 06:36:10.067,403533.0,1.0,,,,,,,,,,,,,,,,,,
+58,MACE,,ml-iap,MIT,https://github.com/ACEsuit/mace,MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.,22,True,,ACEsuit/mace,https://github.com/ACEsuit/mace,2022-06-21 18:44:34,2024-08-13 16:18:00,2024-08-12 10:01:02,699.0,58.0,169.0,24.0,219.0,46.0,168.0,445.0,2024-07-16 10:55:30,0.3.6,7.0,35.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,
+59,GPUMD,,ml-iap,GPL-3.0,https://github.com/brucefan1983/GPUMD,GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials..,22,True,"['md', 'lang-cpp', 'electrostatics']",brucefan1983/GPUMD,https://github.com/brucefan1983/GPUMD,2017-07-14 15:32:56,2024-08-15 06:41:50,2024-08-15 06:41:47,3940.0,211.0,111.0,28.0,510.0,15.0,160.0,428.0,2024-05-15 13:24:38,3.9.4,41.0,30.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,
+60,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.,22,True,['workflows'],deepmodeling/dpgen,https://github.com/deepmodeling/dpgen,2019-06-13 11:43:56,2024-08-13 00:50:19,2024-04-10 06:31:36,2083.0,,167.0,13.0,835.0,31.0,261.0,290.0,2024-04-10 06:37:07,0.12.1,18.0,64.0,dpgen,deepmodeling/dpgen,6.0,6.0,https://pypi.org/project/dpgen,317.0,352.0,https://anaconda.org/deepmodeling/dpgen,2023-06-16 19:27:03.566,204.0,1.0,,,,,,1745.0,,,,,,,,,,,,
+61,CHGNet,,uip,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.,22,True,"['ml-iap', 'md', 'pretrained', 'electrostatics', 'magnetism', 'structure-relaxation']",CederGroupHub/chgnet,https://github.com/CederGroupHub/chgnet,2023-02-24 23:44:24,2024-08-09 02:58:00,2024-08-01 11:11:48,413.0,20.0,58.0,5.0,89.0,3.0,48.0,217.0,2024-06-05 16:28:25,0.3.8,16.0,7.0,chgnet,,30.0,30.0,https://pypi.org/project/chgnet,31347.0,31347.0,,,,2.0,,,,,,,,,,,,,,,,,,
+62,MPContribs,,datasets,MIT,https://github.com/materialsproject/MPContribs,Platform for materials scientists to contribute and disseminate their materials data through Materials Project.,22,True,,materialsproject/MPContribs,https://github.com/materialsproject/MPContribs,2014-12-11 18:25:27,2024-08-12 17:10:18,2024-08-12 17:10:17,5576.0,41.0,19.0,10.0,1710.0,20.0,78.0,34.0,2024-06-20 22:37:13,5.8.4,78.0,25.0,mpcontribs-client,,38.0,38.0,https://pypi.org/project/mpcontribs-client,1261.0,1261.0,,,,1.0,,,,,,,,,,,,,,,,,,
+63,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.,21,True,"['general-ml', 'lang-py']",ml-tooling/best-of-ml-python,https://github.com/ml-tooling/best-of-ml-python,2020-11-29 19:41:36,2024-07-22 15:36:22,2024-07-22 15:36:22,496.0,9.0,2257.0,409.0,266.0,19.0,34.0,16172.0,2024-06-06 15:57:10,2024.06.06,100.0,46.0,,,,,,,,,,,1.0,,,,,,,,-1.0,,,,,,,,,,
+64,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 17:04:05,2024-08-12 14:01:29,2024-04-04 13:37:56,1437.0,,3022.0,300.0,538.0,260.0,570.0,13111.0,,,,115.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+65,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 03:51:15,2024-07-15 07:18:56,2024-02-04 20:37:53,1083.0,,278.0,29.0,41.0,31.0,176.0,1827.0,2023-04-07 20:33:15,1.1.0,10.0,50.0,dive-into-graphs,,,,https://pypi.org/project/dive-into-graphs,435.0,435.0,,,,2.0,,,,,,,,,,,,,,,,,,
+66,FAIR Chemistry datasets,,datasets,MIT,https://github.com/FAIR-Chem/fairchem,"Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project.",21,True,['catalysis'],FAIR-Chem/fairchem,https://github.com/FAIR-Chem/fairchem,2019-09-26 04:47:27,2024-08-15 02:44:15,2024-08-14 19:31:46,820.0,59.0,230.0,23.0,616.0,8.0,187.0,746.0,2024-08-14 16:52:55,fairchem_demo_ocpapi-0.1.0,8.0,41.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,
+67,pymatviz,,visualization,MIT,https://github.com/janosh/pymatviz,A toolkit for visualizations in materials informatics.,21,True,"['general-tool', 'probabilistic']",janosh/pymatviz,https://github.com/janosh/pymatviz,2021-02-21 12:40:34,2024-08-08 09:44:49,2024-08-07 20:43:20,323.0,50.0,12.0,7.0,150.0,10.0,31.0,150.0,2024-07-31 23:17:33,0.10.0,26.0,7.0,pymatviz,,8.0,8.0,https://pypi.org/project/pymatviz,2142.0,2142.0,,,,1.0,,,,,,,,1.0,,,,,,,,,,
+68,Metatensor,,data-structures,BSD-3-Clause,https://github.com/lab-cosmo/metatensor,Self-describing sparse tensor data format for atomistic machine learning and beyond.,21,True,"['lang-rust', 'lang-c', 'lang-cpp', 'lang-py']",lab-cosmo/metatensor,https://github.com/lab-cosmo/metatensor,2022-03-01 15:58:28,2024-08-15 09:48:55,2024-08-15 08:46:03,774.0,67.0,15.0,18.0,514.0,74.0,128.0,47.0,2024-07-15 12:55:26,metatensor-torch-v0.5.3,38.0,21.0,,,11.0,11.0,,,2431.0,,,,2.0,,,,,,24318.0,,,,,,,,,,,,
+69,DM21,,ml-dft,Apache-2.0,https://github.com/google-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,False,,google-deepmind/deepmind-research,https://github.com/google-deepmind/deepmind-research,2019-01-15 09:54:13,2024-08-09 16:59:25,2023-06-02 17:04:50,369.0,,2532.0,325.0,236.0,177.0,138.0,13011.0,,,,92.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,
+70,FLARE,,active-learning,MIT,https://github.com/mir-group/flare,An open-source Python package for creating fast and accurate interatomic potentials.,20,True,"['lang-cpp', 'ml-iap']",mir-group/flare,https://github.com/mir-group/flare,2018-08-30 23:40:56,2024-06-11 22:18:56,2024-06-11 15:13:36,4399.0,7.0,64.0,20.0,187.0,33.0,170.0,283.0,2024-03-25 15:48:12,1.3.0,6.0,36.0,,,11.0,11.0,,,0.0,,,,1.0,,,,,,7.0,,,,,,,,,,,,
+71,ALIGNN,,rep-learn,https://github.com/usnistgov/alignn/blob/main/LICENSE.rst,https://github.com/usnistgov/alignn,Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en.,20,True,,usnistgov/alignn,https://github.com/usnistgov/alignn,2021-04-19 20:08:09,2024-07-29 12:39:30,2024-07-15 20:09:42,707.0,15.0,77.0,10.0,107.0,33.0,24.0,204.0,2024-06-28 03:29:50,2024.5.27,46.0,7.0,alignn,,14.0,14.0,https://pypi.org/project/alignn,870.0,870.0,,,,2.0,,,,,,,,,,,,,,,,,,
+72,e3nn-jax,,rep-learn,Apache-2.0,https://github.com/e3nn/e3nn-jax,jax library for E3 Equivariant Neural Networks.,20,True,,e3nn/e3nn-jax,https://github.com/e3nn/e3nn-jax,2021-06-08 13:21:51,2024-08-14 05:14:56,2024-08-14 05:13:07,1000.0,15.0,18.0,12.0,48.0,1.0,19.0,173.0,2024-08-14 05:14:56,0.20.7,43.0,7.0,e3nn-jax,,36.0,36.0,https://pypi.org/project/e3nn-jax,2716.0,2716.0,,,,2.0,,,,,,,,2.0,,,,,,,,,,
+73,fairchem,,ml-iap,,https://github.com/FAIR-Chem/fairchem,FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project (ocp).,19,True,"['pretrained', 'rep-learn', 'catalysis']",FAIR-Chem/fairchem,https://github.com/FAIR-Chem/fairchem,2019-09-26 04:47:27,2024-08-15 02:44:15,2024-08-14 19:31:46,820.0,59.0,230.0,23.0,616.0,8.0,187.0,746.0,2024-08-14 16:52:55,fairchem_demo_ocpapi-0.1.0,8.0,41.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+74,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 17:56:06,2024-06-02 00:38:18,2023-12-04 18:03:57,189.0,,41.0,9.0,77.0,11.0,58.0,278.0,2023-06-19 20:50:51,3.0.3,27.0,7.0,exmol,,20.0,20.0,https://pypi.org/project/exmol,637.0,637.0,,,,1.0,,,,,,,,,,,,,,,,,,
+75,KFAC-JAX,,math,Apache-2.0,https://github.com/google-deepmind/kfac-jax,Second Order Optimization and Curvature Estimation with K-FAC in JAX.,19,True,,google-deepmind/kfac-jax,https://github.com/google-deepmind/kfac-jax,2022-03-18 10:19:24,2024-08-15 06:27:08,2024-08-12 15:12:14,220.0,22.0,16.0,12.0,243.0,5.0,9.0,225.0,2024-04-03 17:12:00,0.0.6,5.0,13.0,kfac-jax,,10.0,10.0,https://pypi.org/project/kfac-jax,667.0,667.0,,,,1.0,,,,,,,,,,,,,,,,,,
+76,Chemiscope,,visualization,BSD-3-Clause,https://github.com/lab-cosmo/chemiscope,An interactive structure/property explorer for materials and molecules.,19,True,['lang-js'],lab-cosmo/chemiscope,https://github.com/lab-cosmo/chemiscope,2019-10-03 09:59:42,2024-08-13 16:40:24,2024-08-05 21:35:00,730.0,22.0,29.0,19.0,238.0,33.0,86.0,122.0,2024-06-14 16:42:03,0.7.1,16.0,22.0,,,6.0,6.0,,,50.0,,,,3.0,,,,,,246.0,,,,,,chemiscope,https://www.npmjs.com/package/chemiscope,46.0,,,,
+77,MAST-ML,,general-tool,MIT,https://github.com/uw-cmg/MAST-ML,MAterials Simulation Toolkit for Machine Learning (MAST-ML).,19,True,,uw-cmg/MAST-ML,https://github.com/uw-cmg/MAST-ML,2017-02-16 17:03:57,2024-08-05 20:49:39,2024-04-17 20:51:19,3296.0,,58.0,14.0,37.0,22.0,191.0,101.0,2024-04-17 21:30:14,3.2.0,7.0,18.0,,,43.0,43.0,,,2.0,,,,2.0,,,,,,95.0,,,,,,,,,,,,
+78,mlcolvar,,md,MIT,https://github.com/luigibonati/mlcolvar,A unified framework for machine learning collective variables for enhanced sampling simulations.,19,True,['enhanced-sampling'],luigibonati/mlcolvar,https://github.com/luigibonati/mlcolvar,2021-09-21 21:32:04,2024-07-31 15:12:33,2024-06-14 14:38:42,1089.0,58.0,23.0,7.0,80.0,13.0,57.0,91.0,2024-06-12 17:03:44,1.1.1,10.0,8.0,mlcolvar,,2.0,2.0,https://pypi.org/project/mlcolvar,154.0,154.0,,,,2.0,,,,,,,,,,,,,,,,,,
+79,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.,19,True,,mala-project/mala,https://github.com/mala-project/mala,2021-03-31 11:40:38,2024-08-14 11:15:04,2024-07-04 09:53:01,2307.0,92.0,23.0,9.0,303.0,37.0,230.0,80.0,2024-02-01 08:57:56,1.2.1,9.0,44.0,,,1.0,1.0,,,,,,,1.0,,,,,,,,,,,,,,,,,,
+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..,19,True,['scikit-learn'],scikit-learn-contrib/scikit-matter,https://github.com/scikit-learn-contrib/scikit-matter,2020-10-12 19:23:26,2024-08-12 16:25:31,2024-08-06 07:51:07,386.0,18.0,20.0,17.0,162.0,13.0,56.0,72.0,2023-08-24 17:18:49,0.2.0,7.0,15.0,skmatter,conda-forge/skmatter,10.0,10.0,https://pypi.org/project/skmatter,1134.0,1246.0,https://anaconda.org/conda-forge/skmatter,2023-08-24 19:08:29.551,1918.0,2.0,,,,,,,,,,,,,,,,,,
+81,ZnDraw,,visualization,EPL-2.0,https://github.com/zincware/ZnDraw,"A powerful tool for visualizing, modifying, and analysing atomistic systems.",19,True,"['md', 'generative', 'lang-js']",zincware/ZnDraw,https://github.com/zincware/ZnDraw,2023-04-12 15:01:21,2024-08-15 09:04:27,2024-08-14 07:17:45,381.0,81.0,2.0,1.0,341.0,68.0,208.0,29.0,2024-08-14 07:17:12,0.4.6,24.0,7.0,zndraw,,3.0,3.0,https://pypi.org/project/zndraw,1112.0,1112.0,,,,3.0,,,,,,,,,,,,,,,,,,
+82,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 14:48:35,2024-08-04 17:35:07,2024-08-04 17:35:03,7711.0,31.0,736.0,247.0,21.0,,14.0,4711.0,,,,12.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,
+83,Uni-Mol,,rep-learn,MIT,https://github.com/deepmodeling/Uni-Mol,Official Repository for the Uni-Mol Series Methods.,18,True,['pretrained'],deepmodeling/Uni-Mol,https://github.com/deepmodeling/Uni-Mol,2022-05-22 13:26:41,2024-08-14 10:40:57,2024-08-14 10:40:52,130.0,21.0,116.0,15.0,111.0,59.0,88.0,637.0,2024-07-06 07:05:10,0.2.1,3.0,16.0,,,,,,,659.0,,,,2.0,,,,,,14504.0,,,,,,,,,,,,
+84,GT4SD,,generative,MIT,https://github.com/GT4SD/gt4sd-core,"GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process.",18,True,"['pretrained', 'drug-discovery', 'rep-learn']",GT4SD/gt4sd-core,https://github.com/GT4SD/gt4sd-core,2022-02-11 19:06:58,2024-07-04 11:11:27,2024-07-04 11:11:26,296.0,8.0,66.0,17.0,148.0,1.0,98.0,326.0,2024-06-13 15:18:45,1.4.1,57.0,20.0,gt4sd,,,,https://pypi.org/project/gt4sd,661.0,661.0,,,,1.0,,,,,,,,,,,,,,,,,,
+85,ATOM3D,,datasets,MIT,https://github.com/drorlab/atom3d,ATOM3D: tasks on molecules in three dimensions.,18,False,"['biomolecules', 'benchmarking']",drorlab/atom3d,https://github.com/drorlab/atom3d,2020-04-03 22:53:11,2023-03-02 18:21:02,2023-03-02 18:20:29,798.0,,35.0,17.0,6.0,21.0,40.0,294.0,2022-07-20 00:56:05,0.2.6,15.0,10.0,atom3d,,40.0,40.0,https://pypi.org/project/atom3d,521.0,521.0,,,,2.0,,,,,,,,,,,,,,,,,,
+86,Neural Force Field,,ml-iap,MIT,https://github.com/learningmatter-mit/NeuralForceField,Neural Network Force Field based on PyTorch.,18,True,['pretrained'],learningmatter-mit/NeuralForceField,https://github.com/learningmatter-mit/NeuralForceField,2020-10-04 15:17:41,2024-08-11 22:33:54,2024-07-24 20:30:53,3121.0,76.0,47.0,7.0,5.0,1.0,18.0,223.0,2024-05-29 21:15:00,1.0.0,1.0,40.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+87,MatBench,,community,MIT,https://github.com/materialsproject/matbench,Matbench: Benchmarks for materials science property prediction.,18,True,"['datasets', 'benchmarking', 'model-repository']",materialsproject/matbench,https://github.com/materialsproject/matbench,2021-02-24 03:58:42,2024-06-26 23:31:08,2024-01-20 09:41:36,772.0,,42.0,8.0,297.0,31.0,26.0,105.0,2022-07-27 04:40:26,0.6,5.0,25.0,matbench,,16.0,16.0,https://pypi.org/project/matbench,501.0,501.0,,,,1.0,,,,,,,,,,,,,,,,,,
+88,kgcnn,,rep-learn,MIT,https://github.com/aimat-lab/gcnn_keras,"Graph convolutions in Keras with TensorFlow, PyTorch or Jax.",18,True,,aimat-lab/gcnn_keras,https://github.com/aimat-lab/gcnn_keras,2020-07-17 11:12:46,2024-05-06 10:08:41,2024-05-06 10:08:14,3099.0,,29.0,7.0,30.0,12.0,74.0,103.0,2024-02-27 12:21:46,4.0.1,26.0,7.0,kgcnn,,18.0,18.0,https://pypi.org/project/kgcnn,229.0,229.0,,,,2.0,,,,,,,,,,,,,,,,,,
+89,apax,,ml-iap,MIT,https://github.com/apax-hub/apax,A flexible and performant framework for training machine learning potentials.,18,True,,apax-hub/apax,https://github.com/apax-hub/apax,2022-11-18 12:31:19,2024-08-12 22:21:46,2024-08-09 15:26:47,1705.0,245.0,1.0,4.0,209.0,14.0,101.0,14.0,2024-08-09 20:31:11,0.6.0,6.0,7.0,apax,,2.0,2.0,https://pypi.org/project/apax,310.0,310.0,,,,2.0,,,,,,,,,,,,,,,,,,
+90,DeepQMC,,ml-wft,MIT,https://github.com/deepqmc/deepqmc,Deep learning quantum Monte Carlo for electrons in real space.,17,True,,deepqmc/deepqmc,https://github.com/deepqmc/deepqmc,2019-12-06 14:50:59,2024-08-14 09:04:29,2024-08-14 09:04:27,1461.0,1.0,58.0,23.0,156.0,5.0,39.0,340.0,2023-11-20 10:09:02,1.1.2,10.0,13.0,deepqmc,,2.0,2.0,https://pypi.org/project/deepqmc,100.0,100.0,,,,1.0,,,,,,,,,,,,,,,,,,
+91,M3GNet,,uip,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,False,"['ml-iap', 'pretrained']",materialsvirtuallab/m3gnet,https://github.com/materialsvirtuallab/m3gnet,2022-01-18 18:10:58,2023-06-06 23:56:08,2023-06-06 23:56:03,261.0,,58.0,11.0,33.0,15.0,20.0,225.0,2022-11-17 23:25:35,0.2.4,16.0,15.0,m3gnet,,23.0,23.0,https://pypi.org/project/m3gnet,667.0,667.0,,,,2.0,,,,,,,,,,,,,,,,,,
+92,FitSNAP,,md,GPL-2.0,https://github.com/FitSNAP/FitSNAP,Software for generating SNAP machine-learning interatomic potentials.,17,True,,FitSNAP/FitSNAP,https://github.com/FitSNAP/FitSNAP,2019-09-12 14:46:18,2024-08-09 16:04:42,2024-08-09 16:04:41,1388.0,9.0,48.0,7.0,182.0,12.0,56.0,142.0,2023-06-28 16:00:48,3.1.0,7.0,24.0,,conda-forge/fitsnap3,2.0,2.0,,,168.0,https://anaconda.org/conda-forge/fitsnap3,2023-06-16 00:19:04.615,7583.0,2.0,,,,,,9.0,,,,,,,,,,,,
+93,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..,17,True,"['workflows', 'benchmarking']",IntelLabs/matsciml,https://github.com/IntelLabs/matsciml,2022-09-13 20:27:28,2024-08-14 22:46:53,2024-08-14 22:46:53,2373.0,231.0,17.0,5.0,214.0,19.0,35.0,134.0,2023-08-31 23:59:40,1.0.0,2.0,11.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+94,DADApy,,unsupervised,Apache-2.0,https://github.com/sissa-data-science/DADApy,Distance-based Analysis of DAta-manifolds in python.,17,True,,sissa-data-science/DADApy,https://github.com/sissa-data-science/DADApy,2021-02-16 17:45:23,2024-08-08 11:25:55,2024-07-24 22:45:27,825.0,36.0,16.0,7.0,110.0,5.0,27.0,101.0,2024-07-02 15:52:45,0.3.1,5.0,19.0,dadapy,,7.0,7.0,https://pypi.org/project/dadapy,134.0,134.0,,,,1.0,,,,,,,,,,,,,,,,,,
+95,CatLearn,,rep-eng,GPL-3.0,https://github.com/SUNCAT-Center/CatLearn,,17,False,['surface-science'],SUNCAT-Center/CatLearn,https://github.com/SUNCAT-Center/CatLearn,2018-04-20 04:16:14,2024-06-28 07:53:45,2023-02-07 09:31:25,1960.0,,50.0,19.0,80.0,9.0,17.0,99.0,2020-03-27 09:26:03,0.6.2,8.0,22.0,catlearn,,5.0,5.0,https://pypi.org/project/catlearn,180.0,180.0,,,,1.0,,,,,,,,,,,,,,,,,,
+96,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.,17,True,,Materials-Consortia/OPTIMADE,https://github.com/Materials-Consortia/OPTIMADE,2018-01-08 23:32:29,2024-07-29 21:09:07,2024-06-12 09:31:09,1786.0,2.0,35.0,21.0,297.0,64.0,169.0,76.0,2024-06-10 16:32:29,1.2.0,9.0,21.0,,,,,,,,,,,2.0,,,,,,,,-1.0,,,,,,,,,,
+97,Graphormer,,rep-learn,MIT,https://github.com/microsoft/Graphormer,Graphormer is a general-purpose deep learning backbone for molecular modeling.,16,True,"['transformer', 'pretrained']",microsoft/Graphormer,https://github.com/microsoft/Graphormer,2021-05-27 05:31:18,2024-06-07 17:01:35,2024-05-28 06:22:34,77.0,2.0,321.0,31.0,46.0,86.0,64.0,2044.0,2024-04-03 08:23:10,dig-v1.0,2.0,14.0,,,,,,,,,,,2.0,,,,,,,,,True,,,,,,,,,
+98,OpenML,,community,BSD-3,https://github.com/openml/OpenML,Open Machine Learning.,16,True,['datasets'],openml/OpenML,https://github.com/openml/OpenML,2012-12-11 11:27:40,2024-04-05 08:51:24,2024-01-12 08:40:06,2317.0,,90.0,48.0,202.0,367.0,560.0,656.0,,,,35.0,,,,,,,,,,,2.0,,,,,,,,,True,,,,,,,,,
+99,ChemCrow,,language-models,MIT,https://github.com/ur-whitelab/chemcrow-public,Open source package for the accurate solution of reasoning-intensive chemical tasks.,16,True,['ai-agent'],ur-whitelab/chemcrow-public,https://github.com/ur-whitelab/chemcrow-public,2023-06-04 15:59:05,2024-04-03 19:49:19,2024-03-27 04:32:41,110.0,,75.0,16.0,22.0,4.0,14.0,547.0,2024-03-27 04:28:45,0.3.24,12.0,3.0,chemcrow,,4.0,4.0,https://pypi.org/project/chemcrow,542.0,542.0,,,,1.0,,,,,,,,,,,,,,,,,,
+100,Uni-Fold,,biomolecules,Apache-2.0,https://github.com/dptech-corp/Uni-Fold,An open-source platform for developing protein models beyond AlphaFold.,16,True,,dptech-corp/Uni-Fold,https://github.com/dptech-corp/Uni-Fold,2022-07-30 03:37:29,2024-06-26 02:52:56,2024-01-08 06:19:47,98.0,,66.0,7.0,80.0,20.0,50.0,364.0,2022-10-19 12:44:31,2.2.0,3.0,7.0,,,,,,,161.0,,,,3.0,,,,,,3558.0,,,,,,,,,,,,
+101,GT4SD - Generative Toolkit for Scientific Discovery,,community,MIT,https://huggingface.co/GT4SD,Gradio apps of generative models in GT4SD.,16,True,"['generative', 'pretrained', 'drug-discovery', 'model-repository']",GT4SD/gt4sd-core,https://github.com/GT4SD/gt4sd-core,2022-02-11 19:06:58,2024-07-04 11:11:27,2024-07-04 11:11:26,296.0,8.0,66.0,17.0,148.0,1.0,98.0,326.0,2024-06-13 15:18:45,1.4.1,57.0,20.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+102,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 18:05:13,2017-02-21 23:20:23,106.0,,106.0,18.0,16.0,19.0,9.0,296.0,2017-02-03 00:28:29,1.3.0,7.0,2.0,chemdataextractor,chemdataextractor/chemdataextractor,112.0,112.0,https://pypi.org/project/chemdataextractor,686.0,750.0,https://anaconda.org/chemdataextractor/chemdataextractor,2023-06-16 13:17:47.249,3138.0,1.0,,,,,,2996.0,,,,,,,,,,,,
+103,gpax,,math,MIT,https://github.com/ziatdinovmax/gpax,Gaussian Processes for Experimental Sciences.,16,True,"['probabilistic', 'active-learning']",ziatdinovmax/gpax,https://github.com/ziatdinovmax/gpax,2021-10-28 13:43:18,2024-08-09 21:37:33,2024-05-21 08:13:54,787.0,1.0,22.0,7.0,68.0,7.0,32.0,194.0,2024-03-20 06:39:05,0.1.8,16.0,6.0,gpax,,1.0,1.0,https://pypi.org/project/gpax,128.0,128.0,,,,1.0,,,,,,,,,,,,,,,,,,
+104,openmm-torch,,md,https://github.com/openmm/openmm-torch#license,https://github.com/openmm/openmm-torch,OpenMM plugin to define forces with neural networks.,16,True,"['ml-iap', 'lang-cpp']",openmm/openmm-torch,https://github.com/openmm/openmm-torch,2019-09-27 18:15:19,2024-07-09 06:18:50,2024-07-09 06:18:50,71.0,4.0,24.0,12.0,61.0,25.0,65.0,177.0,2023-10-09 08:49:10,1.4,16.0,8.0,,conda-forge/openmm-torch,,,,,9229.0,https://anaconda.org/conda-forge/openmm-torch,2024-06-03 12:02:18.688,396876.0,2.0,,,,,,,,-1.0,,,,,,,,,,
+105,XenonPy,,general-tool,BSD-3-Clause,https://github.com/yoshida-lab/XenonPy,XenonPy is a Python Software for Materials Informatics.,16,True,,yoshida-lab/XenonPy,https://github.com/yoshida-lab/XenonPy,2018-01-17 10:13:29,2024-07-15 21:14:48,2024-04-21 06:58:38,693.0,,57.0,11.0,184.0,20.0,66.0,130.0,2023-05-21 15:54:32,0.6.8,45.0,10.0,xenonpy,,,,https://pypi.org/project/xenonpy,211.0,229.0,,,,2.0,,,,,,1393.0,,,,,,,,,,,,
+106,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', 'model-repository']",janosh/matbench-discovery,https://github.com/janosh/matbench-discovery,2022-06-20 18:32:44,2024-08-11 20:57:25,2024-08-11 20:57:23,357.0,23.0,11.0,8.0,77.0,2.0,32.0,82.0,2024-07-15 20:07:19,1.2.0,6.0,7.0,matbench-discovery,,2.0,2.0,https://pypi.org/project/matbench-discovery,142.0,142.0,,,,2.0,,,,,,,,-1.0,,,,,,,,,,
+107,PMTransformer,,generative,MIT,https://github.com/hspark1212/MOFTransformer,"Universal Transfer Learning in Porous Materials, including MOFs.",16,True,"['transfer-learning', 'pretrained', 'transformer']",hspark1212/MOFTransformer,https://github.com/hspark1212/MOFTransformer,2021-12-11 06:30:12,2024-06-20 06:58:46,2024-06-20 06:57:57,410.0,6.0,11.0,5.0,127.0,,36.0,82.0,2024-06-20 07:02:24,2.2.0,17.0,2.0,moftransformer,,6.0,6.0,https://pypi.org/project/moftransformer,298.0,298.0,,,,1.0,,,,,,,,,True,,,,,,,,,
+108,SpheriCart,,math,MIT,https://github.com/lab-cosmo/sphericart,Multi-language library for the calculation of spherical harmonics in Cartesian coordinates.,16,True,,lab-cosmo/sphericart,https://github.com/lab-cosmo/sphericart,2023-02-04 15:15:25,2024-08-06 22:48:54,2024-08-06 22:45:41,364.0,16.0,10.0,5.0,102.0,19.0,13.0,62.0,2023-04-26 12:06:09,0.3.0,3.0,10.0,sphericart,,3.0,3.0,https://pypi.org/project/sphericart,200.0,203.0,,,,1.0,,,,,,60.0,,,,,,,,,,,,
+109,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.,16,True,['ml-dft'],materialsproject/pyrho,https://github.com/materialsproject/pyrho,2020-05-25 22:44:02,2024-08-14 00:15:19,2024-02-23 02:53:46,287.0,,6.0,9.0,115.0,1.0,3.0,36.0,2024-02-23 02:54:45,0.4.4,28.0,8.0,mp-pyrho,,23.0,23.0,https://pypi.org/project/mp-pyrho,6490.0,6490.0,,,,3.0,,,,,,,,,,,,,,,,,,
+110,wfl,,ml-iap,GPL-2.0,https://github.com/libAtoms/workflow,Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows.,16,True,"['workflows', 'htc']",libAtoms/workflow,https://github.com/libAtoms/workflow,2021-11-04 17:03:34,2024-08-14 08:48:30,2024-08-08 22:10:12,1143.0,106.0,18.0,9.0,178.0,63.0,91.0,27.0,2024-04-25 15:07:11,0.2.4,4.0,18.0,,,1.0,1.0,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+111,IPSuite,,active-learning,EPL-2.0,https://github.com/zincware/IPSuite,A Python toolkit for FAIR development and deployment of machine-learned interatomic potentials.,16,True,"['ml-iap', 'md', 'workflows', 'htc', 'FAIR']",zincware/IPSuite,https://github.com/zincware/IPSuite,2023-03-01 16:34:45,2024-08-14 17:22:32,2024-08-09 08:56:01,442.0,38.0,8.0,3.0,203.0,67.0,65.0,17.0,2024-08-08 20:37:20,0.1.3,5.0,8.0,ipsuite,,6.0,6.0,https://pypi.org/project/ipsuite,139.0,139.0,,,,2.0,,,,,,,,,,,,,,,,,,
+112,MoLeR,,generative,MIT,https://github.com/microsoft/molecule-generation,Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation.,15,True,,microsoft/molecule-generation,https://github.com/microsoft/molecule-generation,2022-02-17 19:16:29,2024-01-04 18:10:00,2024-01-03 14:28:02,67.0,,38.0,11.0,37.0,10.0,28.0,250.0,2024-01-04 18:12:47,0.4.1,5.0,5.0,molecule-generation,,,,https://pypi.org/project/molecule-generation,229.0,229.0,,,,2.0,,,,,,,,,,,,,,,,,,
+113,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 04:48:38,2024-04-12 13:38:21,2018-09-10 11:14:35,75.0,,81.0,23.0,101.0,30.0,19.0,197.0,,,3.0,2.0,qml,,31.0,31.0,https://pypi.org/project/qml,362.0,362.0,,,,3.0,,,,,,,,,,,,,,,,,,
+114,ChemNLP project,,language-models,MIT,https://github.com/OpenBioML/chemnlp,ChemNLP project.,15,True,['datasets'],OpenBioML/chemnlp,https://github.com/OpenBioML/chemnlp,2023-02-13 16:20:23,2024-08-15 02:41:56,2024-08-15 02:41:32,367.0,36.0,45.0,3.0,284.0,111.0,140.0,144.0,,,,27.0,chemnlp,,,,https://pypi.org/project/chemnlp,58.0,58.0,,,,2.0,,,,,,,,,,,,,,,,,,
+115,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,['automl'],hackingmaterials/automatminer,https://github.com/hackingmaterials/automatminer,2018-05-10 18:27:08,2023-11-12 10:09:39,2022-01-06 19:39:49,1666.0,,49.0,12.0,233.0,36.0,138.0,134.0,2020-07-28 02:19:07,1.0.3.20200727,17.0,13.0,automatminer,,8.0,8.0,https://pypi.org/project/automatminer,222.0,222.0,,,,3.0,,,,,,,,,,,,,,,,,,
+116,MODNet,,rep-eng,MIT,https://github.com/ppdebreuck/modnet,MODNet: a framework for machine learning materials properties.,15,True,"['pretrained', 'small-data', 'transfer-learning']",ppdebreuck/modnet,https://github.com/ppdebreuck/modnet,2020-03-13 07:39:21,2024-06-24 13:31:56,2024-06-24 12:29:45,276.0,1.0,31.0,7.0,174.0,15.0,27.0,74.0,2024-05-07 14:09:13,0.4.4,21.0,8.0,,,7.0,7.0,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+117,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.,15,True,,uf3/uf3,https://github.com/uf3/uf3,2021-10-01 13:21:44,2024-07-27 23:40:38,2024-07-12 22:56:23,703.0,21.0,20.0,6.0,82.0,13.0,30.0,56.0,2023-10-27 16:36:21,0.4.0,4.0,10.0,uf3,,1.0,1.0,https://pypi.org/project/uf3,35.0,35.0,,,,2.0,,,,,,,,,,,,,,,,,,
+118,aviary,,materials-discovery,MIT,https://github.com/CompRhys/aviary,The Wren sits on its Roost in the Aviary.,15,True,,CompRhys/aviary,https://github.com/CompRhys/aviary,2021-09-28 12:29:05,2024-08-05 20:10:13,2024-08-05 20:09:13,638.0,28.0,11.0,3.0,61.0,2.0,24.0,45.0,2024-07-22 19:03:03,1.0.0,5.0,4.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,
+119,KLIFF,,ml-iap,LGPL-2.1,https://github.com/openkim/kliff,KIM-based Learning-Integrated Fitting Framework for interatomic potentials.,15,True,"['probabilistic', 'workflows']",openkim/kliff,https://github.com/openkim/kliff,2017-08-01 20:33:58,2024-08-14 00:02:36,2024-07-06 01:33:46,1073.0,8.0,20.0,4.0,148.0,22.0,19.0,34.0,2023-12-17 02:12:00,0.4.3,18.0,9.0,kliff,conda-forge/kliff,3.0,3.0,https://pypi.org/project/kliff,75.0,2132.0,https://anaconda.org/conda-forge/kliff,2023-12-18 18:25:23.770,94653.0,2.0,,,,,,,,,,,,,,,,,,
+120,Polynomials4ML.jl,,math,MIT,https://github.com/ACEsuit/Polynomials4ML.jl,"Polynomials for ML: fast evaluation, batching, differentiation.",15,True,['lang-julia'],ACEsuit/Polynomials4ML.jl,https://github.com/ACEsuit/Polynomials4ML.jl,2022-09-20 23:05:53,2024-06-22 16:34:34,2024-06-22 16:18:31,410.0,50.0,5.0,4.0,39.0,17.0,34.0,12.0,2024-06-22 16:34:35,0.3.1,17.0,10.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+121,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 04:03:53,2022-05-10 13:22:20,45.0,,446.0,58.0,17.0,5.0,63.0,2475.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+122,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 16:56:47,2024-05-13 08:28:51,2024-03-26 12:57:17,75.0,,130.0,47.0,77.0,27.0,42.0,883.0,2023-01-05 18:42:00,0.8,9.0,23.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+123,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.,14,True,['transformer'],google-deepmind/ferminet,https://github.com/google-deepmind/ferminet,2020-10-06 12:21:06,2024-06-04 15:46:46,2024-06-04 15:46:09,228.0,4.0,110.0,37.0,30.0,1.0,44.0,659.0,,,,18.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+124,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 10:15:02,2024-06-26 14:25:20,2023-10-17 22:37:11,244.0,,42.0,17.0,33.0,27.0,36.0,339.0,,,,10.0,escnn,,,,https://pypi.org/project/escnn,527.0,527.0,,,,2.0,,,,,,,,,,,,,,,,,,
+125,sGDML,,ml-iap,MIT,https://github.com/stefanch/sGDML,sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model.,14,True,,stefanch/sGDML,https://github.com/stefanch/sGDML,2018-07-11 15:20:30,2023-08-31 12:58:49,2023-08-31 12:57:53,205.0,,35.0,8.0,12.0,6.0,11.0,139.0,2023-08-31 12:58:49,1.0.2,15.0,8.0,sgdml,,9.0,9.0,https://pypi.org/project/sgdml,141.0,141.0,,,,2.0,,,,,,,,,,,,,,,,,,
+126,SevenNet,,uip,GPL-3.0,https://github.com/MDIL-SNU/SevenNet,SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that..,14,True,"['ml-iap', 'md', 'pretrained']",MDIL-SNU/SevenNet,https://github.com/MDIL-SNU/SevenNet,2023-02-16 06:31:53,2024-08-15 10:05:32,2024-07-30 04:17:16,380.0,67.0,9.0,4.0,54.0,2.0,6.0,86.0,2024-07-26 01:40:27,0.9.3,6.0,6.0,,,1.0,1.0,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+127,PyXtalFF,,ml-iap,MIT,https://github.com/MaterSim/PyXtal_FF,Machine Learning Interatomic Potential Predictions.,14,True,,MaterSim/PyXtal_FF,https://github.com/MaterSim/PyXtal_FF,2019-01-08 08:43:35,2024-02-15 16:12:06,2024-01-07 14:27:45,561.0,,23.0,9.0,4.0,11.0,51.0,85.0,2023-06-09 17:17:24,0.2.3,19.0,9.0,pyxtal_ff,,,,https://pypi.org/project/pyxtal_ff,71.0,71.0,,,,2.0,,,,,,,,,,,,,,,,,,
+128,HydraGNN,,rep-learn,BSD-3,https://github.com/ORNL/HydraGNN,Distributed PyTorch implementation of multi-headed graph convolutional neural networks.,14,True,,ORNL/HydraGNN,https://github.com/ORNL/HydraGNN,2021-05-28 03:32:03,2024-08-13 16:34:41,2024-08-06 16:10:42,675.0,23.0,23.0,10.0,228.0,14.0,32.0,56.0,2023-11-10 15:25:43,3.0,2.0,13.0,,,1.0,1.0,,,,,,,2.0,,,,,,,,,True,,,,,,,,,
+129,SchNetPack G-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/schnetpack-gschnet,G-SchNet extension for SchNetPack.,14,True,,atomistic-machine-learning/schnetpack-gschnet,https://github.com/atomistic-machine-learning/schnetpack-gschnet,2022-04-21 12:34:13,2024-07-05 12:47:35,2024-07-05 12:44:53,164.0,31.0,8.0,3.0,1.0,,14.0,43.0,2024-07-03 16:43:48,1.1.0,3.0,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+130,SALTED,,ml-dft,GPL-3.0,https://github.com/andreagrisafi/SALTED,Symmetry-Adapted Learning of Three-dimensional Electron Densities.,14,True,,andreagrisafi/SALTED,https://github.com/andreagrisafi/SALTED,2020-01-22 10:24:29,2024-08-02 15:26:59,2024-07-08 10:41:32,699.0,49.0,4.0,3.0,39.0,1.0,5.0,30.0,2024-06-22 12:42:33,3.0.0,2.0,16.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+131,GlassPy,,rep-eng,GPL-3.0,https://github.com/drcassar/glasspy,Python module for scientists working with glass materials.,14,True,,drcassar/glasspy,https://github.com/drcassar/glasspy,2019-07-18 23:15:43,2024-08-11 17:13:25,2024-01-21 13:59:55,334.0,,7.0,6.0,13.0,1.0,5.0,26.0,2024-01-21 13:47:58,0.4.6,8.0,,glasspy,,5.0,5.0,https://pypi.org/project/glasspy,165.0,165.0,,,,2.0,,,,,,,,,,,,,,,,,,
+132,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.,13,True,['lang-fortran'],rouyang2017/SISSO,https://github.com/rouyang2017/SISSO,2017-10-16 11:31:57,2024-06-19 15:58:39,2023-09-12 08:50:38,166.0,,76.0,6.0,3.0,8.0,57.0,229.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+133,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 16:26:02,2022-09-05 10:56:20,387.0,,75.0,12.0,53.0,64.0,85.0,216.0,2022-05-23 12:53:39,2.2.0,11.0,9.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+134,Librascal,,rep-eng,LGPL-2.1,https://github.com/lab-cosmo/librascal,A scalable and versatile library to generate representations for atomic-scale learning.,13,True,,lab-cosmo/librascal,https://github.com/lab-cosmo/librascal,2018-02-01 08:38:51,2024-01-11 17:38:31,2023-11-30 14:48:28,2931.0,,19.0,22.0,201.0,100.0,132.0,80.0,,,3.0,30.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+135,OpenMM-ML,,md,MIT,https://github.com/openmm/openmm-ml,High level API for using machine learning models in OpenMM simulations.,13,True,['ml-iap'],openmm/openmm-ml,https://github.com/openmm/openmm-ml,2021-02-10 20:55:25,2024-08-06 19:01:36,2024-08-06 19:01:36,46.0,2.0,18.0,14.0,33.0,20.0,34.0,80.0,2024-06-06 16:49:09,1.2,6.0,5.0,,conda-forge/openmm-ml,,,,,190.0,https://anaconda.org/conda-forge/openmm-ml,2024-06-07 16:52:07.157,4577.0,3.0,,,,,,,,,,,,,,,,,,
+136,NNPOps,,ml-iap,MIT,https://github.com/openmm/NNPOps,High-performance operations for neural network potentials.,13,True,"['md', 'lang-cpp']",openmm/NNPOps,https://github.com/openmm/NNPOps,2020-09-10 21:02:00,2024-07-10 15:29:02,2024-07-10 15:29:02,95.0,1.0,17.0,9.0,63.0,21.0,34.0,80.0,2023-07-26 11:21:58,0.6,7.0,9.0,,conda-forge/nnpops,,,,,6342.0,https://anaconda.org/conda-forge/nnpops,2024-05-31 15:49:32.883,183926.0,2.0,,,,,,,,,,,,,,,,,,
+137,ChatMOF,,language-models,MIT,https://github.com/Yeonghun1675/ChatMOF,Predict and Inverse design for metal-organic framework with large-language models (llms).,13,True,['generative'],Yeonghun1675/ChatMOF,https://github.com/Yeonghun1675/ChatMOF,2023-05-19 06:33:06,2024-07-01 04:57:44,2024-07-01 04:57:36,72.0,4.0,7.0,1.0,12.0,,5.0,53.0,2024-06-14 09:56:27,0.2.1,6.0,,chatmof,,2.0,2.0,https://pypi.org/project/chatmof,243.0,243.0,,,,2.0,,,,,,,,,True,,,,,,,,,
+138,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 05:23:51,2021-09-06 05:23:38,25.0,,272.0,23.0,7.0,17.0,20.0,621.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+139,mat2vec,,language-models,MIT,https://github.com/materialsintelligence/mat2vec,Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from materials..,12,False,['rep-learn'],materialsintelligence/mat2vec,https://github.com/materialsintelligence/mat2vec,2019-04-25 07:55:30,2023-05-06 22:45:49,2023-05-06 22:45:49,55.0,,174.0,40.0,7.0,6.0,18.0,615.0,,,,5.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+140,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 17:05:09,2024-07-12 20:06:37,2024-07-12 20:06:37,425.0,6.0,57.0,18.0,5.0,2.0,12.0,477.0,,,,28.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+141,QH9,,datasets,CC-BY-NC-SA-4.0,https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench/QH9,A Quantum Hamiltonian Prediction Benchmark.,12,True,['ml-dft'],divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-07-12 20:06:37,2024-07-12 20:06:37,425.0,6.0,57.0,18.0,5.0,2.0,12.0,477.0,,,,28.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+142,Artificial Intelligence for Science (AIRS),,general-tool,GPL-3.0 license,https://github.com/divelab/AIRS,Artificial Intelligence Research 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 17:05:09,2024-07-12 20:06:37,2024-07-12 20:06:37,425.0,6.0,57.0,18.0,5.0,2.0,12.0,477.0,,,,28.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+143,QHNet,,ml-dft,GPL-3.0,https://github.com/divelab/AIRS/tree/main/OpenDFT/QHNet,Artificial Intelligence Research for Science (AIRS).,12,True,['rep-learn'],divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-07-12 20:06:37,2024-07-12 20:06:37,425.0,6.0,57.0,18.0,5.0,2.0,12.0,477.0,,,,28.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+144,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,False,['rep-learn'],chaitjo/geometric-gnn-dojo,https://github.com/chaitjo/geometric-gnn-dojo,2023-01-21 20:08:45,2024-05-22 11:06:03,2023-06-18 23:17:32,26.0,,43.0,10.0,5.0,3.0,6.0,446.0,2023-06-18 23:20:44,0.2.0,2.0,3.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,
+145,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 00:12:00,2018-03-30 12:26:14,1724.0,,70.0,45.0,8.0,18.0,19.0,270.0,2017-11-08 18:05:50,0.1,1.0,12.0,,,1.0,1.0,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+146,gptchem,,language-models,MIT,https://github.com/kjappelbaum/gptchem,Use GPT-3 to solve chemistry problems.,12,True,,kjappelbaum/gptchem,https://github.com/kjappelbaum/gptchem,2023-01-06 15:34:32,2024-05-17 19:25:11,2023-10-04 11:27:09,147.0,,39.0,9.0,5.0,19.0,2.0,221.0,2023-11-30 09:31:51,0.0.4,2.0,4.0,gptchem,,,,https://pypi.org/project/gptchem,31.0,31.0,,,,2.0,,,,,,,,,,,,,,,,,,
+147,ANI-1,,ml-iap,MIT,https://github.com/isayev/ASE_ANI,ANI-1 neural net potential with python interface (ASE).,12,True,,isayev/ASE_ANI,https://github.com/isayev/ASE_ANI,2016-12-08 05:09:32,2024-03-11 21:50:26,2024-03-11 21:50:26,112.0,,55.0,33.0,9.0,16.0,21.0,220.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+148,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..,12,True,,deepmodeling/DMFF,https://github.com/deepmodeling/DMFF,2022-02-14 01:35:50,2024-07-23 03:11:30,2024-01-12 00:58:20,431.0,,40.0,9.0,158.0,10.0,16.0,146.0,2023-11-09 14:32:37,1.0.0,4.0,14.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+149,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 12:45:14,2024-06-27 12:53:17,2024-06-27 12:53:00,763.0,3.0,28.0,7.0,37.0,6.0,19.0,143.0,2023-08-30 13:54:23,1,1.0,6.0,,,6.0,6.0,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+150,SPICE,,datasets,MIT,https://github.com/openmm/spice-dataset,A collection of QM data for training potential functions.,12,True,"['ml-iap', 'md']",openmm/spice-dataset,https://github.com/openmm/spice-dataset,2021-08-31 18:52:05,2024-04-15 20:17:14,2024-04-15 20:14:31,42.0,,8.0,17.0,46.0,15.0,45.0,141.0,2024-04-15 20:17:14,2.0.1,8.0,,,,,,,,9.0,,,,2.0,,,,,,244.0,,,,,,,,,,,,
+151,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 07:40:17,2024-08-12 14:37:34,2024-08-06 09:40:56,138.0,7.0,13.0,7.0,21.0,3.0,6.0,69.0,2024-06-24 11:09:20,0.3.0,2.0,4.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+152,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/.,12,True,,JARVIS-Materials-Design/jarvis-tools-notebooks,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,2020-06-27 20:22:02,2024-08-14 02:50:36,2024-08-14 02:50:35,753.0,118.0,26.0,4.0,46.0,,,58.0,,,,5.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,
+153,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 23:55:23,2024-02-24 20:11:49,2022-06-14 22:18:28,143.0,,27.0,7.0,19.0,,6.0,57.0,2022-04-27 17:05:25,0.3.12,13.0,4.0,,,13.0,13.0,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+154,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 14:28:34,2024-08-14 13:15:55,2024-08-14 13:10:20,561.0,17.0,13.0,7.0,255.0,32.0,35.0,44.0,,,,14.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+155,synspace,,generative,MIT,https://github.com/whitead/synspace,Synthesis generative model.,12,False,,whitead/synspace,https://github.com/whitead/synspace,2022-12-28 00:59:14,2023-04-15 22:42:57,2023-04-15 18:04:16,27.0,,3.0,3.0,1.0,2.0,1.0,35.0,2023-04-15 22:42:57,0.3.0,3.0,2.0,synspace,,17.0,17.0,https://pypi.org/project/synspace,906.0,906.0,,,,2.0,,,,,,,,,,,,,,,,,,
+156,MACE-MP,,uip,MIT,https://github.com/ACEsuit/mace-mp,Pretrained foundation models for materials chemistry.,12,True,"['ml-iap', 'pretrained', 'rep-learn', 'md']",ACEsuit/mace-mp,https://github.com/ACEsuit/mace-mp,2024-01-11 10:55:55,2024-04-28 17:30:31,2024-04-24 14:56:12,10.0,,5.0,10.0,1.0,1.0,6.0,33.0,2024-04-28 17:30:31,mace_mp_0b,2.0,2.0,mace-torch,,,,https://pypi.org/project/mace-torch,11135.0,14179.0,,,,3.0,,,,,,21310.0,,,True,,,,,,,,,
+157,Compositionally-Restricted Attention-Based Network (CrabNet),,rep-learn,MIT,https://github.com/sparks-baird/CrabNet,Predict materials properties using only the composition information!.,12,False,,sparks-baird/CrabNet,https://github.com/sparks-baird/CrabNet,2021-09-17 07:58:15,2023-06-19 09:35:52,2023-06-19 09:35:52,427.0,,4.0,1.0,54.0,15.0,2.0,12.0,2023-06-07 01:07:33,2.0.8,5.0,5.0,crabnet,,13.0,13.0,https://pypi.org/project/crabnet,262.0,262.0,,,,2.0,,,,,,,,,,,,,,,,,,
+158,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,False,,whitead/dmol-book,https://github.com/whitead/dmol-book,2020-08-19 19:24:32,2023-07-02 18:02:57,2023-07-02 18:02:56,558.0,,111.0,16.0,92.0,28.0,129.0,604.0,,,,19.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,
+159,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 19:49:36,2021-12-08 19:49:36,160.0,,130.0,19.0,9.0,27.0,8.0,347.0,,,,5.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,
+160,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 05:10:37,2021-09-17 05:10:37,52.0,,144.0,25.0,15.0,9.0,10.0,344.0,2019-10-28 18:46:28,1.0,1.0,10.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,
+161,DeepH-pack,,ml-dft,LGPL-3.0,https://github.com/mzjb/DeepH-pack,Deep neural networks for density functional theory Hamiltonian.,11,True,['lang-julia'],mzjb/DeepH-pack,https://github.com/mzjb/DeepH-pack,2022-05-13 02:51:32,2024-05-22 10:50:01,2024-05-22 10:50:01,66.0,2.0,36.0,7.0,17.0,12.0,38.0,199.0,2023-07-11 08:13:06,0.2.2,2.0,9.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+162,PiNN,,ml-iap,BSD-3-Clause,https://github.com/Teoroo-CMC/PiNN,A Python library for building atomic neural networks.,11,True,,Teoroo-CMC/PiNN,https://github.com/Teoroo-CMC/PiNN,2019-10-04 08:13:18,2024-08-02 06:55:47,2024-06-27 11:23:15,160.0,2.0,31.0,6.0,15.0,1.0,5.0,104.0,2019-10-09 09:21:30,0.3.0,1.0,5.0,,,,,,,4.0,,,,2.0,teoroo/pinn,https://hub.docker.com/r/teoroo/pinn,2024-06-27 11:32:07.538231,,243.0,,,,,,,,,,,,,
+163,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.,11,True,,TinkerTools/tinker-hp,https://github.com/TinkerTools/tinker-hp,2018-06-12 12:15:51,2024-08-05 15:47:54,2024-08-05 15:47:21,562.0,14.0,21.0,13.0,2.0,3.0,17.0,78.0,2019-11-24 16:21:50,published-version-V1,1.0,12.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+164,hippynn,,rep-learn,https://github.com/lanl/hippynn/blob/main/LICENSE.txt,https://github.com/lanl/hippynn,python library for atomistic machine learning.,11,True,['workflows'],lanl/hippynn,https://github.com/lanl/hippynn,2021-11-17 00:45:13,2024-08-14 20:00:55,2024-08-14 19:09:04,147.0,18.0,23.0,9.0,75.0,7.0,8.0,65.0,2024-01-29 22:04:53,hippynn-0.0.3,3.0,14.0,,,1.0,1.0,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+165,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.,11,True,,ICAMS/python-ace,https://github.com/ICAMS/python-ace,2021-11-19 11:39:54,2024-07-22 19:16:32,2024-07-22 19:16:32,160.0,1.0,15.0,4.0,24.0,16.0,33.0,62.0,2022-10-24 19:59:33,0.2.8,2.0,5.0,python-ace,,,,https://pypi.org/project/python-ace,18.0,18.0,,,,2.0,,,,,,,,,,,,,,,,,,
+166,Neural-Network-Models-for-Chemistry,,community,,https://github.com/Eipgen/Neural-Network-Models-for-Chemistry,A collection of Nerual Network Models for chemistry.,11,True,['rep-learn'],Eipgen/Neural-Network-Models-for-Chemistry,https://github.com/Eipgen/Neural-Network-Models-for-Chemistry,2022-05-23 06:35:09,2024-08-13 07:05:05,2024-08-08 14:14:24,210.0,14.0,8.0,3.0,22.0,1.0,1.0,59.0,2024-07-17 02:01:45,0.0.5,5.0,3.0,,,,,,,,,,,2.0,,,,,,,,,True,,,,,,,,,
+167,AMPtorch,,general-tool,GPL-3.0,https://github.com/ulissigroup/amptorch,AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch.,11,False,,ulissigroup/amptorch,https://github.com/ulissigroup/amptorch,2019-01-24 15:15:48,2023-07-16 02:11:38,2023-07-16 02:08:13,759.0,,32.0,10.0,99.0,7.0,26.0,58.0,2023-07-16 02:11:38,1.0,3.0,14.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+168,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 05:04:05,2022-01-27 05:04:05,586.0,,17.0,12.0,91.0,4.0,26.0,47.0,2021-09-23 01:41:42,1.1.1,9.0,4.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+169,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..,11,True,,mdsunivie/deeperwin,https://github.com/mdsunivie/deeperwin,2021-06-14 15:18:32,2024-06-07 15:52:47,2024-06-07 15:52:33,66.0,6.0,6.0,3.0,5.0,,11.0,46.0,2024-03-25 13:47:47,transferable_atomic_orbitals,3.0,7.0,deeperwin,,,,https://pypi.org/project/deeperwin,46.0,46.0,,,,3.0,,,,,,8.0,,,,,,,,,,,,
+170,nlcc,,language-models,MIT,https://github.com/whitead/nlcc,Natural language computational chemistry command line interface.,11,False,['single-paper'],whitead/nlcc,https://github.com/whitead/nlcc,2021-08-19 18:23:52,2023-02-04 03:07:56,2023-02-04 03:06:33,144.0,,7.0,5.0,1.0,,9.0,44.0,2023-02-04 03:07:56,0.6.0,10.0,3.0,nlcc,,1.0,1.0,https://pypi.org/project/nlcc,45.0,45.0,,,,2.0,,,,,,,,,,,,,,,,,,
+171,pair_nequip,,md,MIT,https://github.com/mir-group/pair_nequip,LAMMPS pair style for NequIP.,11,True,"['ml-iap', 'rep-learn']",mir-group/pair_nequip,https://github.com/mir-group/pair_nequip,2021-04-02 15:28:02,2024-06-05 17:06:39,2024-06-05 17:06:39,101.0,4.0,12.0,9.0,8.0,9.0,20.0,40.0,2022-05-20 00:39:04,0.5.2,4.0,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+172,load-atoms,,datasets,MIT,https://github.com/jla-gardner/load-atoms,download and manipulate atomistic datasets.,11,True,['data-structures'],jla-gardner/load-atoms,https://github.com/jla-gardner/load-atoms,2022-11-21 21:59:15,2024-04-06 05:14:57,2024-04-06 05:13:05,271.0,,2.0,1.0,31.0,2.0,29.0,37.0,,,,3.0,load-atoms,,3.0,3.0,https://pypi.org/project/load-atoms,459.0,459.0,,,,2.0,,,,,,,,,True,,,,,,,,,
+173,NeuralXC,,ml-dft,BSD-3-Clause,https://github.com/semodi/neuralxc,Implementation of a machine learned density functional.,11,False,,semodi/neuralxc,https://github.com/semodi/neuralxc,2019-03-14 18:13:40,2024-06-17 22:55:40,2021-07-05 21:36:23,337.0,,10.0,5.0,10.0,5.0,5.0,33.0,,,3.0,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+174,CCS_fit,,ml-iap,GPL-3.0,https://github.com/Teoroo-CMC/CCS,Curvature Constrained Splines.,11,True,,Teoroo-CMC/CCS,https://github.com/Teoroo-CMC/CCS,2021-12-13 14:29:53,2024-05-14 08:53:09,2024-02-16 09:31:25,762.0,,10.0,3.0,13.0,8.0,6.0,7.0,2024-02-16 09:31:28,0.22.5,100.0,8.0,ccs_fit,,,,https://pypi.org/project/ccs_fit,255.0,275.0,,,,2.0,,,,,,422.0,,,,,,,,,,,,
+175,pretrained-gnns,,rep-learn,MIT,https://github.com/snap-stanford/pretrain-gnns,Strategies for Pre-training Graph Neural Networks.,10,False,['pretrained'],snap-stanford/pretrain-gnns,https://github.com/snap-stanford/pretrain-gnns,2020-01-30 22:12:41,2023-07-29 06:21:39,2023-07-29 06:21:39,13.0,,160.0,17.0,8.0,34.0,29.0,954.0,,,,2.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+176,GNoME Explorer,,community,Apache-2.0,https://next-gen.materialsproject.org/materials/gnome,Graph Networks for Materials Exploration Database.,10,True,"['datasets', 'materials-discovery']",google-deepmind/materials_discovery,https://github.com/google-deepmind/materials_discovery,2023-11-28 10:29:51,2024-07-09 19:00:36,2023-12-02 03:54:29,8.0,,132.0,47.0,7.0,17.0,4.0,854.0,,,,2.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+177,Materials Discovery: GNoME,,materials-discovery,Apache-2.0,https://github.com/google-deepmind/materials_discovery,"Graph Networks for Materials Science (GNoME) and dataset of 381,000 novel stable materials.",10,True,"['uip', 'datasets', 'rep-learn', 'proprietary']",google-deepmind/materials_discovery,https://github.com/google-deepmind/materials_discovery,2023-11-28 10:29:51,2024-07-09 19:00:36,2023-12-02 03:54:29,8.0,,132.0,47.0,7.0,17.0,4.0,854.0,,,,2.0,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,
+178,OpenChem,,general-tool,MIT,https://github.com/Mariewelt/OpenChem,OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research.,10,False,,Mariewelt/OpenChem,https://github.com/Mariewelt/OpenChem,2018-07-10 01:27:33,2023-11-26 05:03:36,2022-04-27 19:27:40,444.0,,111.0,36.0,12.0,15.0,2.0,668.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+179,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 17:56:18,2022-07-10 17:56:12,6.0,,109.0,8.0,,2.0,36.0,417.0,,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+180,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, i.e. in modern materials science.",10,True,,tilde-lab/awesome-materials-informatics,https://github.com/tilde-lab/awesome-materials-informatics,2018-02-15 15:14:16,2024-07-12 09:19:42,2024-07-12 09:19:42,138.0,3.0,80.0,17.0,54.0,,8.0,359.0,2023-03-02 19:56:59,2023.03.02,1.0,19.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+181,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..,10,False,,mir-group/allegro,https://github.com/mir-group/allegro,2022-02-06 23:50:40,2024-07-01 20:43:10,2023-05-08 21:16:45,38.0,,44.0,20.0,5.0,18.0,15.0,309.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+182,GDC,,rep-learn,MIT,https://github.com/gasteigerjo/gdc,"Graph Diffusion Convolution, as proposed in Diffusion Improves Graph Learning (NeurIPS 2019).",10,False,['generative'],gasteigerjo/gdc,https://github.com/gasteigerjo/gdc,2019-10-26 16:05:11,2023-04-26 14:22:40,2023-04-26 14:22:40,28.0,,42.0,3.0,1.0,,11.0,262.0,,,,3.0,,,1.0,1.0,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+183,AI4Chemistry course,,educational,MIT,https://github.com/schwallergroup/ai4chem_course,"EPFL AI for chemistry course, Spring 2023. https://schwallergroup.github.io/ai4chem_course.",10,True,['chemistry'],schwallergroup/ai4chem_course,https://github.com/schwallergroup/ai4chem_course,2022-08-22 07:29:30,2024-05-02 20:41:12,2024-05-02 20:41:12,232.0,,30.0,4.0,9.0,1.0,3.0,127.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,True,,,,,,,,,
+184,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.,10,True,,deepmodeling/deepks-kit,https://github.com/deepmodeling/deepks-kit,2020-07-29 03:27:50,2024-08-08 16:28:48,2024-04-13 03:44:40,384.0,,35.0,14.0,44.0,5.0,14.0,96.0,,,,7.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+185,Grad DFT,,ml-dft,Apache-2.0,https://github.com/XanaduAI/GradDFT,GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation..,10,True,,XanaduAI/GradDFT,https://github.com/XanaduAI/GradDFT,2023-05-15 16:18:25,2024-02-13 16:05:53,2024-02-13 16:05:51,419.0,,5.0,5.0,43.0,11.0,43.0,71.0,,,,4.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+186,LLaMP,,language-models,BSD-3-Clause,https://github.com/chiang-yuan/llamp,A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An..,10,True,"['materials-discovery', 'cheminformatics', 'generative', 'MD', 'multimodal', 'language-models', 'lang-py', 'general-tool']",chiang-yuan/llamp,https://github.com/chiang-yuan/llamp,2023-07-01 08:15:34,2024-08-11 08:26:47,2024-08-01 23:06:38,372.0,9.0,6.0,,30.0,8.0,17.0,49.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+187,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 17:47:33,2024-06-26 14:49:22,2024-06-26 14:49:19,555.0,6.0,25.0,10.0,25.0,1.0,7.0,43.0,,,,13.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+188,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 14:39:52,2024-05-15 17:26:24,2024-05-15 17:25:23,1200.0,,11.0,3.0,40.0,5.0,15.0,42.0,,,,11.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+189,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 21:05:09,2022-02-24 19:00:50,989.0,,6.0,7.0,28.0,8.0,17.0,35.0,,,,10.0,flare_pp,,,,https://pypi.org/project/flare_pp,129.0,129.0,,,,2.0,,,,,,,,,,,,,,,,,,
+190,cmlkit,,rep-eng,MIT,https://github.com/sirmarcel/cmlkit,tools for machine learning in condensed matter physics and quantum chemistry.,10,False,['benchmarking'],sirmarcel/cmlkit,https://github.com/sirmarcel/cmlkit,2018-05-31 07:56:52,2022-04-01 00:39:14,2022-03-25 22:27:04,526.0,,6.0,3.0,1.0,6.0,2.0,34.0,,,,,cmlkit,,5.0,5.0,https://pypi.org/project/cmlkit,118.0,118.0,,,,2.0,,,,,,,,,,,,,,,,,,
+191,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,"['model-repository', 'knowledge-base', 'pretrained']",openkim/kim-api,https://github.com/openkim/kim-api,2014-07-28 21:21:08,2023-08-16 00:09:44,2022-03-17 23:01:36,2371.0,,20.0,12.0,55.0,17.0,18.0,31.0,,,,24.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+192,Point Edge Transformer (PET),,ml-iap,MIT,https://github.com/spozdn/pet,Point Edge Transformer.,10,True,"['rep-learn', 'transformer']",spozdn/pet,https://github.com/spozdn/pet,2023-02-08 18:36:10,2024-07-18 13:35:00,2024-07-02 10:29:58,201.0,12.0,5.0,4.0,12.0,5.0,,18.0,,,,7.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+193,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-..",10,False,['lang-julia'],ACEsuit/ACEhamiltonians.jl,https://github.com/ACEsuit/ACEhamiltonians.jl,2022-01-17 20:54:22,2024-06-05 15:25:30,2023-04-12 15:04:14,33.0,,6.0,5.0,42.0,2.0,3.0,12.0,2024-02-07 16:35:47,0.1.0,2.0,4.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+194,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 13:04:49,2022-11-10 13:04:45,265.0,,6.0,3.0,,,,10.0,2022-04-12 15:10:32,2.0.0,5.0,2.0,pyNNsMD,,1.0,1.0,https://pypi.org/project/pyNNsMD,46.0,46.0,,,,3.0,,,,,,,,,,,,,,,,,,
+195,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 00:09:17,2024-08-13 04:26:08,2024-08-13 04:22:01,226.0,5.0,5.0,4.0,22.0,22.0,33.0,8.0,,,,7.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+196,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 05:13:25,2021-11-18 09:11:56,63.0,,67.0,17.0,5.0,9.0,17.0,482.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+197,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 02:04:45,2020-11-28 02:04:45,79.0,,75.0,6.0,,6.0,1.0,286.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+198,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 09:57:19,2023-10-03 09:57:19,103.0,,57.0,4.0,,1.0,30.0,278.0,,,,2.0,,,1.0,1.0,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+199,MoLFormers UI,,community,Apache-2.0,https://molformer.res.ibm.com/,A family of foundation models trained on chemicals.,9,True,"['transformer', 'language-models', 'pretrained', 'drug-discovery']",IBM/molformer,https://github.com/IBM/molformer,2022-11-07 18:48:17,2023-10-16 16:34:25,2023-10-16 16:33:13,7.0,,40.0,10.0,3.0,9.0,10.0,231.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+200,MoLFormer,,language-models,Apache-2.0,https://github.com/IBM/molformer,Repository for MolFormer.,9,True,"['transformer', 'pretrained', 'drug-discovery']",IBM/molformer,https://github.com/IBM/molformer,2022-11-07 18:48:17,2023-10-16 16:34:25,2023-10-16 16:33:13,7.0,,40.0,10.0,3.0,9.0,10.0,231.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+201,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 08:42:35,2018-09-04 08:42:34,53.0,,65.0,16.0,,1.0,2.0,212.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+202,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 03:46:18,2021-02-20 03:46:09,20.0,,40.0,4.0,,1.0,3.0,197.0,,,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,
+203,Equiformer,,rep-learn,MIT,https://github.com/atomicarchitects/equiformer,[ICLR23 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs.,9,True,['transformer'],atomicarchitects/equiformer,https://github.com/atomicarchitects/equiformer,2023-02-28 00:21:30,2024-07-27 08:42:53,2024-07-18 10:32:17,6.0,3.0,36.0,5.0,2.0,5.0,7.0,187.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+204,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,False,,TUM-DAML/gemnet_pytorch,https://github.com/TUM-DAML/gemnet_pytorch,2021-10-11 07:30:36,2023-04-26 14:20:12,2023-04-26 14:20:12,36.0,,27.0,4.0,1.0,,14.0,175.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+205,MolSkill,,language-models,MIT,https://github.com/microsoft/molskill,Extracting medicinal chemistry intuition via preference machine learning.,9,True,"['drug-discovery', 'recommender']",microsoft/molskill,https://github.com/microsoft/molskill,2023-01-12 13:48:31,2023-10-31 17:03:36,2023-10-31 17:03:36,81.0,,9.0,6.0,8.0,2.0,4.0,101.0,2023-08-04 12:22:15,1.2b,5.0,4.0,,msr-ai4science/molskill,,,,,15.0,https://anaconda.org/msr-ai4science/molskill,2023-06-18 17:27:43.196,275.0,3.0,,,,,,,,,,,,,,,,,,
+206,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 21:57:33,2022-10-03 21:57:33,99.0,,17.0,8.0,,3.0,3.0,67.0,2021-04-05 06:49:29,0.2,2.0,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+207,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,False,['lang-julia'],ACEsuit/ACE.jl,https://github.com/ACEsuit/ACE.jl,2019-11-30 16:22:51,2023-06-09 21:31:30,2023-06-09 21:29:10,912.0,,15.0,8.0,65.0,24.0,58.0,66.0,,,,12.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+208,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 02:09:46,2018-04-19 02:00:46,120.0,,26.0,14.0,6.0,9.0,7.0,62.0,2018-04-15 16:55:15,1.2,3.0,4.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+209,HamGNN,,ml-dft,GPL-3.0,https://github.com/QuantumLab-ZY/HamGNN,An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix.,9,True,"['rep-learn', 'magnetism', 'lang-c']",QuantumLab-ZY/HamGNN,https://github.com/QuantumLab-ZY/HamGNN,2023-07-14 12:20:27,2024-08-06 17:17:12,2024-08-06 17:16:48,63.0,32.0,12.0,5.0,,20.0,4.0,49.0,,,,,,,,,,,,,,,2.0,,,,,,,,,True,,,,,,,,,
+210,AI for Science paper collection,,community,Apache-2.0,https://github.com/sherrylixuecheng/AI_for_Science_paper_collection,List the AI for Science papers accepted by top conferences.,9,True,,sherrylixuecheng/AI_for_Science_paper_collection,https://github.com/sherrylixuecheng/AI_for_Science_paper_collection,2024-06-28 16:20:57,2024-08-13 03:53:31,2024-08-04 06:21:15,66.0,66.0,5.0,2.0,8.0,,,43.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+211,pair_allegro,,md,MIT,https://github.com/mir-group/pair_allegro,LAMMPS pair style for Allegro deep learning interatomic potentials with parallelization support.,9,True,"['ml-iap', 'rep-learn']",mir-group/pair_allegro,https://github.com/mir-group/pair_allegro,2021-08-09 17:26:51,2024-06-05 17:00:50,2024-06-05 17:00:50,101.0,10.0,7.0,10.0,3.0,10.0,18.0,34.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+212,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.,9,True,['active-learning'],lanl/alf,https://github.com/lanl/ALF,2023-01-04 23:13:24,2024-08-08 16:59:38,2024-08-08 16:59:10,149.0,3.0,11.0,8.0,27.0,,,29.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+213,lie-nn,,math,MIT,https://github.com/lie-nn/lie-nn,Tools for building equivariant polynomials on reductive Lie groups.,9,False,['rep-learn'],lie-nn/lie-nn,https://github.com/lie-nn/lie-nn,2022-04-01 18:02:49,2023-06-29 19:38:34,2023-06-20 22:30:53,249.0,,1.0,8.0,3.0,,,26.0,2023-06-20 22:31:12,0.0.0,1.0,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+214,FAENet,,rep-learn,MIT,https://github.com/vict0rsch/faenet,Frame Averaging Equivariant GNN for materials modeling.,9,True,,vict0rsch/faenet,https://github.com/vict0rsch/faenet,2023-02-10 22:10:27,2023-10-12 08:46:26,2023-10-12 08:46:22,125.0,,2.0,3.0,5.0,,,25.0,2023-09-12 04:00:49,0.1.2,1.0,3.0,faenet,,2.0,2.0,https://pypi.org/project/faenet,62.0,62.0,,,,3.0,,,,,,,,,,,,,,,,,,
+215,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 21:27:41,2024-06-26 12:42:53,2024-06-26 12:42:45,242.0,2.0,5.0,4.0,7.0,3.0,,24.0,,,,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+216,CBFV,,rep-eng,,https://github.com/Kaaiian/CBFV,Tool to quickly create a composition-based feature vector.,9,False,,kaaiian/CBFV,https://github.com/Kaaiian/CBFV,2019-09-05 23:07:46,2022-03-30 05:47:53,2021-10-24 17:10:17,49.0,,6.0,4.0,7.0,5.0,5.0,23.0,,,,3.0,CBFV,,9.0,9.0,https://pypi.org/project/CBFV,286.0,286.0,,,,2.0,,,,,,,,,,,,,,,,,,
+217,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,..",9,True,,ICAMS/lammps-user-pace,https://github.com/ICAMS/lammps-user-pace,2021-02-25 10:04:48,2024-07-10 09:17:44,2023-11-27 21:28:13,59.0,,10.0,6.0,16.0,,6.0,21.0,2023-11-25 21:58:41,.2023.11.25.fix,6.0,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+218,UVVisML,,rep-learn,MIT,https://github.com/learningmatter-mit/uvvisml,Predict optical properties of molecules with machine learning.,9,False,"['optical-properties', 'single-paper', 'probabilistic']",learningmatter-mit/uvvisml,https://github.com/learningmatter-mit/uvvisml,2021-10-13 05:58:48,2023-05-26 22:35:14,2023-05-26 22:35:14,17.0,,6.0,4.0,1.0,,,21.0,2022-02-06 18:14:14,0.0.2,2.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+219,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 09:19:05,2024-08-08 08:26:12,2024-07-09 06:58:53,309.0,12.0,8.0,8.0,7.0,6.0,3.0,16.0,,,,8.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+220,BenchML,,rep-eng,Apache-2.0,https://github.com/capoe/benchml,ML benchmarking and pipeling framework.,9,False,['benchmarking'],capoe/benchml,https://github.com/capoe/benchml,2020-04-28 13:26:29,2023-05-24 15:13:06,2023-05-24 15:04:57,341.0,,4.0,5.0,7.0,3.0,10.0,15.0,,,,9.0,benchml,,,,https://pypi.org/project/benchml,91.0,91.0,,,,2.0,,,,,,,,,,,,,,,,,,
+221,OPTIMADE Tutorial Exercises,,educational,MIT,https://github.com/Materials-Consortia/optimade-tutorial-exercises,Tutorial exercises for the OPTIMADE API.,9,True,['datasets'],Materials-Consortia/optimade-tutorial-exercises,https://github.com/Materials-Consortia/optimade-tutorial-exercises,2021-08-25 17:33:15,2023-09-27 08:32:31,2023-09-27 08:32:30,49.0,,7.0,12.0,15.0,,3.0,14.0,2023-06-12 07:47:14,2.0.1,5.0,6.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+222,Q-stack,,ml-dft,MIT,https://github.com/lcmd-epfl/Q-stack,Stack of codes for dedicated pre- and post-processing tasks for Quantum Machine Learning (QML).,9,True,"['excited-states', 'general-tool']",lcmd-epfl/Q-stack,https://github.com/lcmd-epfl/Q-stack,2021-10-20 15:33:26,2024-07-25 14:47:23,2024-07-19 11:29:08,413.0,32.0,5.0,2.0,45.0,9.0,20.0,14.0,,,1.0,7.0,,,,,,,,,,,2.0,,,,,,,,,True,,,,,,,,,
+223,calorine,,ml-iap,https://gitlab.com/materials-modeling/calorine/-/blob/master/LICENSE,https://gitlab.com/materials-modeling/calorine,A Python package for constructing and sampling neuroevolution potential models. https://doi.org/10.21105/joss.06264.,9,False,,,,2021-04-23 16:12:56,2021-04-23 16:12:56,,,,4.0,,,9.0,76.0,12.0,,,14.0,,calorine,,,,https://pypi.org/project/calorine,1273.0,1273.0,,,,3.0,,,,,,,,,,,,,,,materials-modeling/calorine,https://gitlab.com/materials-modeling/calorine,,
+224,Materials Data Facility (MDF),,datasets,Apache-2.0,https://www.materialsdatafacility.org,"A simple way to publish, discover, and access materials datasets. Publication of very large datasets supported (e.g.,..",9,True,,materials-data-facility/connect_client,https://github.com/materials-data-facility/connect_client,2018-09-12 20:49:58,2024-03-10 03:11:45,2024-02-05 22:48:40,158.0,,1.0,4.0,35.0,1.0,6.0,10.0,2024-02-05 22:49:58,0.5.0,23.0,7.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+225,optimade.science,,community,MIT,https://optimade.science,A sky-scanner Optimade browser-only GUI.,9,True,['datasets'],tilde-lab/optimade.science,https://github.com/tilde-lab/optimade.science,2019-06-08 14:10:54,2024-06-10 12:03:39,2024-06-10 12:03:39,247.0,4.0,2.0,4.0,32.0,7.0,19.0,8.0,2023-03-02 20:13:25,2.0.0,1.0,8.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+226,2DMD 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..",9,True,['material-defect'],HSE-LAMBDA/ai4material_design,https://github.com/HSE-LAMBDA/ai4material_design,2021-03-25 10:06:20,2023-11-21 11:30:42,2023-11-21 11:30:33,1118.0,,3.0,8.0,28.0,,12.0,6.0,,,,11.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+227,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..",9,True,"['pretrained', 'material-defect']",HSE-LAMBDA/ai4material_design,https://github.com/HSE-LAMBDA/ai4material_design,2021-03-25 10:06:20,2023-11-21 11:30:42,2023-11-21 11:30:33,1118.0,,3.0,8.0,28.0,,12.0,6.0,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+228,MADICES Awesome Interoperability,,community,MIT,MADICES/MADICES.github.io/blob/main/docs/awesome_interoperability.md,Linked data interoperability resources of the Machine-actionable data interoperability for the chemical sciences..,9,False,['datasets'],MADICES/MADICES.github.io,https://github.com/MADICES/MADICES.github.io,2021-12-26 13:27:32,2024-07-10 07:36:51,2024-07-10 07:35:07,217.0,18.0,5.0,4.0,17.0,1.0,14.0,1.0,,,,10.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+229,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,..",8,True,"['educational', 'rep-learn']",neurreps/awesome-neural-geometry,https://github.com/neurreps/awesome-neural-geometry,2022-07-31 01:19:57,2024-08-13 14:39:08,2024-07-17 18:47:49,124.0,2.0,56.0,28.0,13.0,,1.0,886.0,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+230,Awesome-Graph-Generation,,community,,https://github.com/yuanqidu/awesome-graph-generation,A curated list of up-to-date graph generation papers and resources.,8,True,['rep-learn'],yuanqidu/awesome-graph-generation,https://github.com/yuanqidu/awesome-graph-generation,2021-08-07 05:43:46,2024-06-24 01:56:37,2024-03-17 06:07:46,84.0,,16.0,7.0,2.0,,,255.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+231,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,False,,rdkit/rdkit-tutorials,https://github.com/rdkit/rdkit-tutorials,2016-10-07 03:34:01,2023-03-19 13:36:55,2023-03-19 13:36:55,68.0,,70.0,17.0,7.0,3.0,1.0,251.0,,,,5.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+232,EquiformerV2,,rep-learn,MIT,https://github.com/atomicarchitects/equiformer_v2,[ICLR24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations.,8,True,,atomicarchitects/equiformer_v2,https://github.com/atomicarchitects/equiformer_v2,2023-06-21 07:09:58,2024-07-31 23:38:48,2024-07-16 05:51:23,16.0,4.0,24.0,5.0,1.0,15.0,2.0,184.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+233,BestPractices,,educational,MIT,https://github.com/anthony-wang/BestPractices,Things that you should (and should not) do in your Materials Informatics research.,8,True,,anthony-wang/BestPractices,https://github.com/anthony-wang/BestPractices,2020-05-05 19:41:25,2023-11-17 02:58:25,2023-11-17 02:58:25,17.0,,68.0,8.0,8.0,5.0,2.0,165.0,,,,3.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+234,G-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/G-SchNet,G-SchNet - a generative model for 3d molecular structures.,8,False,,atomistic-machine-learning/G-SchNet,https://github.com/atomistic-machine-learning/G-SchNet,2019-10-21 13:48:59,2023-03-24 12:05:41,2023-03-24 12:05:41,64.0,,25.0,7.0,,,10.0,129.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+235,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.,8,False,,ncfrey/resources,https://github.com/ncfrey/resources,2020-11-17 23:47:07,2022-02-18 13:37:51,2022-02-18 13:37:51,8.0,,22.0,9.0,,,,116.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+236,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 15:56:17,2022-08-08 15:56:17,25.0,,18.0,12.0,2.0,8.0,3.0,96.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+237,AIMNet,,ml-iap,MIT,https://github.com/aiqm/aimnet,Atoms In Molecules Neural Network Potential.,8,False,['single-paper'],aiqm/aimnet,https://github.com/aiqm/aimnet,2018-09-26 17:28:37,2019-11-21 23:49:01,2019-11-21 23:49:00,7.0,,24.0,10.0,2.0,4.0,,94.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+238,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 19:51:06,2021-04-29 19:51:06,78.0,,19.0,5.0,15.0,23.0,5.0,87.0,,,,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+239,Awesome Neural SBI,,community,MIT,https://github.com/smsharma/awesome-neural-sbi,Community-sourced list of papers and resources on neural simulation-based inference.,8,True,['active-learning'],smsharma/awesome-neural-sbi,https://github.com/smsharma/awesome-neural-sbi,2023-01-20 19:48:13,2024-06-17 04:24:32,2024-06-17 04:24:27,56.0,5.0,4.0,6.0,2.0,1.0,1.0,80.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+240,MACE-Jax,,ml-iap,MIT,https://github.com/ACEsuit/mace-jax,Equivariant machine learning interatomic potentials in JAX.,8,True,,ACEsuit/mace-jax,https://github.com/ACEsuit/mace-jax,2023-02-06 12:10:16,2023-10-04 08:07:35,2023-10-04 08:07:35,207.0,,4.0,11.0,1.0,3.0,3.0,58.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+241,graphite,,rep-learn,MIT,https://github.com/LLNL/graphite,A repository for implementing graph network models based on atomic structures.,8,True,,llnl/graphite,https://github.com/LLNL/graphite,2022-06-27 19:15:27,2024-08-08 04:10:45,2024-08-08 04:10:44,30.0,2.0,9.0,5.0,4.0,2.0,1.0,53.0,,,,2.0,,,11.0,11.0,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+242,cG-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/cG-SchNet,cG-SchNet - a conditional generative neural network for 3d molecular structures.,8,False,,atomistic-machine-learning/cG-SchNet,https://github.com/atomistic-machine-learning/cG-SchNet,2021-12-02 15:35:18,2023-03-24 12:09:56,2023-03-24 12:09:56,28.0,,15.0,3.0,,,3.0,50.0,2022-02-21 13:36:41,1.0,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+243,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 17:31:02,2024-01-19 18:11:52,2024-01-19 18:11:38,25.0,,8.0,3.0,1.0,,8.0,45.0,2022-03-08 02:14:28,1.0,1.0,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+244,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,False,['lang-cpp'],lab-cosmo/sketchmap,https://github.com/lab-cosmo/sketchmap,2014-05-20 09:33:32,2024-02-20 20:57:41,2023-05-24 22:47:50,64.0,,10.0,31.0,1.0,3.0,5.0,44.0,,,,8.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+245,PyNEP,,ml-iap,MIT,https://github.com/bigd4/PyNEP,A python interface of the machine learning potential NEP used in GPUMD.,8,True,,bigd4/PyNEP,https://github.com/bigd4/PyNEP,2022-03-21 06:27:13,2024-06-01 09:06:22,2024-06-01 09:06:22,80.0,3.0,17.0,2.0,15.0,4.0,7.0,44.0,,,,7.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+246,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 14:48:56,2024-07-19 14:11:30,2024-07-19 14:11:30,205.0,2.0,20.0,10.0,66.0,,,39.0,,,,13.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+247,SIMPLE-NN v2,,ml-iap,GPL-3.0,https://github.com/MDIL-SNU/SIMPLE-NN_v2,SIMPLE-NN is an open package that constructs Behler-Parrinello-type neural-network interatomic potentials from ab..,8,True,,MDIL-SNU/SIMPLE-NN_v2,https://github.com/MDIL-SNU/SIMPLE-NN_v2,2021-03-02 09:36:49,2023-12-29 02:08:47,2023-12-29 02:08:47,504.0,,17.0,5.0,88.0,4.0,9.0,39.0,,,,13.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+248,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 05:20:37,2020-06-30 05:20:37,38.0,,17.0,11.0,1.0,1.0,3.0,36.0,,,,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+249,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 16:19:31,2022-10-03 16:19:29,33.0,,7.0,5.0,1.0,,1.0,30.0,2021-07-19 18:09:36,1.0.1,1.0,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+250,CGAT,,rep-learn,MIT,https://github.com/hyllios/CGAT,Crystal graph attention neural networks for materials prediction.,8,False,,hyllios/CGAT,https://github.com/hyllios/CGAT,2021-03-28 09:51:15,2023-07-18 12:04:35,2023-01-10 22:31:07,153.0,,8.0,3.0,1.0,,1.0,25.0,2023-07-18 12:04:35,0.1,1.0,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+251,SkipAtom,,rep-eng,MIT,https://github.com/lantunes/skipatom,"Distributed representations of atoms, inspired by the Skip-gram model.",8,False,,lantunes/skipatom,https://github.com/lantunes/skipatom,2021-06-19 13:09:13,2023-07-16 19:28:39,2022-05-04 13:18:30,46.0,,3.0,2.0,7.0,3.0,1.0,23.0,,,1.0,,skipatom,conda-forge/skipatom,1.0,1.0,https://pypi.org/project/skipatom,92.0,151.0,https://anaconda.org/conda-forge/skipatom,2023-06-18 08:42:05.505,1492.0,3.0,,,,,,,,,,,,,,,,,,
+252,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.,8,True,['lang-julia'],ACEsuit/ACE1.jl,https://github.com/ACEsuit/ACE1.jl,2022-01-14 19:52:49,2024-07-02 14:12:25,2024-07-02 14:12:23,558.0,1.0,7.0,5.0,30.0,22.0,24.0,20.0,,,,9.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+253,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 20:56:40,2024-08-06 20:45:38,2024-07-27 21:36:58,602.0,1.0,3.0,4.0,4.0,3.0,4.0,19.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+254,bVAE-IM,,generative,MIT,https://github.com/tsudalab/bVAE-IM,Implementation of Chemical Design with GPU-based Ising Machine.,8,False,"['qml', 'single-paper']",tsudalab/bVAE-IM,https://github.com/tsudalab/bVAE-IM,2023-03-01 08:26:56,2023-07-11 04:39:24,2023-07-11 04:39:24,39.0,,3.0,8.0,,,,11.0,2023-03-01 14:26:13,1.0.0,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+255,SiMGen,,generative,MIT,https://github.com/RokasEl/simgen,Zero Shot Molecular Generation via Similarity Kernels.,8,True,['visualization'],RokasEl/simgen,https://github.com/RokasEl/simgen,2023-01-25 16:41:18,2024-06-13 15:43:18,2024-02-15 10:31:41,257.0,,,2.0,23.0,1.0,3.0,11.0,2024-02-14 10:35:02,0.1.0,1.0,4.0,simgen,,1.0,1.0,https://pypi.org/project/simgen,20.0,20.0,,,,3.0,,,,,,,,,True,,,,,,,,,
+256,T-e3nn,,rep-learn,MIT,https://github.com/Hongyu-yu/T-e3nn,Time-reversal Euclidean neural networks based on e3nn.,8,False,['magnetism'],Hongyu-yu/T-e3nn,https://github.com/Hongyu-yu/T-e3nn,2022-11-21 14:49:45,2023-02-21 16:36:26,2023-02-21 16:36:25,2145.0,,,2.0,,,,8.0,,,,26.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+257,MLXDM,,ml-iap,MIT,https://github.com/RowleyGroup/MLXDM,A Neural Network Potential with Rigorous Treatment of Long-Range Dispersion https://doi.org/10.1039/D2DD00150K.,8,True,['long-range'],RowleyGroup/MLXDM,https://github.com/RowleyGroup/MLXDM,2022-05-03 17:47:26,2024-08-08 21:52:47,2024-08-08 21:48:15,48.0,13.0,2.0,5.0,,,,6.0,,,,7.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+258,GEOM,,datasets,,https://github.com/learningmatter-mit/geom,GEOM: Energy-annotated molecular conformations.,7,False,['drug-discovery'],learningmatter-mit/geom,https://github.com/learningmatter-mit/geom,2020-06-03 17:58:37,2022-04-24 18:57:39,2022-04-24 18:57:39,95.0,,24.0,10.0,,2.0,11.0,188.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+259,tensorfieldnetworks,,rep-learn,MIT,https://github.com/tensorfieldnetworks/tensorfieldnetworks,Rotation- and translation-equivariant neural networks for 3D point clouds.,7,False,,tensorfieldnetworks/tensorfieldnetworks,https://github.com/tensorfieldnetworks/tensorfieldnetworks,2018-02-09 23:18:13,2020-01-07 17:22:16,2020-01-07 17:22:15,10.0,,28.0,9.0,2.0,1.0,2.0,151.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+260,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 17:45:42,2020-12-07 11:09:20,4.0,,26.0,9.0,1.0,5.0,,88.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+261,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 08:26:15,2017-07-11 08:25:39,9.0,,30.0,14.0,,,3.0,76.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+262,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 05:02:21,2020-07-15 04:55:41,96.0,,10.0,7.0,13.0,1.0,1.0,76.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+263,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 12:49:05,2020-03-11 15:25:51,160.0,,10.0,6.0,1.0,3.0,,59.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+264,Awesome-Crystal-GNNs,,community,MIT,https://github.com/kdmsit/Awesome-Crystal-GNNs,This repository contains a collection of resources and papers on GNN Models on Crystal Solid State Materials.,7,True,,kdmsit/Awesome-Crystal-GNNs,https://github.com/kdmsit/Awesome-Crystal-GNNs,2022-11-15 11:12:18,2024-06-16 16:02:41,2024-06-16 16:02:37,34.0,2.0,7.0,4.0,,,,54.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+265,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 23:29:39,2021-06-07 23:27:19,265.0,,7.0,6.0,1.0,,,38.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+266,AdsorbML,,rep-learn,MIT,https://github.com/Open-Catalyst-Project/AdsorbML,,7,False,"['surface-science', 'single-paper']",Open-Catalyst-Project/AdsorbML,https://github.com/Open-Catalyst-Project/AdsorbML,2022-11-30 01:38:20,2024-05-07 21:54:19,2023-07-31 16:28:09,56.0,,4.0,7.0,11.0,3.0,1.0,35.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+267,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 23:13:19,2020-05-01 20:12:23,49.0,,13.0,8.0,27.0,2.0,1.0,34.0,,,,5.0,,,1.0,1.0,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+268,chemlift,,language-models,MIT,https://github.com/lamalab-org/chemlift,Language-interfaced fine-tuning for chemistry.,7,True,,lamalab-org/chemlift,https://github.com/lamalab-org/chemlift,2023-07-10 06:54:07,2023-11-30 10:47:50,2023-10-14 16:50:14,36.0,,3.0,1.0,1.0,11.0,7.0,31.0,2023-11-30 19:42:07,0.0.1,1.0,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+269,Mat2Spec,,ml-dft,MIT,https://github.com/gomes-lab/Mat2Spec,Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings.,7,False,['spectroscopy'],gomes-lab/Mat2Spec,https://github.com/gomes-lab/Mat2Spec,2022-01-17 11:45:57,2022-04-17 17:12:29,2022-04-17 17:12:29,8.0,,10.0,,,,,27.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+270,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,False,,emilemathieu/escnn_jax,https://github.com/emilemathieu/escnn_jax,2023-06-15 09:45:45,2023-06-28 14:40:32,2023-06-28 14:39:56,203.0,,2.0,,,,,26.0,,,,8.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+271,LLM-Prop,,language-models,MIT,https://github.com/vertaix/LLM-Prop,A repository for the LLM-Prop implementation.,7,True,,vertaix/LLM-Prop,https://github.com/vertaix/LLM-Prop,2022-10-16 19:15:21,2024-04-26 14:20:54,2024-04-26 14:20:54,175.0,,5.0,2.0,,1.0,1.0,25.0,,,,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+272,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,,,,10.0,,,2.0,28.0,20.0,,,19.0,,aalto-boss,,,,https://pypi.org/project/aalto-boss,603.0,603.0,,,,2.0,,,,,,,,,,,,,,,cest-group/boss,https://gitlab.com/cest-group/boss,,
+273,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 14:53:52,2021-08-14 16:26:32,100.0,,4.0,2.0,3.0,13.0,3.0,16.0,,,2.0,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+274,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..,7,False,['structure-optimization'],,,2022-03-08 09:08:13,2022-03-08 09:08:13,,,,5.0,,,14.0,9.0,13.0,,,2.0,,agox,,,,https://pypi.org/project/agox,197.0,197.0,,,,2.0,,,,,,,,,,,,,,,agox/agox,https://gitlab.com/agox/agox,,
+275,COSMO Software Cookbook,,educational,BSD-3-Clause,https://github.com/lab-cosmo/atomistic-cookbook,The COSMO cookbook contains recipes for atomic-scale modelling for materials and molecules.,7,True,,lab-cosmo/software-cookbook,https://github.com/lab-cosmo/atomistic-cookbook,2023-05-23 10:33:47,2024-08-14 17:52:22,2024-08-14 17:43:05,69.0,5.0,1.0,16.0,57.0,2.0,10.0,12.0,,,,9.0,,,,,,,,,,,3.0,,,,,,,lab-cosmo/atomistic-cookbook,,,,,,,,,,,
+276,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..,7,True,,lab-cosmo/nice,https://github.com/lab-cosmo/nice,2020-07-03 08:47:41,2024-04-15 14:39:34,2024-04-15 14:39:33,233.0,,3.0,17.0,7.0,2.0,1.0,12.0,,,1.0,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+277,rxngenerator,,generative,MIT,https://github.com/tsudalab/rxngenerator,A generative model for molecular generation via multi-step chemical reactions.,7,False,,tsudalab/rxngenerator,https://github.com/tsudalab/rxngenerator,2021-06-18 07:44:53,2024-07-24 05:27:21,2022-08-09 07:21:05,16.0,,3.0,9.0,2.0,1.0,,11.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+278,AIS Square,,datasets,LGPL-3.0,https://github.com/deepmodeling/AIS-Square,"A collaborative and open-source platform for sharing AI for Science datasets, models, and workflows. Home of the..",7,True,"['community', 'model-repository']",deepmodeling/AIS-Square,https://github.com/deepmodeling/AIS-Square,2022-09-13 09:52:30,2024-07-30 13:14:00,2023-12-06 03:06:55,469.0,,8.0,8.0,210.0,5.0,1.0,10.0,,,,8.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+279,rho_learn,,ml-dft,MIT,https://github.com/jwa7/rho_learn,A proof-of-concept framework for torch-based learning of the electron density and related scalar fields.,7,False,,jwa7/rho_learn,https://github.com/jwa7/rho_learn,2023-03-27 16:59:34,2024-08-14 15:12:39,2024-03-20 15:20:39,111.0,,1.0,,4.0,,,3.0,,,,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+280,MAChINE,,educational,MIT,https://github.com/aimat-lab/MAChINE,Client-Server Web App to introduce usage of ML in materials science to beginners.,7,False,,aimat-lab/MAChINE,https://github.com/aimat-lab/MAChINE,2023-04-17 14:29:06,2023-09-29 14:20:12,2023-09-29 10:20:31,1026.0,,,,7.0,9.0,23.0,1.0,,,,7.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+281,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,False,"['xrd', 'single-paper']",aimat-lab/ML4pXRDs,https://github.com/aimat-lab/ML4pXRDs,2022-12-01 16:24:29,2023-07-14 08:17:06,2023-07-14 08:17:04,1320.0,,,3.0,,,,,2023-03-22 11:04:31,1.0,1.0,,,,,,,,0.0,,,,3.0,,,,,,2.0,,,,,,,,,,,,
+282,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 06:38:45,2024-06-07 15:51:11,2024-06-07 15:51:11,30.0,1.0,40.0,12.0,2.0,1.0,1.0,248.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+283,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,False,['magnetism'],Xiaoxun-Gong/DeepH-E3,https://github.com/Xiaoxun-Gong/DeepH-E3,2023-03-16 11:25:58,2023-04-04 13:27:01,2023-04-04 13:26:27,16.0,,16.0,6.0,,9.0,6.0,60.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+284,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 02:26:58,2022-03-12 02:26:41,107.0,,31.0,4.0,13.0,5.0,,58.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+285,DeepDFT,,ml-dft,MIT,https://github.com/peterbjorgensen/DeepDFT,Official implementation of DeepDFT model.,6,False,,peterbjorgensen/DeepDFT,https://github.com/peterbjorgensen/DeepDFT,2020-11-03 11:51:15,2023-02-28 15:37:49,2023-02-28 15:37:37,128.0,,7.0,1.0,,,5.0,54.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+286,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 17:25:55,2022-04-11 17:25:55,12.0,,5.0,5.0,,4.0,3.0,53.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+287,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 23:56:35,2018-07-09 23:56:34,27.0,,4.0,5.0,,2.0,1.0,39.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+288,MACE-Layer,,rep-learn,MIT,https://github.com/ACEsuit/mace-layer,Higher order equivariant graph neural networks for 3D point clouds.,6,False,,ACEsuit/mace-layer,https://github.com/ACEsuit/mace-layer,2022-11-09 17:03:41,2023-06-27 15:32:49,2023-06-06 10:09:58,19.0,,6.0,5.0,2.0,1.0,,33.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+289,MACE-tutorials,,educational,MIT,https://github.com/ilyes319/mace-tutorials,Another set of tutorials for the MACE interatomic potential by one of the authors.,6,True,"['ml-iap', 'rep-learn', 'md']",ilyes319/mace-tutorials,https://github.com/ilyes319/mace-tutorials,2023-09-11 18:09:18,2024-07-16 12:45:45,2024-07-16 12:45:42,7.0,2.0,9.0,3.0,,1.0,,31.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+290,milad,,rep-eng,GPL-3.0,https://github.com/muhrin/milad,Moment Invariants Local Atomic Descriptor.,6,False,['generative'],muhrin/milad,https://github.com/muhrin/milad,2020-04-23 09:14:24,2022-12-03 10:40:05,2022-12-03 10:39:59,110.0,,1.0,4.0,,,,29.0,,,,,,,2.0,2.0,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+291,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 01:53:35,2022-04-06 01:53:22,1.0,,8.0,12.0,,,,28.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+292,SciBot,,language-models,,https://github.com/CFN-softbio/SciBot,SciBot is a simple demo of building a domain-specific chatbot for science.,6,True,['ai-agent'],CFN-softbio/SciBot,https://github.com/CFN-softbio/SciBot,2023-06-12 12:41:44,2024-04-19 18:34:24,2024-04-19 18:17:00,22.0,,8.0,6.0,,,,28.0,,,,,,,1.0,1.0,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+293,ChargE3Net,,ml-dft,MIT,https://github.com/AIforGreatGood/charge3net,Higher-order equivariant neural networks for charge density prediction in materials.,6,True,['rep-learn'],AIforGreatGood/charge3net,https://github.com/AIforGreatGood/charge3net,2023-12-16 13:54:56,2024-08-01 20:33:49,2024-07-29 16:24:35,9.0,5.0,6.0,4.0,,1.0,1.0,28.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+294,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).,6,False,['lang-cpp'],,,2023-04-24 14:05:53,2023-04-24 14:05:53,,,,6.0,,,23.0,6.0,25.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,ashapeev/mlip-3,https://gitlab.com/ashapeev/mlip-3,,
+295,GLAMOUR,,rep-learn,MIT,https://github.com/learningmatter-mit/GLAMOUR,Graph Learning over Macromolecule Representations.,6,False,['single-paper'],learningmatter-mit/GLAMOUR,https://github.com/learningmatter-mit/GLAMOUR,2021-08-20 18:16:40,2022-12-31 17:56:21,2022-12-31 17:56:21,14.0,,6.0,3.0,,,8.0,20.0,2021-08-23 18:58:52,0.1,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+296,SA-GPR,,rep-eng,LGPL-3.0,https://github.com/dilkins/TENSOAP,Public repository for symmetry-adapted Gaussian Process Regression (SA-GPR).,6,True,['lang-c'],dilkins/TENSOAP,https://github.com/dilkins/TENSOAP,2020-05-04 14:19:01,2024-07-23 13:03:45,2024-07-23 13:03:44,26.0,1.0,13.0,3.0,10.0,2.0,5.0,19.0,2020-12-17 16:51:47,2020.0,1.0,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+297,EquivariantOperators.jl,,math,MIT,https://github.com/aced-differentiate/EquivariantOperators.jl,This package is deprecated. Functionalities are migrating to Porcupine.jl.,6,True,['lang-julia'],aced-differentiate/EquivariantOperators.jl,https://github.com/aced-differentiate/EquivariantOperators.jl,2021-11-29 03:36:21,2023-09-27 18:34:44,2023-09-27 18:34:44,62.0,,,4.0,,,,18.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+298,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 17:05:36,2021-08-30 17:05:32,162.0,,2.0,4.0,,2.0,,11.0,,,,7.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+299,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 17:23:46,2022-02-10 17:23:46,225.0,,6.0,16.0,10.0,5.0,3.0,11.0,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+300,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,,,,10.0,,,,,9.0,,,2.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,PANNAdevs/panna,https://gitlab.com/PANNAdevs/panna,,
+301,ML for catalysis tutorials,,educational,MIT,https://github.com/ulissigroup/ml_catalysis_tutorials,A jupyter book repo for tutorial on how to use OCP ML models for catalysis.,6,False,,ulissigroup/ml_catalysis_tutorials,https://github.com/ulissigroup/ml_catalysis_tutorials,2022-10-28 20:37:30,2022-10-31 18:06:07,2022-10-31 17:49:25,40.0,,1.0,4.0,,,,8.0,,,,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+302,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 11:23:13,2024-03-19 13:27:28,2024-03-19 13:27:02,107.0,,5.0,27.0,1.0,,,7.0,,,,9.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+303,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 05:31:21,2022-02-09 05:31:12,37.0,,2.0,3.0,,,3.0,7.0,2021-02-03 21:40:23,pub,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+304,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 13:47:44,2020-03-27 13:47:36,289.0,,3.0,3.0,1.0,,2.0,7.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+305,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..,6,True,['ml-iap'],lab-cosmo/equisolve,https://github.com/lab-cosmo/equisolve,2022-10-04 15:29:19,2023-10-27 10:03:59,2023-10-27 09:55:17,55.0,,1.0,17.0,43.0,19.0,4.0,5.0,,,,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+306,Cephalo,,language-models,Apache-2.0,https://github.com/lamm-mit/Cephalo,Multimodal Vision-Language Models for Bio-Inspired Materials Analysis and Design.,6,True,"['generative', 'multimodal', 'pretrained']",lamm-mit/Cephalo,https://github.com/lamm-mit/Cephalo,2024-05-28 12:29:13,2024-07-23 09:27:58,2024-07-23 09:27:57,24.0,24.0,1.0,1.0,,,,5.0,,,,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+307,cnine,,math,,https://github.com/risi-kondor/cnine,Cnine tensor library.,6,False,['lang-cpp'],risi-kondor/cnine,https://github.com/risi-kondor/cnine,2022-10-07 20:54:54,2024-08-14 20:13:06,2024-08-09 03:21:10,381.0,10.0,4.0,2.0,7.0,,1.0,4.0,,,,6.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,https://risi-kondor.github.io/cnine/,
+308,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.,6,False,['lang-fortran'],libAtoms/soap_turbo,https://github.com/libAtoms/soap_turbo,2021-03-19 15:20:25,2024-06-28 11:53:50,2023-05-24 09:42:00,36.0,,8.0,8.0,,5.0,3.0,4.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+309,KSR-DFT,,others,Apache-2.0,https://github.com/pedersor/ksr_dft,Kohn-Sham regularizer for machine-learned DFT functionals.,6,False,,pedersor/ksr_dft,https://github.com/pedersor/ksr_dft,2023-03-01 17:24:48,2023-03-04 07:20:22,2023-03-04 07:20:18,466.0,,,1.0,,,,4.0,,,,,,,,,,,,,,,1.0,,,,,,,,,True,,,,,,,,,
+310,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 09:57:29,2023-07-05 09:57:14,241.0,,1.0,2.0,,1.0,,3.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+311,ACEpsi.jl,,ml-wft,MIT,https://github.com/ACEsuit/ACEpsi.jl,ACE wave function parameterizations.,6,False,"['rep-eng', 'lang-julia']",ACEsuit/ACEpsi.jl,https://github.com/ACEsuit/ACEpsi.jl,2022-10-21 03:51:18,2024-04-12 06:18:19,2023-10-05 21:21:35,162.0,,,4.0,16.0,5.0,4.0,2.0,,,,6.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+312,OPTIMADE providers dashboard,,datasets,,https://www.optimade.org/providers-dashboard/,A dashboard of known providers.,6,False,,Materials-Consortia/providers-dashboard,https://github.com/Materials-Consortia/providers-dashboard,2020-06-17 16:15:07,2024-08-15 06:33:50,2024-08-01 23:27:42,139.0,21.0,3.0,19.0,141.0,10.0,18.0,1.0,,,,7.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+313,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 19:49:35,2023-01-10 19:49:13,1336.0,,,1.0,,,,1.0,,,,17.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+314,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..",6,True,['lang-julia'],ACEsuit/ACE1pack.jl,https://github.com/ACEsuit/ACE1pack.jl,2023-08-21 16:25:00,2023-08-21 16:30:19,2023-08-21 15:48:54,547.0,,,1.0,,,,,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,https://acesuit.github.io/ACE1pack.jl,
+315,COATI,,generative,Apache 2.0,https://github.com/terraytherapeutics/COATI,COATI: multi-modal contrastive pre-training for representing and traversing chemical space.,5,True,"['drug-discovery', 'multimodal', 'pretrained', 'rep-learn']",terraytherapeutics/COATI,https://github.com/terraytherapeutics/COATI,2023-08-11 14:56:39,2024-03-23 18:06:26,2024-03-23 18:06:26,16.0,,5.0,2.0,6.0,1.0,2.0,89.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+316,AI4Science101,,educational,,https://github.com/deepmodeling/AI4Science101,AI for Science.,5,False,,deepmodeling/AI4Science101,https://github.com/deepmodeling/AI4Science101,2022-06-19 02:26:48,2024-04-11 02:15:55,2022-09-04 02:06:18,139.0,,13.0,9.0,29.0,,1.0,83.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+317,crystal-text-llm,,language-models,CC-BY-NC-4.0,https://github.com/facebookresearch/crystal-text-llm,Large language models to generate stable crystals.,5,True,['materials-discovery'],facebookresearch/crystal-text-llm,https://github.com/facebookresearch/crystal-text-llm,2024-02-05 22:29:12,2024-06-18 17:10:52,2024-06-18 17:10:52,13.0,2.0,10.0,5.0,3.0,7.0,2.0,63.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+318,The Perovskite Database Project,,datasets,,https://github.com/Jesperkemist/perovskitedatabase,"Perovskite Database Project aims at making all perovskite device data, both past and future, available in a form..",5,True,['community'],Jesperkemist/perovskitedatabase,https://github.com/Jesperkemist/perovskitedatabase,2021-01-17 14:26:45,2024-03-07 11:09:21,2024-03-07 11:09:17,44.0,,18.0,3.0,7.0,1.0,,58.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+319,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 14:31:47,2019-09-17 14:31:19,2.0,,19.0,5.0,,1.0,,58.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+320,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 08:10:24,2022-07-22 08:10:24,49.0,,17.0,1.0,14.0,,,34.0,,,,11.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+321,Joint Multidomain Pre-Training (JMP),,uip,CC-BY-NC-4.0,https://github.com/facebookresearch/JMP,Code for From Molecules to Materials Pre-training Large Generalizable Models for Atomic Property Prediction.,5,True,"['pretrained', 'ml-iap', 'general-tool']",facebookresearch/JMP,https://github.com/facebookresearch/JMP,2024-03-14 23:10:10,2024-06-20 04:11:08,2024-05-07 08:19:12,1.0,,4.0,4.0,1.0,1.0,,32.0,,,,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+322,xDeepH,,ml-dft,LGPL-3.0,https://github.com/mzjb/xDeepH,Extended DeepH (xDeepH) method for magnetic materials.,5,False,"['magnetism', 'lang-julia']",mzjb/xDeepH,https://github.com/mzjb/xDeepH,2023-02-23 12:56:49,2023-06-14 11:44:53,2023-06-14 11:44:46,4.0,,3.0,3.0,,1.0,,31.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+323,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 21:04:09,2022-03-01 21:04:04,11.0,,2.0,5.0,,,,30.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+324,ML-DFT,,ml-dft,MIT,https://github.com/MihailBogojeski/ml-dft,A package for density functional approximation using machine learning.,5,False,,MihailBogojeski/ml-dft,https://github.com/MihailBogojeski/ml-dft,2020-09-14 22:15:56,2020-09-18 16:36:30,2020-09-18 16:36:30,9.0,,7.0,2.0,,1.0,1.0,23.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+325,Coarse-Graining-Auto-encoders,,unsupervised,,https://github.com/learningmatter-mit/Coarse-Graining-Auto-encoders,Implementation of coarse-graining Autoencoders.,5,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 21:39:34,2019-08-16 21:39:33,14.0,,7.0,6.0,,,,21.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+326,CSPML (crystal structure prediction with machine learning-based element substitution),,materials-discovery,,https://github.com/Minoru938/CSPML,Original implementation of CSPML.,5,True,['structure-prediction'],minoru938/cspml,https://github.com/Minoru938/CSPML,2022-01-15 10:59:27,2024-07-09 12:40:53,2024-07-09 12:40:53,23.0,16.0,8.0,2.0,,2.0,1.0,18.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+327,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 20:35:48,2023-11-01 20:35:44,39.0,,3.0,1.0,2.0,1.0,,17.0,2023-06-22 22:36:36,1.1.0,3.0,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+328,FieldSchNet,,rep-learn,MIT,https://github.com/atomistic-machine-learning/field_schnet,Deep neural network for molecules in external fields.,5,False,,atomistic-machine-learning/field_schnet,https://github.com/atomistic-machine-learning/field_schnet,2020-11-18 10:26:59,2022-05-19 09:28:38,2022-05-19 09:28:38,26.0,,4.0,2.0,1.0,1.0,,16.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+329,Does this material exist?,,community,MIT,https://thismaterialdoesnotexist.com/,Vote on whether you think predicted crystal structures could be synthesised.,5,True,"['for-fun', 'materials-discovery']",ml-evs/this-material-does-not-exist,https://github.com/ml-evs/this-material-does-not-exist,2023-12-01 18:16:28,2024-07-29 09:50:18,2024-04-10 12:32:06,16.0,,3.0,2.0,2.0,2.0,,15.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+330,3DSC 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.,5,True,"['superconductors', 'materials-discovery']",aimat-lab/3DSC,https://github.com/aimat-lab/3DSC,2021-11-02 09:07:57,2024-01-08 09:21:11,2024-01-08 09:21:11,53.0,,4.0,2.0,,1.0,,15.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+331,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 02:29:03,2022-01-24 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,,,,,,,,,,,,,,,,,,
+332,CraTENet,,rep-learn,MIT,https://github.com/lantunes/CraTENet,An attention-based deep neural network for thermoelectric transport properties.,5,False,['transport-phenomena'],lantunes/CraTENet,https://github.com/lantunes/CraTENet,2022-06-30 10:40:06,2023-04-05 01:13:22,2023-04-05 01:13:11,24.0,,1.0,1.0,,,,13.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+333,ACEHAL,,active-learning,,https://github.com/ACEsuit/ACEHAL,Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials.,5,True,['lang-julia'],ACEsuit/ACEHAL,https://github.com/ACEsuit/ACEHAL,2023-02-24 17:33:47,2023-10-01 12:19:41,2023-09-21 21:50:43,121.0,,7.0,5.0,15.0,4.0,6.0,11.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+334,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..,5,True,['magnetism'],StefanoSanvitoGroup/BERT-PSIE-TC,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,2023-01-25 10:27:26,2023-08-18 11:47:45,2023-08-18 12:48:31,36.0,,3.0,1.0,,,,11.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+335,QMLearn,,ml-esm,MIT,http://qmlearn.rutgers.edu/,Quantum Machine Learning by learning one-body reduced density matrices in the AO basis...,5,False,,,,2022-02-15 13:42:13,2022-02-15 13:42:13,,,,3.0,,,,,11.0,,,0.0,,,,,,,,,,,,2.0,,,,,,,,,True,,,,,,pavanello-research-group/qmlearn,https://gitlab.com/pavanello-research-group/qmlearn,,
+336,SciGlass,,datasets,MIT,https://github.com/drcassar/SciGlass,The database contains a vast set of data on the properties of glass materials.,5,True,,drcassar/SciGlass,https://github.com/drcassar/SciGlass,2019-06-19 19:36:32,2023-08-27 13:46:44,2023-08-27 13:46:44,28.0,,3.0,1.0,,,,10.0,2023-08-27 13:48:09,2.0.1,1.0,2.0,,,,,,,1.0,,,,3.0,,,,,,16.0,,,,,,,,,,,,
+337,InfGCN for Electron Density Estimation,,ml-dft,MIT,https://github.com/ccr-cheng/InfGCN-pytorch,Official implementation of the NeurIPS 23 spotlight paper of InfGCN.,5,True,['rep-learn'],ccr-cheng/infgcn-pytorch,https://github.com/ccr-cheng/InfGCN-pytorch,2023-10-01 21:21:40,2023-12-05 01:31:19,2023-12-05 01:31:14,3.0,,3.0,1.0,,,3.0,10.0,,,,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+338,GN-MM,,ml-iap,MIT,https://gitlab.com/zaverkin_v/gmnn,The Gaussian Moment Neural Network (GM-NN) package developed for large-scale atomistic simulations employing atomistic..,5,False,"['active-learning', 'md', 'rep-eng', 'magnetism']",,,2021-09-19 15:56:31,2021-09-19 15:56:31,,,,4.0,,,,,10.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,zaverkin_v/gmnn,https://gitlab.com/zaverkin_v/gmnn,,
+339,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,True,"['ai-agent', 'dataset']",maykcaldas/MAPI_LLM,https://github.com/maykcaldas/MAPI_LLM,2023-03-30 04:24:54,2024-04-20 03:16:17,2024-04-11 22:22:28,31.0,,2.0,1.0,7.0,,,8.0,2023-06-29 18:48:44,0.0.1,1.0,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+340,EGraFFBench,,rep-learn,,https://github.com/M3RG-IITD/MDBENCHGNN,,5,False,"['single-paper', 'benchmarking', 'ml-iap']",M3RG-IITD/MDBENCHGNN,https://github.com/M3RG-IITD/MDBENCHGNN,2023-07-06 18:15:34,2023-11-19 05:16:12,2023-11-19 05:14:44,161.0,,,,,4.0,,8.0,2023-07-16 05:46:38,0.1.0,1.0,5.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+341,GDB-9-Ex9 and ORNL_AISD-Ex,,datasets,,https://github.com/ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python,Distributed computing workflow for generation and analysis of large scale molecular datasets obtained running multi-..,5,True,,ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python,https://github.com/ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python,2023-01-06 18:09:54,2023-08-11 16:49:35,2023-08-11 16:49:35,47.0,,5.0,6.0,13.0,2.0,,6.0,,,,7.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+342,MEGAN: Multi Explanation Graph Attention Student,,xai,MIT,https://github.com/aimat-lab/graph_attention_student,Minimal implementation of graph attention student model architecture.,5,True,,aimat-lab/graph_attention_student,https://github.com/aimat-lab/graph_attention_student,2022-07-28 06:22:50,2024-07-31 07:57:28,2024-07-31 07:57:23,88.0,9.0,1.0,3.0,1.0,,2.0,5.0,,,,2.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+343,MolSLEPA,,generative,MIT,https://github.com/tsudalab/MolSLEPA,Interpretable Fragment-based Molecule Design with Self-learning Entropic Population Annealing.,5,False,['xai'],tsudalab/MolSLEPA,https://github.com/tsudalab/MolSLEPA,2023-04-10 15:04:55,2023-04-13 12:48:49,2023-04-13 12:48:49,11.0,,1.0,8.0,2.0,,,5.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+344,MEGAN,,xai,MIT,https://github.com/aimat-lab/graph_attention_student,Minimal implementation of graph attention student model architecture.,5,True,"['xai', 'rep-learn']",aimat-lab/graph_attention_student,https://github.com/aimat-lab/graph_attention_student,2022-07-28 06:22:50,2024-07-31 07:57:28,2024-07-31 07:57:23,88.0,9.0,1.0,3.0,1.0,,2.0,5.0,,,,2.0,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,
+345,MXenes4HER,,rep-eng,GPL-3.0,https://github.com/cnislab/MXenes4HER,Predicting hydrogen evolution (HER) activity over 4500 MXene materials https://doi.org/10.1039/D3TA00344B.,5,False,"['materials-discovery', 'catalysis', 'scikit-learn', 'single-paper']",cnislab/MXenes4HER,https://github.com/cnislab/MXenes4HER,2022-11-28 09:27:36,2023-02-27 18:08:05,2023-02-27 18:08:05,67.0,,3.0,1.0,1.0,,,5.0,,,,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+346,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 09:40:00,2024-05-24 05:53:06,2024-05-24 05:53:06,63.0,1.0,4.0,23.0,,,,4.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,True
+347,q-pac,,ml-esm,MIT,https://gitlab.com/jmargraf/qpac,Kernel charge equilibration method.,5,False,['electrostatics'],,,2020-11-15 20:11:27,2020-11-15 20:11:27,,,,4.0,,,2.0,,4.0,,,0.0,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,jmargraf/qpac,https://gitlab.com/jmargraf/qpac,,
+348,paper-ml-robustness-material-property,,unsupervised,BSD-3-Clause,https://github.com/mathsphy/paper-ml-robustness-material-property,A critical examination of robustness and generalizability of machine learning prediction of materials properties.,5,False,"['datasets', 'single-paper']",mathsphy/paper-ml-robustness-material-property,https://github.com/mathsphy/paper-ml-robustness-material-property,2023-02-21 02:38:13,2023-04-13 01:18:02,2023-04-13 01:18:02,3.0,,3.0,1.0,,,,4.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+349,SISSO++,,rep-eng,Apache-2.0,https://gitlab.com/sissopp_developers/sissopp,C++ Implementation of SISSO with python bindings.,5,False,['lang-cpp'],,,2021-04-30 14:20:59,2021-04-30 14:20:59,,,,3.0,,,2.0,18.0,3.0,,,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,sissopp_developers/sissopp,https://gitlab.com/sissopp_developers/sissopp,,
+350,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 18:35:45,2023-04-07 10:19:10,120.0,,1.0,7.0,1.0,,4.0,2.0,,,,10.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+351,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..,5,False,"['xai', 'rep-learn']",aimat-lab/visual_graph_datasets,https://github.com/aimat-lab/visual_graph_datasets,2023-06-01 11:33:18,2024-06-26 14:06:17,2024-06-26 14:06:13,53.0,3.0,2.0,3.0,,1.0,,1.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+352,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 08:29:46,2022-01-26 08:29:46,24.0,,,3.0,2.0,,,1.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+353,"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.",5,False,,Teoroo-CMC/DoE_Course_Material,https://github.com/Teoroo-CMC/DoE_Course_Material,2023-05-22 08:11:41,2023-06-26 12:48:17,2023-06-26 12:48:15,157.0,,15.0,2.0,1.0,,,1.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+354,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,"['pretrained', 'single-paper']",learningmatter-mit/per-site_cgcnn,https://github.com/learningmatter-mit/per-site_cgcnn,2023-05-30 18:59:03,2023-06-05 17:38:46,2023-06-05 17:38:41,28.0,,,,,,,1.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+355,Per-site PAiNN,,rep-learn,MIT,https://github.com/learningmatter-mit/per-site_painn,Fork of PaiNN for PerovskiteOrderingGCNNs.,5,False,"['probabilistic', 'pretrained', 'single-paper']",learningmatter-mit/per-site_painn,https://github.com/learningmatter-mit/per-site_painn,2023-06-04 14:23:49,2023-06-05 17:35:19,2023-06-05 17:30:34,123.0,,,,,,,1.0,,,,10.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+356,Geometric-GNNs,,community,,https://github.com/AlexDuvalinho/geometric-gnns,List of Geometric GNNs for 3D atomic systems.,4,False,"['datasets', 'educational', 'rep-learn']",AlexDuvalinho/geometric-gnns,https://github.com/AlexDuvalinho/geometric-gnns,2023-08-31 09:10:32,2024-02-29 16:25:54,2024-02-29 16:25:53,37.0,,6.0,1.0,3.0,,1.0,85.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+357,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 08:10:31,2020-10-19 08:10:30,13.0,,17.0,2.0,,,,67.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+358,MAGUS,,materials-discovery,,https://gitlab.com/bigd4/magus,Machine learning And Graph theory assisted Universal structure Searcher.,4,False,"['structure-prediction', 'active-learning']",,,2023-01-31 09:00:23,2023-01-31 09:00:23,,,,15.0,,,,,56.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,bigd4/magus,https://gitlab.com/bigd4/magus,,
+359,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 23:31:47,2024-02-23 21:44:03,2024-02-23 21:43:58,4.0,,8.0,1.0,1.0,2.0,1.0,32.0,,,,,atom2vec,,2.0,2.0,https://pypi.org/project/atom2vec,64.0,64.0,,,,3.0,,,,,,,,,,,,,,,,,,
+360,Allegro-Legato,,ml-iap,MIT,https://github.com/ibayashi-hikaru/allegro-legato,An extension of Allegro with enhanced robustness and time-to-failure.,4,False,['md'],ibayashi-hikaru/allegro-legato,https://github.com/ibayashi-hikaru/allegro-legato,2023-01-17 19:46:10,2023-08-03 22:25:11,2023-08-03 22:24:35,82.0,,1.0,1.0,,,,19.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+361,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 15:19:40,2024-04-09 12:06:56,2024-03-20 09:00:27,11.0,,1.0,2.0,3.0,,,17.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+362,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 14:22:00,2023-04-26 14:22:00,9.0,,3.0,2.0,,,,16.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+363,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 04:23:47,2023-10-07 04:07:59,9.0,,1.0,2.0,,1.0,,14.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+364,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..,4,False,"['lang-cpp', 'structure-prediction']",MDIL-SNU/SPINNER,https://github.com/MDIL-SNU/SPINNER,2021-07-15 02:10:58,2024-07-20 05:12:50,2021-11-25 07:58:15,102.0,,2.0,1.0,,1.0,,12.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+365,SOMD,,md,AGPL-3.0,https://github.com/initqp/somd,Molecular dynamics package designed for the SIESTA DFT code.,4,False,"['ml-iap', 'active-learning']",initqp/somd,https://github.com/initqp/somd,2023-03-09 19:00:41,2024-07-03 18:06:36,2024-07-03 17:46:18,301.0,2.0,2.0,1.0,11.0,,1.0,11.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+366,charge-density-models,,ml-dft,MIT,https://github.com/ulissigroup/charge-density-models,Tools to build charge density models using [fairchem](https://github.com/FAIR-Chem/fairchem).,4,False,['rep-learn'],ulissigroup/charge-density-models,https://github.com/ulissigroup/charge-density-models,2022-06-22 13:47:53,2023-11-29 15:07:42,2023-11-29 15:07:42,96.0,,2.0,2.0,16.0,1.0,2.0,10.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+367,paper-data-redundancy,,datasets,BSD-3-Clause,https://github.com/mathsphy/paper-data-redundancy,Repo for the paper Exploiting redundancy in large materials datasets for efficient machine learning with less data.,4,False,"['small-data', 'single-paper']",mathsphy/paper-data-redundancy,https://github.com/mathsphy/paper-data-redundancy,2023-06-10 15:00:28,2024-03-22 20:24:35,2024-03-22 20:24:34,17.0,,,1.0,,,,7.0,2023-10-11 14:09:07,1.0,1.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+368,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 16:37:18,2023-07-10 16:37:18,18.0,,4.0,2.0,2.0,,,7.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+369,Mapping out phase diagrams with generative classifiers,,generative,MIT,https://github.com/arnoldjulian/Mapping-out-phase-diagrams-with-generative-classifiers,Repository for our ``Mapping out phase diagrams with generative models paper.,4,False,['phase-transition'],arnoldjulian/Mapping-out-phase-diagrams-with-generative-classifiers,https://github.com/arnoldjulian/Mapping-out-phase-diagrams-with-generative-classifiers,2023-06-07 21:43:14,2023-06-27 08:12:29,2023-06-27 08:12:29,39.0,,2.0,1.0,,,,7.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+370,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 19:21:34,2021-05-04 19:21:30,10.0,,6.0,4.0,,,,6.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+371,automl-materials,,rep-eng,MIT,https://github.com/mm-tud/automl-materials,AutoML for Regression Tasks on Small Tabular Data in Materials Design.,4,False,"['automl', 'benchmarking', 'single-paper']",mm-tud/automl-materials,https://github.com/mm-tud/automl-materials,2022-10-07 09:49:18,2022-11-15 15:22:54,2022-11-15 15:22:45,6.0,,1.0,2.0,,,,5.0,,,,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+372,gkx: Green-Kubo Method in JAX,,rep-learn,MIT,https://github.com/sirmarcel/gkx,Green-Kubo + JAX + MLPs = Anharmonic Thermal Conductivities Done Fast.,4,False,['transport-phenomena'],sirmarcel/gkx,https://github.com/sirmarcel/gkx,2023-04-30 12:25:16,2024-03-20 09:05:20,2024-03-20 09:05:14,3.0,,,1.0,,,,3.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+373,ML-atomate,,materials-discovery,GPL-3.0,https://github.com/takahashi-akira-36m/ml_atomate,Machine learning-assisted Atomate code for autonomous computational materials screening.,4,False,"['active-learning', 'workflows']",takahashi-akira-36m/ml_atomate,https://github.com/takahashi-akira-36m/ml_atomate,2023-09-21 08:45:10,2023-11-17 09:54:23,2023-11-17 09:51:02,6.0,,1.0,1.0,,,,3.0,2023-09-29 03:52:46,stam_m_2023_fix,2.0,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+374,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 21:35:06,2023-01-13 21:28:08,134.0,,1.0,3.0,14.0,4.0,3.0,2.0,,,,10.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+375,halex,,ml-esm,,https://github.com/ecignoni/halex,Hamiltonian Learning for Excited States https://doi.org/10.48550/arXiv.2311.00844.,4,False,['excited-states'],ecignoni/halex,https://github.com/ecignoni/halex,2023-09-04 06:54:15,2024-02-08 10:20:53,2024-02-08 10:20:49,169.0,,,3.0,,1.0,,2.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+376,AI4ChemMat Hands-On Series,,educational,MPL-2.0,https://github.com/ai4chemmat/ai4chemmat.github.io,Hands-On Series organized by Chemistry and Materials working group at Argonne Nat Lab.,4,False,,ai4chemmat/ai4chemmat.github.io,https://github.com/ai4chemmat/ai4chemmat.github.io,2023-03-24 21:25:21,2024-04-24 16:32:18,2024-04-24 16:32:18,40.0,,,2.0,,,,1.0,,,,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+377,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 13:25:49,2023-07-19 13:25:49,46.0,,,3.0,,,,1.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+378,ACE Workflows,,ml-iap,,https://github.com/ACEsuit/ACEworkflows,Workflow Examples for ACE Models.,4,False,"['lang-julia', 'workflows']",ACEsuit/ACEworkflows,https://github.com/ACEsuit/ACEworkflows,2023-04-04 16:57:36,2023-10-12 18:01:00,2023-10-12 18:00:39,45.0,,1.0,3.0,7.0,1.0,,,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+379,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,848.0,848.0,,,,3.0,,,,,,,,,,,,,,,,,http://mlatom.com/manual/,
+380,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,,,,0.0,,,,,,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,jmargraf/gprep,https://gitlab.com/jmargraf/gprep,,
+381,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 21:25:42,2023-05-02 17:07:48,17.0,,1.0,4.0,3.0,,,,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+382,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 17:10:22,2022-10-18 17:10:22,17.0,,1.0,3.0,3.0,,,,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+383,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 18:23:17,2024-03-08 02:59:22,2024-03-08 02:59:22,93.0,,2.0,1.0,2.0,,,19.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+384,ALEBREW,,active-learning,https://github.com/nec-research/alebrew/blob/main/LICENSE.txt,https://github.com/nec-research/alebrew,Official repository for the paper Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic..,3,False,"['ml-iap', 'md']",nec-research/alebrew,https://github.com/nec-research/alebrew,2024-02-27 07:32:23,2024-03-17 13:51:57,2024-03-17 13:51:52,2.0,,3.0,1.0,,1.0,,9.0,,,,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+385,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.,3,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 15:59:08,2023-10-19 15:35:49,74.0,,,2.0,,,,6.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+386,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 20:38:23,2023-06-16 20:38:23,96.0,,1.0,2.0,27.0,,,6.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+387,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 15:54:27,2020-01-09 15:54:26,8.0,,2.0,4.0,,,1.0,6.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+388,interface-lammps-mlip-3,,md,GPL-2.0,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,An interface between LAMMPS and MLIP (version 3).,3,False,,,,2023-04-24 12:48:51,2023-04-24 12:48:51,,,,5.0,,,4.0,1.0,5.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,ivannovikov/interface-lammps-mlip-3,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,,
+389,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 23:46:05,2022-12-20 23:45:57,5.0,,2.0,2.0,,,,5.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+390,APET,,ml-dft,GPL-3.0,https://github.com/emotionor/APET,Atomic Positional Embedding-based Transformer.,3,False,"['density-of-states', 'transformer']",emotionor/APET,https://github.com/emotionor/APET,2023-03-06 01:53:16,2024-04-24 03:43:39,2023-09-28 03:16:11,11.0,,,2.0,,,,4.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+391,e3psi,,ml-esm,LGPL-3.0,https://github.com/muhrin/e3psi,Equivariant machine learning library for learning from electronic structures.,3,False,,muhrin/e3psi,https://github.com/muhrin/e3psi,2022-08-08 10:48:30,2024-01-05 12:59:56,2024-01-05 12:59:09,19.0,,,2.0,,,,3.0,,,,,,,1.0,1.0,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+392,torch_spex,,math,,https://github.com/lab-cosmo/torch_spex,Spherical expansions in PyTorch.,3,False,,lab-cosmo/torch_spex,https://github.com/lab-cosmo/torch_spex,2023-03-28 09:48:36,2024-03-28 05:33:01,2024-03-28 05:33:01,77.0,,2.0,18.0,35.0,7.0,2.0,3.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+393,PiNN Lab,,educational,GPL-3.0,https://github.com/Teoroo-CMC/PiNN_lab,Material for running a lab session on atomic neural networks.,3,False,,Teoroo-CMC/PiNN_lab,https://github.com/Teoroo-CMC/PiNN_lab,2019-03-17 22:09:30,2023-05-01 15:59:56,2023-05-01 15:59:22,9.0,,1.0,3.0,1.0,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+394,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 04:27:40,2022-10-11 04:27:40,6.0,,,1.0,,,,2.0,,,,3.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+395,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 18:48:12,2022-12-16 18:48:12,4.0,,,1.0,,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+396,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 05:46:08,2024-07-26 14:19:51,2023-05-24 09:18:24,111.0,,1.0,2.0,4.0,17.0,2.0,1.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+397,ML-for-CurieTemp-Predictions,,rep-eng,MIT,https://github.com/msg-byu/ML-for-CurieTemp-Predictions,Machine Learning Predictions of High-Curie-Temperature Materials.,3,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 19:05:50,2023-06-14 19:05:47,25.0,,,1.0,,,,1.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+398,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.,..",3,False,['material-defect'],HSE-LAMBDA/MEGNetSparse,https://github.com/HSE-LAMBDA/MEGNetSparse,2023-07-19 08:17:42,2023-08-21 17:11:34,2023-08-21 17:11:25,19.0,,1.0,2.0,,,,1.0,,,,2.0,MEGNetSparse,,1.0,1.0,https://pypi.org/project/MEGNetSparse,23.0,23.0,,,,3.0,,,,,,,,,,,,,,,,,,
+399,Magpie,,general-tool,MIT,https://bitbucket.org/wolverton/magpie/,Materials Agnostic Platform for Informatics and Exploration (Magpie).,3,False,['lang-java'],,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+400,PyFLAME,,ml-iap,,https://gitlab.com/flame-code/PyFLAME,An automated approach for developing neural network interatomic potentials with FLAME..,3,False,"['active-learning', 'structure-prediction', 'structure-optimization', 'rep-eng', 'lang-fortran']",,,2021-04-07 09:16:07,2021-04-07 09:16:07,,,,4.0,,,,,,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,flame-code/PyFLAME,https://gitlab.com/flame-code/PyFLAME,,
+401,nep-data,,datasets,,https://gitlab.com/brucefan1983/nep-data,Data related to the NEP machine-learned potential of GPUMD.,2,False,"['ml-iap', 'md', 'transport-phenomena']",,,2021-11-22 19:43:01,2021-11-22 19:43:01,,,,4.0,,,1.0,1.0,12.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,brucefan1983/nep-data,https://gitlab.com/brucefan1983/nep-data,,
+402,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 00:40:18,2021-11-09 00:40:10,17.0,,1.0,1.0,,1.0,,8.0,,,,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+403,A3MD,,ml-dft,,https://github.com/brunocuevas/a3md,MPNN-like + Analytic Density Model = Accurate electron densities.,2,False,"['rep-learn', 'single-paper']",brunocuevas/a3md,https://github.com/brunocuevas/a3md,2021-06-02 07:23:17,2021-12-02 17:10:39,2021-12-02 17:10:34,4.0,,1.0,2.0,,,,8.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+404,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 19:20:32,2023-02-22 19:20:32,8.0,,1.0,1.0,,,,7.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+405,tmQM_wB97MV Dataset,,datasets,,https://github.com/ulissigroup/tmQM_wB97MV,Code for Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV..,2,False,"['catalysis', 'rep-learn']",ulissigroup/tmqm_wB97MV,https://github.com/ulissigroup/tmQM_wB97MV,2023-07-17 21:40:20,2024-04-09 22:01:26,2024-04-09 22:01:26,17.0,,1.0,3.0,,,2.0,5.0,,,,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+406,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 08:06:22,2022-10-03 08:05:53,36.0,,,1.0,1.0,,,4.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+407,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..",2,False,,Minoru938/KmdPlus,https://github.com/Minoru938/KmdPlus,2023-03-26 10:06:34,2023-10-17 08:28:01,2023-10-17 08:28:01,7.0,,1.0,1.0,,,,3.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+408,AisNet,,ml-iap,MIT,https://github.com/loilisxka/AisNet,A Universal Interatomic Potential Neural Network with Encoded Local Environment Features..,2,False,,loilisxka/AisNet,https://github.com/loilisxka/AisNet,2022-10-11 05:54:59,2022-10-11 06:02:47,2022-10-11 05:58:06,2.0,,,1.0,,,,3.0,,,,2.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+409,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 11:20:39,2023-11-28 11:17:01,24.0,,,2.0,,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+410,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 15:48:41,2023-07-08 15:48:37,109.0,,,1.0,,,,2.0,,,,5.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+411,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 21:45:40,2022-12-22 21:45:40,19.0,,,2.0,,,,2.0,2022-08-18 05:25:24,1.0.0,1.0,4.0,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+412,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,76.0,76.0,,,,3.0,,,,,,,,,,,,,,,,,https://amp.readthedocs.io/,
+413,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/,
+414,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,,,,,,,,,,,,,,,,,,
+415,nnp-pre-training,,ml-iap,,https://github.com/jla-gardner/nnp-pre-training,Synthetic pre-training for neural-network interatomic potentials.,1,False,"['pretrained', 'md']",jla-gardner/nnp-pre-training,https://github.com/jla-gardner/nnp-pre-training,2023-07-12 11:58:29,2023-12-19 12:08:14,2023-12-19 12:08:14,11.0,,,1.0,,,,6.0,2023-12-19 12:02:35,1.0,1.0,2.0,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+416,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 03:57:10,2022-06-07 03:53:49,1.0,,,2.0,,,,3.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+417,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,,,,0.0,,,,,2.0,,,0.0,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,jmargraf/kdf,https://gitlab.com/jmargraf/kdf,,
+418,mag-ace,,ml-iap,,https://github.com/mttrin93/mag-ace,Magnetic ACE potential. FORTRAN interface for LAMMPS SPIN package.,1,False,"['magnetism', 'md', 'lang-fortran']",mttrin93/mag-ace,https://github.com/mttrin93/mag-ace,2023-12-26 19:00:40,2023-12-26 22:34:27,2023-12-26 22:34:27,7.0,,,1.0,,,,2.0,,,,,,,,,,,,,,,3.0,,,,,,,,,True,,,,,,,,,
+419,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 19:01:45,2022-10-22 19:01:42,12.0,,1.0,2.0,,,,1.0,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+420,GitHub topic materials-informatics,,community,,https://github.com/topics/materials-informatics,GitHub topic materials-informatics.,1,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+421,MateriApps,,community,,https://ma.issp.u-tokyo.ac.jp/en/,A Portal Site of Materials Science Simulation.,1,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,
+422,Allegro-JAX,,ml-iap,,https://github.com/mariogeiger/allegro-jax,JAX implementation of the Allegro interatomic potential.,0,True,,mariogeiger/allegro-jax,https://github.com/mariogeiger/allegro-jax,2023-07-02 19:00:00,2024-04-09 18:44:30,2024-04-09 18:44:30,7.0,,2.0,2.0,1.0,1.0,1.0,17.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+423,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,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+424,MLDensity,,ml-dft,,https://github.com/StefanoSanvitoGroup/MLdensity,Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure..,0,False,,StefanoSanvitoGroup/MLdensity,https://github.com/StefanoSanvitoGroup/MLdensity,2023-01-31 20:44:45,2024-05-27 12:28:57,2023-02-22 19:25:51,14.0,,,2.0,,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
diff --git a/latest-changes.md b/latest-changes.md
index b3e6b03..9d053fa 100644
--- a/latest-changes.md
+++ b/latest-changes.md
@@ -2,24 +2,71 @@
_Projects that have a higher project-quality score compared to the last update. There might be a variety of reasons, such as increased downloads or code activity._
-- apax (🥈18 · ⭐ 14 · 📈) - A flexible and performant framework for training machine learning potentials. MIT
-- IPSuite (🥈17 · ⭐ 16 · 📈) - A Python toolkit for FAIR development and deployment of machine-learned interatomic potentials. EPL-2.0 ML-IAP MD workflows HTC FAIR
-- rxngenerator (🥉7 · ⭐ 11 · 💀) - A generative model for molecular generation via multi-step chemical reactions. MIT
-- cnine (🥉6 · ⭐ 4 · 📈) - Cnine tensor library. Unlicensed C++
-- NequIP-JAX (🥉5 · ⭐ 17 · 💤) - JAX implementation of the NequIP interatomic potential. Unlicensed
+- paper-qa (🥇27 · ⭐ 3.8K · 📈) - LLM Chain for answering questions from documents with citations. Apache-2 ai-agent
+- DScribe (🥇23 · ⭐ 390 · 📈) - DScribe is a python package for creating machine learning descriptors for atomistic systems. Apache-2
+- pymatviz (🥇21 · ⭐ 150 · 📈) - A toolkit for visualizations in materials informatics. MIT general-tool probabilistic
+- e3nn-jax (🥈20 · ⭐ 170 · 📈) - jax library for E3 Equivariant Neural Networks. Apache-2
## 📉 Trending Down
_Projects that have a lower project-quality score compared to the last update. There might be a variety of reasons such as decreased downloads or code activity._
-- gpax (🥇16 · ⭐ 190 · 📉) - Gaussian Processes for Experimental Sciences. MIT probabilistic active-learning
-- OpenMM-ML (🥉13 · ⭐ 81 · 📉) - High level API for using machine learning models in OpenMM simulations. MIT ML-IAP
-- Pacemaker (🥈11 · ⭐ 62 · 📉) - Python package for fitting atomic cluster expansion (ACE) potentials. Custom
-- graphite (🥉8 · ⭐ 53 · 📉) - A repository for implementing graph network models based on atomic structures. MIT
+- dpdata (🥇23 · ⭐ 200 · 📉) - Manipulating multiple atomic simulation data formats, including DeePMD-kit, VASP, LAMMPS, ABACUS, etc. LGPL-3.0
+- Best-of Machine Learning with Python (🥇21 · ⭐ 16K · 📉) - A ranked list of awesome machine learning Python libraries. Updated weekly. CC-BY-4.0 general-ml Python
+- Open Databases Integration for Materials Design (OPTIMADE) (🥈17 · ⭐ 76 · 📉) - Specification of a common REST API for access to materials databases. CC-BY-4.0
+- openmm-torch (🥈16 · ⭐ 180 · 📉) - OpenMM plugin to define forces with neural networks. Custom ML-IAP C++
+- MatBench Discovery (🥈16 · ⭐ 82 · 📉) - An evaluation framework for machine learning models simulating high-throughput materials discovery. MIT datasets benchmarking model-repository
## ➕ Added Projects
_Projects that were recently added to this best-of list._
-- QH9 (🥈12 · ⭐ 470 · ➕) - A Quantum Hamiltonian Prediction Benchmark. CC-BY-NC-SA 4.0 ML-DFT
+- DPA-2 (🥇26 · ⭐ 1.4K · ➕) - Towards a universal large atomic model for molecular and material simulation https://doi.org/10.48550/arXiv.2312.15492. LGPL-3.0 ML-IAP pretrained workflows datasets
+- Graphormer (🥈16 · ⭐ 2K · ➕) - Graphormer is a general-purpose deep learning backbone for molecular modeling. MIT transformer pretrained
+- OpenML (🥈16 · ⭐ 660 · 💤) - Open Machine Learning. BSD-3 datasets
+- PMTransformer (🥇16 · ⭐ 82 · ➕) - Universal Transfer Learning in Porous Materials, including MOFs. MIT transfer-learning pretrained transformer
+- SevenNet (🥉14 · ⭐ 86 · ➕) - SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that.. GPL-3.0 ML-IAP MD pretrained
+- HydraGNN (🥈14 · ⭐ 56 · ➕) - Distributed PyTorch implementation of multi-headed graph convolutional neural networks. BSD-3
+- ChatMOF (🥈13 · ⭐ 53 · ➕) - Predict and Inverse design for metal-organic framework with large-language models (llms). MIT generative
+- MACE-MP (🥉12 · ⭐ 33 · ➕) - Pretrained foundation models for materials chemistry. MIT ML-IAP pretrained rep-learn MD
+- Neural-Network-Models-for-Chemistry (🥈11 · ⭐ 59 · ➕) - A collection of Nerual Network Models for chemistry. Unlicensed rep-learn
+- load-atoms (🥈11 · ⭐ 37 · ➕) - download and manipulate atomistic datasets. MIT data-structures
+- AI4Chemistry course (🥈10 · ⭐ 130 · ➕) - EPFL AI for chemistry course, Spring 2023. https://schwallergroup.github.io/ai4chem_course. MIT chemistry
+- HamGNN (🥈9 · ⭐ 49 · ➕) - An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix. GPL-3.0 rep-learn magnetism C-lang
+- AI for Science paper collection (🥉9 · ⭐ 43 · 🐣) - List the AI for Science papers accepted by top conferences. Apache-2
+- Q-stack (🥈9 · ⭐ 14 · ➕) - Stack of codes for dedicated pre- and post-processing tasks for Quantum Machine Learning (QML). MIT excited-states general-tool
+- MADICES Awesome Interoperability (🥉9 · ⭐ 1 · ➕) - Linked data interoperability resources of the Machine-actionable data interoperability for the chemical sciences.. MIT datasets
+- Awesome-Graph-Generation (🥉8 · ⭐ 260 · ➕) - A curated list of up-to-date graph generation papers and resources. Unlicensed rep-learn
+- Awesome Neural SBI (🥉8 · ⭐ 80 · ➕) - Community-sourced list of papers and resources on neural simulation-based inference. MIT active-learning
+- SiMGen (🥉8 · ⭐ 11 · ➕) - Zero Shot Molecular Generation via Similarity Kernels. MIT viz
+- Awesome-Crystal-GNNs (🥉7 · ⭐ 54 · ➕) - This repository contains a collection of resources and papers on GNN Models on Crystal Solid State Materials. MIT
+- AIS Square (🥉7 · ⭐ 10 · 💤) - A collaborative and open-source platform for sharing AI for Science datasets, models, and workflows. Home of the.. LGPL-3.0 community-resource model-repository
+- rho_learn (🥉7 · ⭐ 3 · ➕) - A proof-of-concept framework for torch-based learning of the electron density and related scalar fields. MIT
+- ChargE3Net (🥉6 · ⭐ 28 · ➕) - Higher-order equivariant neural networks for charge density prediction in materials. MIT rep-learn
+- ML for catalysis tutorials (🥉6 · ⭐ 8 · 💀) - A jupyter book repo for tutorial on how to use OCP ML models for catalysis. MIT
+- Cephalo (🥉6 · ⭐ 5 · 🐣) - Multimodal Vision-Language Models for Bio-Inspired Materials Analysis and Design. Apache-2 generative multimodal pretrained
+- KSR-DFT (🥇6 · ⭐ 4 · 💀) - Kohn-Sham regularizer for machine-learned DFT functionals. Apache-2
+- ACEpsi.jl (🥉6 · ⭐ 2 · 💤) - ACE wave function parameterizations. MIT rep-eng Julia
+- crystal-text-llm (🥉5 · ⭐ 63 · 🐣) - Large language models to generate stable crystals. CC-BY-NC-4.0 materials-discovery
+- The Perovskite Database Project (🥉5 · ⭐ 58 · ➕) - Perovskite Database Project aims at making all perovskite device data, both past and future, available in a form.. Unlicensed community-resource
+- Joint Multidomain Pre-Training (JMP) (🥉5 · ⭐ 32 · 🐣) - Code for From Molecules to Materials Pre-training Large Generalizable Models for Atomic Property Prediction. CC-BY-NC-4.0 pretrained ML-IAP general-tool
+- QMLearn (🥈5 · ⭐ 11 · 💀) - Quantum Machine Learning by learning one-body reduced density matrices in the AO basis... MIT
+- InfGCN for Electron Density Estimation (🥉5 · ⭐ 10 · 💤) - Official implementation of the NeurIPS 23 spotlight paper of InfGCN. MIT rep-learn
+- GN-MM (🥉5 · ⭐ 10 · 💀) - The Gaussian Moment Neural Network (GM-NN) package developed for large-scale atomistic simulations employing atomistic.. MIT active-learning MD rep-eng magnetism
+- EGraFFBench (🥉5 · ⭐ 8 · 💤) - Unlicensed single-paper benchmarking ML-IAP
+- GDB-9-Ex9 and ORNL_AISD-Ex (🥉5 · ⭐ 6 · 💤) - Distributed computing workflow for generation and analysis of large scale molecular datasets obtained running multi-.. Unlicensed
+- MXenes4HER (🥉5 · ⭐ 5 · 💀) - Predicting hydrogen evolution (HER) activity over 4500 MXene materials https://doi.org/10.1039/D3TA00344B. GPL-3.0 materials-discovery catalysis scikit-learn single-paper
+- Geometric-GNNs (🥉4 · ⭐ 85 · ➕) - List of Geometric GNNs for 3D atomic systems. Unlicensed datasets educational rep-learn
+- MAGUS (🥉4 · ⭐ 56 · 💀) - Machine learning And Graph theory assisted Universal structure Searcher. Unlicensed structure-prediction active-learning
+- Allegro-Legato (🥉4 · ⭐ 19 · 💤) - An extension of Allegro with enhanced robustness and time-to-failure. MIT MD
+- Mapping out phase diagrams with generative classifiers (🥉4 · ⭐ 7 · 💀) - Repository for our ``Mapping out phase diagrams with generative models paper. MIT phase-transition
+- automl-materials (🥉4 · ⭐ 5 · 💀) - AutoML for Regression Tasks on Small Tabular Data in Materials Design. MIT autoML benchmarking single-paper
+- ML-atomate (🥉4 · ⭐ 3 · 💤) - Machine learning-assisted Atomate code for autonomous computational materials screening. GPL-3.0 active-learning workflows
+- AI4ChemMat Hands-On Series (🥉4 · ⭐ 1 · ➕) - Hands-On Series organized by Chemistry and Materials working group at Argonne Nat Lab. MPL-2.0
+- ALEBREW (🥉3 · ⭐ 9 · 🐣) - Official repository for the paper Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic.. Custom ML-IAP MD
+- PyFLAME (🥉3 · 💀) - An automated approach for developing neural network interatomic potentials with FLAME.. Unlicensed active-learning structure-prediction structure-optimization rep-eng Fortran
+- tmQM_wB97MV Dataset (🥉2 · ⭐ 5 · ➕) - Code for Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV.. Unlicensed catalysis rep-learn
+- AisNet (🥉2 · ⭐ 3 · 💀) - A Universal Interatomic Potential Neural Network with Encoded Local Environment Features.. MIT
+- nnp-pre-training (🥉1 · ⭐ 6 · 💤) - Synthetic pre-training for neural-network interatomic potentials. Unlicensed pretrained MD
+- mag-ace (🥉1 · ⭐ 2 · 💤) - Magnetic ACE potential. FORTRAN interface for LAMMPS SPIN package. Unlicensed magnetism MD Fortran