From 92e26aedfe9ff28c2e0a40d46ff9910bd3b3d5b3 Mon Sep 17 00:00:00 2001 From: Irratzo Date: Thu, 24 Oct 2024 18:22:04 +0000 Subject: [PATCH] Update best-of list for version 2024.10.24 --- README.md | 473 ++++++++++++++++---------------- history/2024-10-24_changes.md | 20 ++ history/2024-10-24_projects.csv | 432 +++++++++++++++++++++++++++++ latest-changes.md | 25 +- 4 files changed, 702 insertions(+), 248 deletions(-) create mode 100644 history/2024-10-24_changes.md create mode 100644 history/2024-10-24_projects.csv diff --git a/README.md b/README.md index 2d0ff78..f2aa15d 100644 --- a/README.md +++ b/README.md @@ -85,7 +85,7 @@ _Projects that focus on enabling active learning, iterative learning schemes for
FLARE (🥇22 · ⭐ 290) - An open-source Python package for creating fast and accurate interatomic potentials. MIT C++ ML-IAP -- [GitHub](https://github.com/mir-group/flare) (👨‍💻 42 · 🔀 67 · 📥 8 · 📦 12 · 📋 220 - 16% open · ⏱️ 12.10.2024): +- [GitHub](https://github.com/mir-group/flare) (👨‍💻 43 · 🔀 68 · 📥 8 · 📦 12 · 📋 220 - 16% open · ⏱️ 23.10.2024): ``` git clone https://github.com/mir-group/flare @@ -98,12 +98,12 @@ _Projects that focus on enabling active learning, iterative learning schemes for ``` git clone https://github.com/zincware/IPSuite ``` -- [PyPi](https://pypi.org/project/ipsuite) (📥 360 / month · ⏱️ 08.08.2024): +- [PyPi](https://pypi.org/project/ipsuite) (📥 400 / month · ⏱️ 08.08.2024): ``` pip install ipsuite ```
-
Finetuna (🥉10 · ⭐ 44) - Active Learning for Machine Learning Potentials. MIT +
Finetuna (🥉10 · ⭐ 45) - Active Learning for Machine Learning Potentials. MIT - [GitHub](https://github.com/ulissigroup/finetuna) (👨‍💻 11 · 🔀 11 · 📦 1 · 📋 20 - 25% open · ⏱️ 15.05.2024): @@ -139,7 +139,7 @@ _Projects that collect atomistic ML resources or foster communication within com
Best-of Machine Learning with Python (🥇23 · ⭐ 16K) - A ranked list of awesome machine learning Python libraries. Updated weekly. CC-BY-4.0 general-ml Python -- [GitHub](https://github.com/ml-tooling/best-of-ml-python) (👨‍💻 47 · 🔀 2.4K · 📋 61 - 44% open · ⏱️ 10.10.2024): +- [GitHub](https://github.com/ml-tooling/best-of-ml-python) (👨‍💻 47 · 🔀 2.4K · 📋 61 - 44% open · ⏱️ 24.10.2024): ``` git clone https://github.com/ml-tooling/best-of-ml-python @@ -153,14 +153,14 @@ _Projects that collect atomistic ML resources or foster communication within com git clone https://github.com/naganandy/graph-based-deep-learning-literature ```
-
MatBench Discovery (🥇19 · ⭐ 98) - An evaluation framework for machine learning models simulating high-throughput materials discovery. MIT datasets benchmarking model-repository +
MatBench Discovery (🥇19 · ⭐ 99) - An evaluation framework for machine learning models simulating high-throughput materials discovery. MIT datasets benchmarking model-repository -- [GitHub](https://github.com/janosh/matbench-discovery) (👨‍💻 9 · 🔀 14 · 📦 3 · 📋 39 - 10% open · ⏱️ 19.10.2024): +- [GitHub](https://github.com/janosh/matbench-discovery) (👨‍💻 9 · 🔀 15 · 📦 3 · 📋 39 - 10% open · ⏱️ 19.10.2024): ``` git clone https://github.com/janosh/matbench-discovery ``` -- [PyPi](https://pypi.org/project/matbench-discovery) (📥 1.9K / month · ⏱️ 11.09.2024): +- [PyPi](https://pypi.org/project/matbench-discovery) (📥 2.1K / month · ⏱️ 11.09.2024): ``` pip install matbench-discovery ``` @@ -172,7 +172,7 @@ _Projects that collect atomistic ML resources or foster communication within com ``` git clone https://github.com/materialsproject/matbench ``` -- [PyPi](https://pypi.org/project/matbench) (📥 680 / month · 📦 2 · ⏱️ 27.07.2022): +- [PyPi](https://pypi.org/project/matbench) (📥 730 / month · 📦 2 · ⏱️ 27.07.2022): ``` pip install matbench ``` @@ -193,17 +193,17 @@ _Projects that collect atomistic ML resources or foster communication within com git clone https://github.com/GT4SD/gt4sd-core ```
-
AI for Science Resources (🥈13 · ⭐ 500) - List of resources for AI4Science research, including learning resources. GPL-3.0 license +
AI for Science Resources (🥈13 · ⭐ 510) - List of resources for AI4Science research, including learning resources. GPL-3.0 license -- [GitHub](https://github.com/divelab/AIRS) (👨‍💻 29 · 🔀 58 · 📋 15 - 6% open · ⏱️ 03.09.2024): +- [GitHub](https://github.com/divelab/AIRS) (👨‍💻 29 · 🔀 58 · 📋 16 - 12% open · ⏱️ 03.09.2024): ``` git clone https://github.com/divelab/AIRS ```
-
Neural-Network-Models-for-Chemistry (🥈10 · ⭐ 84) - A collection of Nerual Network Models for chemistry. Unlicensed rep-learn +
Neural-Network-Models-for-Chemistry (🥈11 · ⭐ 85) - A collection of Nerual Network Models for chemistry. Unlicensed rep-learn -- [GitHub](https://github.com/Eipgen/Neural-Network-Models-for-Chemistry) (👨‍💻 3 · 🔀 12 · 📋 2 - 50% open · ⏱️ 18.10.2024): +- [GitHub](https://github.com/Eipgen/Neural-Network-Models-for-Chemistry) (👨‍💻 3 · 🔀 12 · 📋 2 - 50% open · ⏱️ 22.10.2024): ``` git clone https://github.com/Eipgen/Neural-Network-Models-for-Chemistry @@ -219,7 +219,7 @@ _Projects that collect atomistic ML resources or foster communication within com
Awesome Materials Informatics (🥈9 · ⭐ 380) - Curated list of known efforts in materials informatics, i.e. in modern materials science. Custom -- [GitHub](https://github.com/tilde-lab/awesome-materials-informatics) (👨‍💻 19 · 🔀 82 · ⏱️ 18.09.2024): +- [GitHub](https://github.com/tilde-lab/awesome-materials-informatics) (👨‍💻 19 · 🔀 84 · ⏱️ 18.09.2024): ``` git clone https://github.com/tilde-lab/awesome-materials-informatics @@ -265,7 +265,7 @@ _Projects that collect atomistic ML resources or foster communication within com git clone https://github.com/yuanqidu/awesome-graph-generation ```
-
Awesome Neural SBI (🥉7 · ⭐ 89) - Community-sourced list of papers and resources on neural simulation-based inference. MIT active-learning +
Awesome Neural SBI (🥉7 · ⭐ 91) - Community-sourced list of papers and resources on neural simulation-based inference. MIT active-learning - [GitHub](https://github.com/smsharma/awesome-neural-sbi) (👨‍💻 3 · 🔀 6 · 📋 2 - 50% open · ⏱️ 17.06.2024): @@ -342,14 +342,14 @@ _Datasets, databases and trained models for atomistic ML._ 🔗 ZINC20 - A free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable.. graph biomolecules -
OPTIMADE Python tools (🥇26 · ⭐ 68 · 📉) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT +
OPTIMADE Python tools (🥇27 · ⭐ 69 · 📈) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT -- [GitHub](https://github.com/Materials-Consortia/optimade-python-tools) (👨‍💻 28 · 🔀 42 · 📦 61 · 📋 460 - 23% open · ⏱️ 20.10.2024): +- [GitHub](https://github.com/Materials-Consortia/optimade-python-tools) (👨‍💻 28 · 🔀 42 · 📦 61 · 📋 460 - 23% open · ⏱️ 22.10.2024): ``` git clone https://github.com/Materials-Consortia/optimade-python-tools ``` -- [PyPi](https://pypi.org/project/optimade) (📥 12K / month · 📦 4 · ⏱️ 15.10.2024): +- [PyPi](https://pypi.org/project/optimade) (📥 14K / month · 📦 4 · ⏱️ 15.10.2024): ``` pip install optimade ``` @@ -358,28 +358,28 @@ _Datasets, databases and trained models for atomistic ML._ conda install -c conda-forge optimade ```
-
FAIR Chemistry datasets (🥇25 · ⭐ 810 · 📈) - Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project. MIT catalysis +
MPContribs (🥇25 · ⭐ 35) - Platform for materials scientists to contribute and disseminate their materials data through Materials Project. MIT -- [GitHub](https://github.com/FAIR-Chem/fairchem) (👨‍💻 42 · 🔀 240 · 📋 230 - 13% open · ⏱️ 20.10.2024): +- [GitHub](https://github.com/materialsproject/MPContribs) (👨‍💻 25 · 🔀 20 · 📦 40 · 📋 100 - 21% open · ⏱️ 22.10.2024): ``` - git clone https://github.com/FAIR-Chem/fairchem + git clone https://github.com/materialsproject/MPContribs ``` -- [PyPi](https://pypi.org/project/fairchem-core) (📥 1.9K / month · 📦 1 · ⏱️ 14.09.2024): +- [PyPi](https://pypi.org/project/mpcontribs-client) (📥 12K / month · 📦 3 · ⏱️ 17.10.2024): ``` - pip install fairchem-core + pip install mpcontribs-client ```
-
MPContribs (🥇25 · ⭐ 35) - Platform for materials scientists to contribute and disseminate their materials data through Materials Project. MIT +
FAIR Chemistry datasets (🥇24 · ⭐ 830 · 📉) - Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project. MIT catalysis -- [GitHub](https://github.com/materialsproject/MPContribs) (👨‍💻 25 · 🔀 20 · 📦 40 · 📋 100 - 21% open · ⏱️ 18.10.2024): +- [GitHub](https://github.com/FAIR-Chem/fairchem) (👨‍💻 42 · 🔀 240 · 📋 230 - 13% open · ⏱️ 23.10.2024): ``` - git clone https://github.com/materialsproject/MPContribs + git clone https://github.com/FAIR-Chem/fairchem ``` -- [PyPi](https://pypi.org/project/mpcontribs-client) (📥 11K / month · 📦 3 · ⏱️ 17.10.2024): +- [PyPi](https://pypi.org/project/fairchem-core) (📥 2.2K / month · 📦 1 · ⏱️ 14.09.2024): ``` - pip install mpcontribs-client + pip install fairchem-core ```
Open Databases Integration for Materials Design (OPTIMADE) (🥈18 · ⭐ 83) - Specification of a common REST API for access to materials databases. CC-BY-4.0 @@ -397,14 +397,14 @@ _Datasets, databases and trained models for atomistic ML._ ``` git clone https://github.com/jla-gardner/load-atoms ``` -- [PyPi](https://pypi.org/project/load-atoms) (📥 3.1K / month · ⏱️ 04.10.2024): +- [PyPi](https://pypi.org/project/load-atoms) (📥 3.6K / month · ⏱️ 04.10.2024): ``` pip install load-atoms ```
-
QH9 (🥈13 · ⭐ 500) - A Quantum Hamiltonian Prediction Benchmark. CC-BY-NC-SA-4.0 ML-DFT +
QH9 (🥈13 · ⭐ 510) - A Quantum Hamiltonian Prediction Benchmark. CC-BY-NC-SA-4.0 ML-DFT -- [GitHub](https://github.com/divelab/AIRS) (👨‍💻 29 · 🔀 58 · 📋 15 - 6% open · ⏱️ 03.09.2024): +- [GitHub](https://github.com/divelab/AIRS) (👨‍💻 29 · 🔀 58 · 📋 16 - 12% open · ⏱️ 03.09.2024): ``` git clone https://github.com/divelab/AIRS @@ -434,14 +434,14 @@ _Datasets, databases and trained models for atomistic ML._ git clone https://github.com/HSE-LAMBDA/ai4material_design ```
-
Meta Open Materials 2024 (OMat24) Dataset (🥈9 · ➕) - Contains over 100 million Density Functional Theory calculations focused on structural and compositional diversity. CC-BY-4.0 +
Meta Open Materials 2024 (OMat24) Dataset (🥈9) - Contains over 100 million Density Functional Theory calculations focused on structural and compositional diversity. CC-BY-4.0 - [GitHub](): ``` git clone https://github.com/https://github.com/FAIR-Chem/fairchem ``` -- [PyPi](https://pypi.org/project/fairchem-core) (📥 1.9K / month · 📦 1 · ⏱️ 14.09.2024): +- [PyPi](https://pypi.org/project/fairchem-core) (📥 2.2K / month · 📦 1 · ⏱️ 14.09.2024): ``` pip install fairchem-core ``` @@ -454,7 +454,7 @@ _Datasets, databases and trained models for atomistic ML._ git clone https://github.com/deepmodeling/AIS-Square ```
-
The Perovskite Database Project (🥉5 · ⭐ 59 · 💤) - Perovskite Database Project aims at making all perovskite device data, both past and future, available in a form.. Unlicensed community-resource +
The Perovskite Database Project (🥉5 · ⭐ 60 · 💤) - Perovskite Database Project aims at making all perovskite device data, both past and future, available in a form.. Unlicensed community-resource - [GitHub](https://github.com/Jesperkemist/perovskitedatabase) (👨‍💻 2 · 🔀 20 · ⏱️ 07.03.2024): @@ -479,8 +479,8 @@ _Datasets, databases and trained models for atomistic ML._ - GEOM (🥉7 · ⭐ 200 · 💀) - GEOM: Energy-annotated molecular conformations. Unlicensed drug-discovery - ANI-1x Datasets (🥉6 · ⭐ 58 · 💀) - 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 +- GDB-9-Ex9 and ORNL_AISD-Ex (🥉6 · ⭐ 6 · 💀) - Distributed computing workflow for generation and analysis of large scale molecular datasets obtained running multi-.. Unlicensed - SciGlass (🥉5 · ⭐ 10 · 💀) - The database contains a vast set of data on the properties of glass materials. MIT -- 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 - linear-regression-benchmarks (🥉5 · ⭐ 1 · 💀) - Data sets used for linear regression benchmarks. MIT benchmarking single-paper - paper-data-redundancy (🥉4 · ⭐ 8) - Repo for the paper Exploiting redundancy in large materials datasets for efficient machine learning with less data. BSD-3 small-data single-paper - Visual Graph Datasets (🥉4 · ⭐ 2) - Datasets for the training of graph neural networks (GNNs) and subsequent visualization of attributional explanations.. MIT XAI rep-learn @@ -503,7 +503,7 @@ _Projects that focus on providing data structures used in atomistic machine lear ``` git clone https://github.com/deepmodeling/dpdata ``` -- [PyPi](https://pypi.org/project/dpdata) (📥 45K / month · 📦 40 · ⏱️ 20.09.2024): +- [PyPi](https://pypi.org/project/dpdata) (📥 53K / month · 📦 40 · ⏱️ 20.09.2024): ``` pip install dpdata ``` @@ -514,20 +514,20 @@ _Projects that focus on providing data structures used in atomistic machine lear
Metatensor (🥈21 · ⭐ 52) - Self-describing sparse tensor data format for atomistic machine learning and beyond. BSD-3 Rust C-lang C++ Python -- [GitHub](https://github.com/metatensor/metatensor) (👨‍💻 22 · 🔀 16 · 📥 28K · 📦 13 · 📋 210 - 34% open · ⏱️ 18.10.2024): +- [GitHub](https://github.com/metatensor/metatensor) (👨‍💻 24 · 🔀 17 · 📥 29K · 📦 13 · 📋 220 - 32% open · ⏱️ 24.10.2024): ``` git clone https://github.com/lab-cosmo/metatensor ```
-
mp-pyrho (🥉17 · ⭐ 37 · 💤) - Tools for re-griding volumetric quantum chemistry data for machine-learning purposes. Custom ML-DFT +
mp-pyrho (🥉18 · ⭐ 37) - Tools for re-griding volumetric quantum chemistry data for machine-learning purposes. Custom ML-DFT -- [GitHub](https://github.com/materialsproject/pyrho) (👨‍💻 8 · 🔀 7 · 📦 25 · 📋 4 - 25% open · ⏱️ 23.02.2024): +- [GitHub](https://github.com/materialsproject/pyrho) (👨‍💻 10 · 🔀 7 · 📦 25 · 📋 5 - 40% open · ⏱️ 22.10.2024): ``` git clone https://github.com/materialsproject/pyrho ``` -- [PyPi](https://pypi.org/project/mp-pyrho) (📥 16K / month · 📦 3 · ⏱️ 23.02.2024): +- [PyPi](https://pypi.org/project/mp-pyrho) (📥 17K / month · 📦 5 · ⏱️ 22.10.2024): ``` pip install mp-pyrho ``` @@ -552,7 +552,7 @@ _Projects and models that focus on quantities of DFT, such as density functional
JAX-DFT (🥇25 · ⭐ 34K) - This library provides basic building blocks that can construct DFT calculations as a differentiable program. Apache-2 -- [GitHub](https://github.com/google-research/google-research) (👨‍💻 810 · 🔀 7.9K · 📋 1.8K - 81% open · ⏱️ 15.10.2024): +- [GitHub](https://github.com/google-research/google-research) (👨‍💻 810 · 🔀 7.9K · 📋 1.8K - 81% open · ⏱️ 21.10.2024): ``` git clone https://github.com/google-research/google-research @@ -560,15 +560,15 @@ _Projects and models that focus on quantities of DFT, such as density functional
MALA (🥇19 · ⭐ 81) - Materials Learning Algorithms. A framework for machine learning materials properties from first-principles data. BSD-3 -- [GitHub](https://github.com/mala-project/mala) (👨‍💻 44 · 🔀 24 · 📦 2 · 📋 280 - 13% open · ⏱️ 21.10.2024): +- [GitHub](https://github.com/mala-project/mala) (👨‍💻 44 · 🔀 24 · 📦 2 · 📋 280 - 13% open · ⏱️ 24.10.2024): ``` git clone https://github.com/mala-project/mala ```
-
QHNet (🥇13 · ⭐ 500) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 rep-learn +
QHNet (🥇13 · ⭐ 510) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 rep-learn -- [GitHub](https://github.com/divelab/AIRS) (👨‍💻 29 · 🔀 58 · 📋 15 - 6% open · ⏱️ 03.09.2024): +- [GitHub](https://github.com/divelab/AIRS) (👨‍💻 29 · 🔀 58 · 📋 16 - 12% open · ⏱️ 03.09.2024): ``` git clone https://github.com/divelab/AIRS @@ -606,7 +606,7 @@ _Projects and models that focus on quantities of DFT, such as density functional git clone https://github.com/XanaduAI/GradDFT ```
-
HamGNN (🥈8 · ⭐ 56) - An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix. GPL-3.0 rep-learn magnetism C-lang +
HamGNN (🥈8 · ⭐ 58) - An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix. GPL-3.0 rep-learn magnetism C-lang - [GitHub](https://github.com/QuantumLab-ZY/HamGNN) (👨‍💻 2 · 🔀 15 · 📋 31 - 80% open · ⏱️ 26.09.2024): @@ -614,9 +614,9 @@ _Projects and models that focus on quantities of DFT, such as density functional git clone https://github.com/QuantumLab-ZY/HamGNN ```
-
ChargE3Net (🥈7 · ⭐ 30) - Higher-order equivariant neural networks for charge density prediction in materials. MIT rep-learn +
ChargE3Net (🥈7 · ⭐ 31) - Higher-order equivariant neural networks for charge density prediction in materials. MIT rep-learn -- [GitHub](https://github.com/AIforGreatGood/charge3net) (👨‍💻 2 · 🔀 9 · 📋 5 - 40% open · ⏱️ 15.08.2024): +- [GitHub](https://github.com/AIforGreatGood/charge3net) (👨‍💻 2 · 🔀 9 · 📋 6 - 50% open · ⏱️ 15.08.2024): ``` git clone https://github.com/AIforGreatGood/charge3net @@ -660,11 +660,11 @@ _Projects and models that focus on quantities of DFT, such as density functional - xDeepH (🥉5 · ⭐ 33 · 💀) - 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 - rho_learn (🥉5 · ⭐ 4 · 💀) - A proof-of-concept workflow for torch-based electron density learning. MIT +- MALADA (🥉4 · ⭐ 1) - MALA Data Acquisition: Helpful tools to build data for MALA. BSD-3 - gprep (🥉4 · 💀) - Fitting DFTB repulsive potentials with GPR. MIT single-paper - DeepCDP (🥉3 · ⭐ 6 · 💀) - DeepCDP: Deep learning Charge Density Prediction. Unlicensed - 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 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 @@ -683,9 +683,9 @@ _Tutorials, guides, cookbooks, recipes, etc._ 🔗 Quantum Chemistry in the Age of Machine Learning - Book, 2022. -
jarvis-tools-notebooks (🥇11 · ⭐ 63) - A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/. NIST +
jarvis-tools-notebooks (🥇11 · ⭐ 64) - A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/. NIST -- [GitHub](https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks) (👨‍💻 5 · 🔀 26 · ⏱️ 14.08.2024): +- [GitHub](https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks) (👨‍💻 5 · 🔀 27 · ⏱️ 14.08.2024): ``` git clone https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks @@ -733,7 +733,7 @@ _Tutorials, guides, cookbooks, recipes, etc._
MACE-tutorials (🥉7 · ⭐ 38) - Another set of tutorials for the MACE interatomic potential by one of the authors. MIT ML-IAP rep-learn MD -- [GitHub](https://github.com/ilyes319/mace-tutorials) (👨‍💻 2 · 🔀 10 · ⏱️ 16.07.2024): +- [GitHub](https://github.com/ilyes319/mace-tutorials) (👨‍💻 2 · 🔀 11 · ⏱️ 16.07.2024): ``` git clone https://github.com/ilyes319/mace-tutorials @@ -775,7 +775,7 @@ _Projects that focus on explainability and model interpretability in atomistic M ``` git clone https://github.com/ur-whitelab/exmol ``` -- [PyPi](https://pypi.org/project/exmol) (📥 2.6K / month · 📦 1 · ⏱️ 03.06.2022): +- [PyPi](https://pypi.org/project/exmol) (📥 3K / month · 📦 1 · ⏱️ 03.06.2022): ``` pip install exmol ``` @@ -816,14 +816,14 @@ _Projects and models that focus on quantities of electronic structure methods, w _General tools for atomistic machine learning._ -
DeepChem (🥇36 · ⭐ 5.5K · 📈) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. MIT +
DeepChem (🥇35 · ⭐ 5.5K · 📉) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. MIT -- [GitHub](https://github.com/deepchem/deepchem) (👨‍💻 250 · 🔀 1.7K · 📦 440 · 📋 1.9K - 34% open · ⏱️ 18.10.2024): +- [GitHub](https://github.com/deepchem/deepchem) (👨‍💻 250 · 🔀 1.7K · 📦 440 · 📋 1.9K - 34% open · ⏱️ 24.10.2024): ``` git clone https://github.com/deepchem/deepchem ``` -- [PyPi](https://pypi.org/project/deepchem) (📥 89K / month · 📦 13 · ⏱️ 18.10.2024): +- [PyPi](https://pypi.org/project/deepchem) (📥 99K / month · 📦 13 · ⏱️ 21.10.2024): ``` pip install deepchem ``` @@ -831,19 +831,19 @@ _General tools for atomistic machine learning._ ``` conda install -c conda-forge deepchem ``` -- [Docker Hub](https://hub.docker.com/r/deepchemio/deepchem) (📥 7.8K · ⭐ 5 · ⏱️ 18.10.2024): +- [Docker Hub](https://hub.docker.com/r/deepchemio/deepchem) (📥 7.8K · ⭐ 5 · ⏱️ 21.10.2024): ``` docker pull deepchemio/deepchem ```
RDKit (🥇35 · ⭐ 2.6K) - BSD-3 C++ -- [GitHub](https://github.com/rdkit/rdkit) (👨‍💻 240 · 🔀 870 · 📥 1.1K · 📦 3 · 📋 3.4K - 28% open · ⏱️ 21.10.2024): +- [GitHub](https://github.com/rdkit/rdkit) (👨‍💻 240 · 🔀 870 · 📥 1.1K · 📦 3 · 📋 3.4K - 28% open · ⏱️ 24.10.2024): ``` git clone https://github.com/rdkit/rdkit ``` -- [PyPi](https://pypi.org/project/rdkit) (📥 2.2M / month · 📦 750 · ⏱️ 07.08.2024): +- [PyPi](https://pypi.org/project/rdkit) (📥 2.1M / month · 📦 750 · ⏱️ 07.08.2024): ``` pip install rdkit ``` @@ -859,23 +859,23 @@ _General tools for atomistic machine learning._ ``` git clone https://github.com/hackingmaterials/matminer ``` -- [PyPi](https://pypi.org/project/matminer) (📥 20K / month · 📦 60 · ⏱️ 06.10.2024): +- [PyPi](https://pypi.org/project/matminer) (📥 21K / month · 📦 60 · ⏱️ 06.10.2024): ``` pip install matminer ``` -- [Conda](https://anaconda.org/conda-forge/matminer) (📥 72K · ⏱️ 06.10.2024): +- [Conda](https://anaconda.org/conda-forge/matminer) (📥 73K · ⏱️ 06.10.2024): ``` conda install -c conda-forge matminer ```
QUIP (🥈26 · ⭐ 350) - libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io. GPL-2.0 MD ML-IAP rep-eng Fortran -- [GitHub](https://github.com/libAtoms/QUIP) (👨‍💻 85 · 🔀 120 · 📥 680 · 📦 43 · 📋 470 - 22% open · ⏱️ 27.09.2024): +- [GitHub](https://github.com/libAtoms/QUIP) (👨‍💻 85 · 🔀 120 · 📥 690 · 📦 43 · 📋 470 - 22% open · ⏱️ 27.09.2024): ``` git clone https://github.com/libAtoms/QUIP ``` -- [PyPi](https://pypi.org/project/quippy-ase) (📥 11K / month · 📦 4 · ⏱️ 15.01.2023): +- [PyPi](https://pypi.org/project/quippy-ase) (📥 12K / month · 📦 4 · ⏱️ 15.01.2023): ``` pip install quippy-ase ``` @@ -891,7 +891,7 @@ _General tools for atomistic machine learning._ ``` git clone https://github.com/materialsvirtuallab/maml ``` -- [PyPi](https://pypi.org/project/maml) (📥 1.1K / month · 📦 2 · ⏱️ 13.06.2024): +- [PyPi](https://pypi.org/project/maml) (📥 1.2K / month · 📦 2 · ⏱️ 13.06.2024): ``` pip install maml ``` @@ -903,7 +903,7 @@ _General tools for atomistic machine learning._ ``` git clone https://github.com/usnistgov/jarvis ``` -- [PyPi](https://pypi.org/project/jarvis-tools) (📥 28K / month · 📦 31 · ⏱️ 18.10.2024): +- [PyPi](https://pypi.org/project/jarvis-tools) (📥 34K / month · 📦 31 · ⏱️ 18.10.2024): ``` pip install jarvis-tools ``` @@ -927,7 +927,7 @@ _General tools for atomistic machine learning._ ``` git clone https://github.com/scikit-learn-contrib/scikit-matter ``` -- [PyPi](https://pypi.org/project/skmatter) (📥 2.8K / month · ⏱️ 24.08.2023): +- [PyPi](https://pypi.org/project/skmatter) (📥 2.7K / month · ⏱️ 24.08.2023): ``` pip install skmatter ``` @@ -938,12 +938,12 @@ _General tools for atomistic machine learning._
XenonPy (🥉16 · ⭐ 140) - XenonPy is a Python Software for Materials Informatics. BSD-3 -- [GitHub](https://github.com/yoshida-lab/XenonPy) (👨‍💻 10 · 🔀 57 · 📥 1.4K · 📋 87 - 24% open · ⏱️ 21.04.2024): +- [GitHub](https://github.com/yoshida-lab/XenonPy) (👨‍💻 10 · 🔀 58 · 📥 1.4K · 📋 87 - 24% open · ⏱️ 21.04.2024): ``` git clone https://github.com/yoshida-lab/XenonPy ``` -- [PyPi](https://pypi.org/project/xenonpy) (📥 2K / month · 📦 1 · ⏱️ 31.10.2022): +- [PyPi](https://pypi.org/project/xenonpy) (📥 2.5K / month · 📦 1 · ⏱️ 31.10.2022): ``` pip install xenonpy ``` @@ -955,14 +955,14 @@ _General tools for atomistic machine learning._ ``` git clone https://github.com/dralgroup/mlatom ``` -- [PyPi](https://pypi.org/project/mlatom) (📥 2.8K / month · ⏱️ 09.10.2024): +- [PyPi](https://pypi.org/project/mlatom) (📥 3.2K / month · ⏱️ 09.10.2024): ``` pip install mlatom ```
-
Artificial Intelligence for Science (AIRS) (🥉13 · ⭐ 500) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 license rep-learn generative ML-IAP MD ML-DFT ML-WFT biomolecules +
Artificial Intelligence for Science (AIRS) (🥉13 · ⭐ 510) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 license rep-learn generative ML-IAP MD ML-DFT ML-WFT biomolecules -- [GitHub](https://github.com/divelab/AIRS) (👨‍💻 29 · 🔀 58 · 📋 15 - 6% open · ⏱️ 03.09.2024): +- [GitHub](https://github.com/divelab/AIRS) (👨‍💻 29 · 🔀 58 · 📋 16 - 12% open · ⏱️ 03.09.2024): ``` git clone https://github.com/divelab/AIRS @@ -1004,19 +1004,19 @@ _Projects that implement generative models for atomistic ML._ ``` git clone https://github.com/GT4SD/gt4sd-core ``` -- [PyPi](https://pypi.org/project/gt4sd) (📥 5.6K / month · ⏱️ 12.09.2024): +- [PyPi](https://pypi.org/project/gt4sd) (📥 6.1K / month · ⏱️ 12.09.2024): ``` pip install gt4sd ```
PMTransformer (🥇16 · ⭐ 85) - Universal Transfer Learning in Porous Materials, including MOFs. MIT transfer-learning pretrained transformer -- [GitHub](https://github.com/hspark1212/MOFTransformer) (👨‍💻 2 · 🔀 12 · 📦 7 · ⏱️ 20.06.2024): +- [GitHub](https://github.com/hspark1212/MOFTransformer) (👨‍💻 2 · 🔀 12 · 📦 8 · ⏱️ 20.06.2024): ``` git clone https://github.com/hspark1212/MOFTransformer ``` -- [PyPi](https://pypi.org/project/moftransformer) (📥 1.1K / month · 📦 1 · ⏱️ 20.06.2024): +- [PyPi](https://pypi.org/project/moftransformer) (📥 1.3K / month · 📦 1 · ⏱️ 20.06.2024): ``` pip install moftransformer ``` @@ -1028,14 +1028,14 @@ _Projects that implement generative models for atomistic ML._ ``` git clone https://github.com/microsoft/molecule-generation ``` -- [PyPi](https://pypi.org/project/molecule-generation) (📥 490 / month · 📦 1 · ⏱️ 05.01.2024): +- [PyPi](https://pypi.org/project/molecule-generation) (📥 520 / month · 📦 1 · ⏱️ 05.01.2024): ``` pip install molecule-generation ```
SchNetPack G-SchNet (🥈13 · ⭐ 48) - G-SchNet extension for SchNetPack. MIT -- [GitHub](https://github.com/atomistic-machine-learning/schnetpack-gschnet) (👨‍💻 3 · 🔀 8 · ⏱️ 19.10.2024): +- [GitHub](https://github.com/atomistic-machine-learning/schnetpack-gschnet) (👨‍💻 3 · 🔀 8 · ⏱️ 22.10.2024): ``` git clone https://github.com/atomistic-machine-learning/schnetpack-gschnet @@ -1048,7 +1048,7 @@ _Projects that implement generative models for atomistic ML._ ``` git clone https://github.com/RokasEl/simgen ``` -- [PyPi](https://pypi.org/project/simgen) (📥 82 / month · ⏱️ 14.02.2024): +- [PyPi](https://pypi.org/project/simgen) (📥 89 / month · ⏱️ 14.02.2024): ``` pip install simgen ``` @@ -1068,7 +1068,7 @@ _Projects that implement generative models for atomistic ML._ - G-SchNet (🥉8 · ⭐ 130 · 💀) - G-SchNet - a generative model for 3d molecular structures. MIT - bVAE-IM (🥉8 · ⭐ 11 · 💀) - Implementation of Chemical Design with GPU-based Ising Machine. MIT QML single-paper - cG-SchNet (🥉7 · ⭐ 52 · 💀) - cG-SchNet - a conditional generative neural network for 3d molecular structures. MIT -- rxngenerator (🥉6 · ⭐ 11 · 💀) - A generative model for molecular generation via multi-step chemical reactions. MIT +- rxngenerator (🥉6 · ⭐ 12 · 💀) - 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
@@ -1082,12 +1082,12 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
DeePMD-kit (🥇26 · ⭐ 1.5K) - A deep learning package for many-body potential energy representation and molecular dynamics. LGPL-3.0 C++ -- [GitHub](https://github.com/deepmodeling/deepmd-kit) (👨‍💻 69 · 🔀 510 · 📥 41K · 📦 19 · 📋 820 - 12% open · ⏱️ 17.09.2024): +- [GitHub](https://github.com/deepmodeling/deepmd-kit) (👨‍💻 69 · 🔀 510 · 📥 42K · 📦 19 · 📋 820 - 12% open · ⏱️ 17.09.2024): ``` git clone https://github.com/deepmodeling/deepmd-kit ``` -- [PyPi](https://pypi.org/project/deepmd-kit) (📥 10K / month · 📦 4 · ⏱️ 25.09.2024): +- [PyPi](https://pypi.org/project/deepmd-kit) (📥 11K / month · 📦 4 · ⏱️ 25.09.2024): ``` pip install deepmd-kit ``` @@ -1100,26 +1100,26 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc docker pull deepmodeling/deepmd-kit ```
-
fairchem (🥇25 · ⭐ 810 · 📈) - FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project. MIT pretrained UIP rep-learn catalysis +
fairchem (🥇24 · ⭐ 830 · 📉) - FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project. MIT pretrained UIP rep-learn catalysis -- [GitHub](https://github.com/FAIR-Chem/fairchem) (👨‍💻 42 · 🔀 240 · 📋 230 - 13% open · ⏱️ 20.10.2024): +- [GitHub](https://github.com/FAIR-Chem/fairchem) (👨‍💻 42 · 🔀 240 · 📋 230 - 13% open · ⏱️ 23.10.2024): ``` git clone https://github.com/FAIR-Chem/fairchem ``` -- [PyPi](https://pypi.org/project/fairchem-core) (📥 1.9K / month · 📦 1 · ⏱️ 14.09.2024): +- [PyPi](https://pypi.org/project/fairchem-core) (📥 2.2K / month · 📦 1 · ⏱️ 14.09.2024): ``` pip install fairchem-core ```
TorchANI (🥇24 · ⭐ 460 · 💤) - Accurate Neural Network Potential on PyTorch. MIT -- [GitHub](https://github.com/aiqm/torchani) (👨‍💻 19 · 🔀 120 · 📦 44 · 📋 170 - 13% open · ⏱️ 14.11.2023): +- [GitHub](https://github.com/aiqm/torchani) (👨‍💻 19 · 🔀 130 · 📦 44 · 📋 170 - 13% open · ⏱️ 14.11.2023): ``` git clone https://github.com/aiqm/torchani ``` -- [PyPi](https://pypi.org/project/torchani) (📥 5K / month · 📦 4 · ⏱️ 14.11.2023): +- [PyPi](https://pypi.org/project/torchani) (📥 5.5K / month · 📦 4 · ⏱️ 14.11.2023): ``` pip install torchani ``` @@ -1128,30 +1128,38 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc conda install -c conda-forge torchani ```
-
NequIP (🥇23 · ⭐ 620) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT +
NequIP (🥇22 · ⭐ 620 · 📉) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT -- [GitHub](https://github.com/mir-group/nequip) (👨‍💻 11 · 🔀 140 · 📦 27 · 📋 95 - 26% open · ⏱️ 09.07.2024): +- [GitHub](https://github.com/mir-group/nequip) (👨‍💻 12 · 🔀 140 · 📦 28 · 📋 96 - 26% open · ⏱️ 21.10.2024): ``` git clone https://github.com/mir-group/nequip ``` -- [PyPi](https://pypi.org/project/nequip) (📥 4.1K / month · 📦 1 · ⏱️ 09.07.2024): +- [PyPi](https://pypi.org/project/nequip) (📥 4.8K / month · 📦 1 · ⏱️ 09.07.2024): ``` pip install nequip ``` -- [Conda](https://anaconda.org/conda-forge/nequip) (📥 6.2K · ⏱️ 10.07.2024): +- [Conda](https://anaconda.org/conda-forge/nequip) (📥 6.3K · ⏱️ 10.07.2024): ``` conda install -c conda-forge nequip ```
-
MACE (🥇22 · ⭐ 500) - MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing. MIT +
MACE (🥇22 · ⭐ 510) - MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing. MIT -- [GitHub](https://github.com/ACEsuit/mace) (👨‍💻 42 · 🔀 190 · 📋 270 - 22% open · ⏱️ 10.10.2024): +- [GitHub](https://github.com/ACEsuit/mace) (👨‍💻 42 · 🔀 190 · 📋 270 - 21% open · ⏱️ 10.10.2024): ``` git clone https://github.com/ACEsuit/mace ```
+
GPUMD (🥇22 · ⭐ 460 · 📈) - GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials.. GPL-3.0 MD C++ electrostatics + +- [GitHub](https://github.com/brucefan1983/GPUMD) (👨‍💻 35 · 🔀 120 · 📋 180 - 9% open · ⏱️ 24.10.2024): + + ``` + git clone https://github.com/brucefan1983/GPUMD + ``` +
TorchMD-NET (🥇22 · ⭐ 330) - Training neural network potentials. MIT MD rep-learn transformer pretrained - [GitHub](https://github.com/torchmd/torchmd-net) (👨‍💻 16 · 🔀 72 · 📋 120 - 28% open · ⏱️ 28.08.2024): @@ -1159,7 +1167,7 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc ``` git clone https://github.com/torchmd/torchmd-net ``` -- [Conda](https://anaconda.org/conda-forge/torchmd-net) (📥 190K · ⏱️ 12.09.2024): +- [Conda](https://anaconda.org/conda-forge/torchmd-net) (📥 200K · ⏱️ 12.09.2024): ``` conda install -c conda-forge torchmd-net ``` @@ -1171,7 +1179,7 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc ``` git clone https://github.com/deepmodeling/dpgen ``` -- [PyPi](https://pypi.org/project/dpgen) (📥 1.5K / month · 📦 1 · ⏱️ 10.04.2024): +- [PyPi](https://pypi.org/project/dpgen) (📥 1.7K / month · 📦 1 · ⏱️ 10.04.2024): ``` pip install dpgen ``` @@ -1180,22 +1188,14 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc conda install -c deepmodeling dpgen ```
-
GPUMD (🥈21 · ⭐ 460) - GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials.. GPL-3.0 MD C++ electrostatics - -- [GitHub](https://github.com/brucefan1983/GPUMD) (👨‍💻 34 · 🔀 120 · 📋 180 - 9% open · ⏱️ 19.10.2024): - - ``` - git clone https://github.com/brucefan1983/GPUMD - ``` -
-
apax (🥈20 · ⭐ 15) - A flexible and performant framework for training machine learning potentials. MIT +
apax (🥈21 · ⭐ 15) - A flexible and performant framework for training machine learning potentials. MIT -- [GitHub](https://github.com/apax-hub/apax) (👨‍💻 7 · 🔀 2 · 📦 3 · 📋 120 - 10% open · ⏱️ 17.10.2024): +- [GitHub](https://github.com/apax-hub/apax) (👨‍💻 7 · 🔀 3 · 📦 3 · 📋 120 - 8% open · ⏱️ 22.10.2024): ``` git clone https://github.com/apax-hub/apax ``` -- [PyPi](https://pypi.org/project/apax) (📥 1.3K / month · ⏱️ 17.09.2024): +- [PyPi](https://pypi.org/project/apax) (📥 1.5K / month · ⏱️ 17.09.2024): ``` pip install apax ``` @@ -1215,7 +1215,7 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc ``` git clone https://github.com/openkim/kliff ``` -- [PyPi](https://pypi.org/project/kliff) (📥 840 / month · ⏱️ 17.12.2023): +- [PyPi](https://pypi.org/project/kliff) (📥 980 / month · ⏱️ 17.12.2023): ``` pip install kliff ``` @@ -1231,37 +1231,37 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc ``` git clone https://github.com/MaterSim/PyXtal_FF ``` -- [PyPi](https://pypi.org/project/pyxtal_ff) (📥 630 / month · ⏱️ 21.12.2022): +- [PyPi](https://pypi.org/project/pyxtal_ff) (📥 700 / month · ⏱️ 21.12.2022): ``` pip install pyxtal_ff ```
-
wfl (🥈15 · ⭐ 32) - Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows. GPL-2.0 workflows HTC +
NNPOps (🥈15 · ⭐ 83) - High-performance operations for neural network potentials. MIT MD C++ -- [GitHub](https://github.com/libAtoms/workflow) (👨‍💻 19 · 🔀 18 · 📦 2 · 📋 160 - 42% open · ⏱️ 11.10.2024): +- [GitHub](https://github.com/openmm/NNPOps) (👨‍💻 9 · 🔀 18 · 📋 57 - 38% open · ⏱️ 10.07.2024): ``` - git clone https://github.com/libAtoms/workflow + git clone https://github.com/openmm/NNPOps + ``` +- [Conda](https://anaconda.org/conda-forge/nnpops) (📥 250K · ⏱️ 11.09.2024): + ``` + conda install -c conda-forge nnpops ```
-
So3krates (MLFF) (🥈14 · ⭐ 85) - Build neural networks for machine learning force fields with JAX. MIT +
wfl (🥈15 · ⭐ 32) - Workflow is a Python toolkit for building interatomic potential creation and atomistic simulation workflows. GPL-2.0 workflows HTC -- [GitHub](https://github.com/thorben-frank/mlff) (👨‍💻 4 · 🔀 16 · 📋 10 - 40% open · ⏱️ 23.08.2024): +- [GitHub](https://github.com/libAtoms/workflow) (👨‍💻 19 · 🔀 18 · 📦 2 · 📋 160 - 41% open · ⏱️ 23.10.2024): ``` - git clone https://github.com/thorben-frank/mlff + git clone https://github.com/libAtoms/workflow ```
-
NNPOps (🥈14 · ⭐ 82) - High-performance operations for neural network potentials. MIT MD C++ +
So3krates (MLFF) (🥈14 · ⭐ 87) - Build neural networks for machine learning force fields with JAX. MIT -- [GitHub](https://github.com/openmm/NNPOps) (👨‍💻 9 · 🔀 18 · 📋 56 - 39% open · ⏱️ 10.07.2024): +- [GitHub](https://github.com/thorben-frank/mlff) (👨‍💻 4 · 🔀 16 · 📋 10 - 40% open · ⏱️ 23.08.2024): ``` - git clone https://github.com/openmm/NNPOps - ``` -- [Conda](https://anaconda.org/conda-forge/nnpops) (📥 250K · ⏱️ 11.09.2024): - ``` - conda install -c conda-forge nnpops + git clone https://github.com/thorben-frank/mlff ```
Ultra-Fast Force Fields (UF3) (🥈14 · ⭐ 60) - UF3: a python library for generating ultra-fast interatomic potentials. Apache-2 @@ -1271,7 +1271,7 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc ``` git clone https://github.com/uf3/uf3 ``` -- [PyPi](https://pypi.org/project/uf3) (📥 120 / month · ⏱️ 27.10.2023): +- [PyPi](https://pypi.org/project/uf3) (📥 140 / month · ⏱️ 27.10.2023): ``` pip install uf3 ``` @@ -1291,11 +1291,23 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc ``` git clone https://github.com/ICAMS/python-ace ``` -- [PyPi](https://pypi.org/project/python-ace) (📥 35 / month · ⏱️ 24.10.2022): +- [PyPi](https://pypi.org/project/python-ace) (📥 41 / month · ⏱️ 24.10.2022): ``` pip install python-ace ```
+
CCS_fit (🥈13 · ⭐ 8 · 💤) - Curvature Constrained Splines. GPL-3.0 + +- [GitHub](https://github.com/Teoroo-CMC/CCS) (👨‍💻 8 · 🔀 11 · 📥 650 · 📋 14 - 57% open · ⏱️ 16.02.2024): + + ``` + git clone https://github.com/Teoroo-CMC/CCS + ``` +- [PyPi](https://pypi.org/project/ccs_fit) (📥 9K / month · ⏱️ 16.02.2024): + ``` + pip install ccs_fit + ``` +
ANI-1 (🥈12 · ⭐ 220 · 💤) - ANI-1 neural net potential with python interface (ASE). MIT - [GitHub](https://github.com/isayev/ASE_ANI) (👨‍💻 6 · 🔀 55 · 📋 37 - 43% open · ⏱️ 11.03.2024): @@ -1316,18 +1328,6 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc docker pull teoroo/pinn ```
-
CCS_fit (🥈12 · ⭐ 8 · 💤) - Curvature Constrained Splines. GPL-3.0 - -- [GitHub](https://github.com/Teoroo-CMC/CCS) (👨‍💻 8 · 🔀 11 · 📥 650 · 📋 14 - 57% open · ⏱️ 16.02.2024): - - ``` - git clone https://github.com/Teoroo-CMC/CCS - ``` -- [PyPi](https://pypi.org/project/ccs_fit) (📥 7.3K / month · ⏱️ 16.02.2024): - ``` - pip install ccs_fit - ``` -
ACEfit (🥈12 · ⭐ 7) - MIT Julia - [GitHub](https://github.com/ACEsuit/ACEfit.jl) (👨‍💻 8 · 🔀 7 · 📋 57 - 38% open · ⏱️ 14.09.2024): @@ -1344,7 +1344,7 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://github.com/TinkerTools/tinker-hp ```
-
PyNEP (🥉10 · ⭐ 48 · 📈) - A python interface of the machine learning potential NEP used in GPUMD. MIT +
PyNEP (🥉10 · ⭐ 48) - A python interface of the machine learning potential NEP used in GPUMD. MIT - [GitHub](https://github.com/bigd4/PyNEP) (👨‍💻 7 · 🔀 16 · 📋 11 - 36% open · ⏱️ 01.06.2024): @@ -1354,11 +1354,11 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc
calorine (🥉10 · ⭐ 12) - A Python package for constructing and sampling neuroevolution potential models. https://doi.org/10.21105/joss.06264. Custom -- [PyPi](https://pypi.org/project/calorine) (📥 3K / month · 📦 2 · ⏱️ 26.07.2024): +- [PyPi](https://pypi.org/project/calorine) (📥 3.2K / month · 📦 2 · ⏱️ 26.07.2024): ``` pip install calorine ``` -- [GitLab](https://gitlab.com/materials-modeling/calorine) (🔀 4 · 📋 86 - 10% open · ⏱️ 26.07.2024): +- [GitLab](https://gitlab.com/materials-modeling/calorine) (🔀 4 · 📋 86 - 9% open · ⏱️ 26.07.2024): ``` git clone https://gitlab.com/materials-modeling/calorine @@ -1452,6 +1452,14 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://github.com/ICAMS/TensorPotential ```
+
NequIP-JAX (🥉5 · ⭐ 18 · 💤) - JAX implementation of the NequIP interatomic potential. Unlicensed + +- [GitHub](https://github.com/mariogeiger/nequip-jax) (👨‍💻 2 · 🔀 3 · ⏱️ 01.11.2023): + + ``` + git clone https://github.com/mariogeiger/nequip-jax + ``` +
Allegro-JAX ( ⭐ 18) - JAX implementation of the Allegro interatomic potential. Unlicensed - [GitHub](https://github.com/mariogeiger/allegro-jax) (👨‍💻 2 · 🔀 2 · 📋 2 - 50% open · ⏱️ 09.04.2024): @@ -1460,7 +1468,7 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc git clone https://github.com/mariogeiger/allegro-jax ```
-
Show 33 hidden projects... +
Show 32 hidden projects... - MEGNet (🥇23 · ⭐ 500 · 💀) - Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals. BSD-3 multifidelity - sGDML (🥈17 · ⭐ 140 · 💀) - sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model. MIT @@ -1484,7 +1492,6 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc - Alchemical learning (🥉5 · ⭐ 2 · 💀) - Code for the Modeling high-entropy transition metal alloys with alchemical compression article. BSD-3 - ACE1Pack.jl (🥉5 · ⭐ 1 · 💀) - Provides convenience functionality for the usage of ACE1.jl, ACEfit.jl, JuLIP.jl for fitting interatomic potentials.. MIT Julia - Allegro-Legato (🥉4 · ⭐ 19 · 💀) - An extension of Allegro with enhanced robustness and time-to-failure. MIT MD -- NequIP-JAX (🥉4 · ⭐ 18 · 💤) - JAX implementation of the NequIP interatomic potential. Unlicensed - glp (🥉4 · ⭐ 17 · 💤) - tools for graph-based machine-learning potentials in jax. MIT - 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 @@ -1504,14 +1511,14 @@ _Machine learning interatomic potentials (aka ML-IAP, MLIAP, MLIP, MLP) and forc _Projects that use (large) language models (LMs, LLMs) or natural language procesing (NLP) techniques for atomistic ML._ -
paper-qa (🥇31 · ⭐ 6.2K · 📈) - High accuracy RAG for answering questions from scientific documents with citations. Apache-2 ai-agent +
paper-qa (🥇31 · ⭐ 6.3K) - High accuracy RAG for answering questions from scientific documents with citations. Apache-2 ai-agent -- [GitHub](https://github.com/Future-House/paper-qa) (👨‍💻 26 · 🔀 580 · 📦 75 · 📋 250 - 38% open · ⏱️ 21.10.2024): +- [GitHub](https://github.com/Future-House/paper-qa) (👨‍💻 26 · 🔀 590 · 📦 76 · 📋 250 - 39% open · ⏱️ 23.10.2024): ``` git clone https://github.com/whitead/paper-qa ``` -- [PyPi](https://pypi.org/project/paper-qa) (📥 16K / month · 📦 8 · ⏱️ 18.10.2024): +- [PyPi](https://pypi.org/project/paper-qa) (📥 18K / month · 📦 8 · ⏱️ 23.10.2024): ``` pip install paper-qa ``` @@ -1523,7 +1530,7 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce ``` git clone https://github.com/OpenBioML/chemnlp ``` -- [PyPi](https://pypi.org/project/chemnlp) (📥 510 / month · 📦 1 · ⏱️ 07.08.2023): +- [PyPi](https://pypi.org/project/chemnlp) (📥 600 / month · 📦 1 · ⏱️ 07.08.2023): ``` pip install chemnlp ``` @@ -1535,7 +1542,7 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce ``` git clone https://github.com/ur-whitelab/chemcrow-public ``` -- [PyPi](https://pypi.org/project/chemcrow) (📥 2.1K / month · ⏱️ 27.03.2024): +- [PyPi](https://pypi.org/project/chemcrow) (📥 2.3K / month · ⏱️ 27.03.2024): ``` pip install chemcrow ``` @@ -1547,7 +1554,7 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce ``` git clone https://github.com/kjappelbaum/gptchem ``` -- [PyPi](https://pypi.org/project/gptchem) (📥 300 / month · ⏱️ 04.10.2023): +- [PyPi](https://pypi.org/project/gptchem) (📥 350 / month · ⏱️ 04.10.2023): ``` pip install gptchem ``` @@ -1559,14 +1566,14 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce ``` git clone https://github.com/Yeonghun1675/ChatMOF ``` -- [PyPi](https://pypi.org/project/chatmof) (📥 1.4K / month · ⏱️ 01.07.2024): +- [PyPi](https://pypi.org/project/chatmof) (📥 1.6K / month · ⏱️ 01.07.2024): ``` pip install chatmof ```
AtomGPT (🥈13 · ⭐ 27) - AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design. Custom generative pretrained transformer -- [GitHub](https://github.com/usnistgov/atomgpt) (👨‍💻 2 · 🔀 3 · 📦 2 · ⏱️ 04.10.2024): +- [GitHub](https://github.com/usnistgov/atomgpt) (👨‍💻 2 · 🔀 4 · 📦 2 · ⏱️ 04.10.2024): ``` git clone https://github.com/usnistgov/atomgpt @@ -1576,14 +1583,14 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce pip install atomgpt ```
-
NIST ChemNLP (🥈12 · ⭐ 72) - ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data. MIT literature-data +
NIST ChemNLP (🥈12 · ⭐ 73) - ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data. MIT literature-data - [GitHub](https://github.com/usnistgov/chemnlp) (👨‍💻 2 · 🔀 16 · 📦 4 · ⏱️ 19.08.2024): ``` git clone https://github.com/usnistgov/chemnlp ``` -- [PyPi](https://pypi.org/project/chemnlp) (📥 510 / month · 📦 1 · ⏱️ 07.08.2023): +- [PyPi](https://pypi.org/project/chemnlp) (📥 600 / month · 📦 1 · ⏱️ 07.08.2023): ``` pip install chemnlp ``` @@ -1608,9 +1615,9 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce conda install -c msr-ai4science molskill ```
-
LLaMP (🥉9 · ⭐ 61) - A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An.. BSD-3 materials-discovery cheminformatics generative MD multimodal language-models Python general-tool +
LLaMP (🥉9 · ⭐ 63) - A web app and Python API for multi-modal RAG framework to ground LLMs on high-fidelity materials informatics. An.. BSD-3 materials-discovery cheminformatics generative MD multimodal language-models Python general-tool -- [GitHub](https://github.com/chiang-yuan/llamp) (👨‍💻 6 · 🔀 7 · 📋 25 - 32% open · ⏱️ 14.10.2024): +- [GitHub](https://github.com/chiang-yuan/llamp) (👨‍💻 6 · 🔀 8 · 📋 25 - 32% open · ⏱️ 14.10.2024): ``` git clone https://github.com/chiang-yuan/llamp @@ -1632,7 +1639,7 @@ _Projects that use (large) language models (LMs, LLMs) or natural language proce git clone https://github.com/vertaix/LLM-Prop ```
-
crystal-text-llm (🥉5 · ⭐ 75) - Large language models to generate stable crystals. CC-BY-NC-4.0 materials-discovery +
crystal-text-llm (🥉5 · ⭐ 76) - Large language models to generate stable crystals. CC-BY-NC-4.0 materials-discovery - [GitHub](https://github.com/facebookresearch/crystal-text-llm) (👨‍💻 3 · 🔀 13 · 📋 11 - 81% open · ⏱️ 18.06.2024): @@ -1676,7 +1683,7 @@ _Projects that implement materials discovery methods using atomistic ML._ 🔗 MatterGen - A generative model for inorganic materials design https://doi.org/10.48550/arXiv.2312.03687. generative proprietary -
aviary (🥇15 · ⭐ 48) - The Wren sits on its Roost in the Aviary. MIT +
aviary (🥇14 · ⭐ 48) - The Wren sits on its Roost in the Aviary. MIT - [GitHub](https://github.com/CompRhys/aviary) (👨‍💻 4 · 🔀 11 · 📋 29 - 13% open · ⏱️ 16.10.2024): @@ -1686,7 +1693,7 @@ _Projects that implement materials discovery methods using atomistic ML._
BOSS (🥇14 · ⭐ 20) - Bayesian Optimization Structure Search (BOSS). Apache-2 probabilistic -- [PyPi](https://pypi.org/project/aalto-boss) (📥 19K / month · ⏱️ 09.10.2024): +- [PyPi](https://pypi.org/project/aalto-boss) (📥 21K / month · ⏱️ 09.10.2024): ``` pip install aalto-boss ``` @@ -1698,11 +1705,11 @@ _Projects that implement materials discovery methods using atomistic ML._
AGOX (🥈11 · ⭐ 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 -- [PyPi](https://pypi.org/project/agox) (📥 1.4K / month · ⏱️ 26.08.2024): +- [PyPi](https://pypi.org/project/agox) (📥 1.6K / month · ⏱️ 23.10.2024): ``` pip install agox ``` -- [GitLab](https://gitlab.com/agox/agox) (🔀 5 · 📋 24 - 54% open · ⏱️ 26.08.2024): +- [GitLab](https://gitlab.com/agox/agox) (🔀 5 · 📋 24 - 37% open · ⏱️ 23.10.2024): ``` git clone https://gitlab.com/agox/agox @@ -1743,36 +1750,36 @@ _Projects that implement mathematical objects used in atomistic machine learning
KFAC-JAX (🥇19 · ⭐ 240) - Second Order Optimization and Curvature Estimation with K-FAC in JAX. Apache-2 -- [GitHub](https://github.com/google-deepmind/kfac-jax) (👨‍💻 15 · 🔀 20 · 📦 11 · 📋 19 - 47% open · ⏱️ 18.10.2024): +- [GitHub](https://github.com/google-deepmind/kfac-jax) (👨‍💻 16 · 🔀 20 · 📦 11 · 📋 19 - 47% open · ⏱️ 24.10.2024): ``` git clone https://github.com/google-deepmind/kfac-jax ``` -- [PyPi](https://pypi.org/project/kfac-jax) (📥 1.1K / month · 📦 1 · ⏱️ 04.04.2024): +- [PyPi](https://pypi.org/project/kfac-jax) (📥 1.3K / month · 📦 1 · ⏱️ 04.04.2024): ``` pip install kfac-jax ```
-
gpax (🥇18 · ⭐ 200) - Gaussian Processes for Experimental Sciences. MIT probabilistic active-learning +
gpax (🥇19 · ⭐ 200) - Gaussian Processes for Experimental Sciences. MIT probabilistic active-learning - [GitHub](https://github.com/ziatdinovmax/gpax) (👨‍💻 6 · 🔀 25 · 📦 3 · 📋 40 - 20% open · ⏱️ 21.05.2024): ``` git clone https://github.com/ziatdinovmax/gpax ``` -- [PyPi](https://pypi.org/project/gpax) (📥 990 / month · ⏱️ 20.03.2024): +- [PyPi](https://pypi.org/project/gpax) (📥 1.1K / month · ⏱️ 20.03.2024): ``` pip install gpax ```
-
SpheriCart (🥇18 · ⭐ 71) - Multi-language library for the calculation of spherical harmonics in Cartesian coordinates. MIT +
SpheriCart (🥈18 · ⭐ 72) - Multi-language library for the calculation of spherical harmonics in Cartesian coordinates. MIT -- [GitHub](https://github.com/lab-cosmo/sphericart) (👨‍💻 10 · 🔀 11 · 📥 87 · 📦 5 · 📋 41 - 56% open · ⏱️ 17.10.2024): +- [GitHub](https://github.com/lab-cosmo/sphericart) (👨‍💻 10 · 🔀 11 · 📥 89 · 📦 5 · 📋 41 - 56% open · ⏱️ 17.10.2024): ``` git clone https://github.com/lab-cosmo/sphericart ``` -- [PyPi](https://pypi.org/project/sphericart) (📥 1.4K / month · ⏱️ 04.09.2024): +- [PyPi](https://pypi.org/project/sphericart) (📥 1.7K / month · ⏱️ 04.09.2024): ``` pip install sphericart ``` @@ -1805,7 +1812,7 @@ _Projects that implement mathematical objects used in atomistic machine learning - lie-nn (🥈9 · ⭐ 26 · 💀) - Tools for building equivariant polynomials on reductive Lie groups. MIT rep-learn - EquivariantOperators.jl (🥉6 · ⭐ 19 · 💀) - This package is deprecated. Functionalities are migrating to Porcupine.jl. MIT Julia -- cnine (🥉6 · ⭐ 4) - Cnine tensor library. Unlicensed C++ +- cnine (🥉5 · ⭐ 4) - Cnine tensor library. Unlicensed C++ - torch_spex (🥉3 · ⭐ 3 · 💤) - Spherical expansions in PyTorch. Unlicensed - Wigner Kernels (🥉1 · ⭐ 2 · 💀) - Collection of programs to benchmark Wigner kernels. Unlicensed benchmarking
@@ -1819,12 +1826,12 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach
JAX-MD (🥇26 · ⭐ 1.2K) - Differentiable, Hardware Accelerated, Molecular Dynamics. Apache-2 -- [GitHub](https://github.com/jax-md/jax-md) (👨‍💻 34 · 🔀 190 · 📦 59 · 📋 150 - 47% open · ⏱️ 05.09.2024): +- [GitHub](https://github.com/jax-md/jax-md) (👨‍💻 34 · 🔀 190 · 📦 60 · 📋 150 - 47% open · ⏱️ 05.09.2024): ``` git clone https://github.com/jax-md/jax-md ``` -- [PyPi](https://pypi.org/project/jax-md) (📥 4.7K / month · 📦 3 · ⏱️ 09.08.2023): +- [PyPi](https://pypi.org/project/jax-md) (📥 5.4K / month · 📦 3 · ⏱️ 09.08.2023): ``` pip install jax-md ``` @@ -1836,19 +1843,19 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach ``` git clone https://github.com/luigibonati/mlcolvar ``` -- [PyPi](https://pypi.org/project/mlcolvar) (📥 410 / month · ⏱️ 12.06.2024): +- [PyPi](https://pypi.org/project/mlcolvar) (📥 470 / month · ⏱️ 12.06.2024): ``` pip install mlcolvar ```
FitSNAP (🥈20 · ⭐ 150) - Software for generating machine-learning interatomic potentials for LAMMPS. GPL-2.0 -- [GitHub](https://github.com/FitSNAP/FitSNAP) (👨‍💻 24 · 🔀 50 · 📥 12 · 📦 3 · 📋 73 - 21% open · ⏱️ 19.09.2024): +- [GitHub](https://github.com/FitSNAP/FitSNAP) (👨‍💻 24 · 🔀 51 · 📥 13 · 📦 3 · 📋 73 - 21% open · ⏱️ 19.09.2024): ``` git clone https://github.com/FitSNAP/FitSNAP ``` -- [Conda](https://anaconda.org/conda-forge/fitsnap3) (📥 8.8K · ⏱️ 16.06.2023): +- [Conda](https://anaconda.org/conda-forge/fitsnap3) (📥 8.9K · ⏱️ 16.06.2023): ``` conda install -c conda-forge fitsnap3 ``` @@ -1860,7 +1867,7 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach ``` git clone https://github.com/openmm/openmm-torch ``` -- [Conda](https://anaconda.org/conda-forge/openmm-torch) (📥 490K · ⏱️ 30.09.2024): +- [Conda](https://anaconda.org/conda-forge/openmm-torch) (📥 500K · ⏱️ 30.09.2024): ``` conda install -c conda-forge openmm-torch ``` @@ -1903,7 +1910,7 @@ _Projects that simplify the integration of molecular dynamics and atomistic mach
SOMD (🥉5 · ⭐ 12) - Molecular dynamics package designed for the SIESTA DFT code. AGPL-3.0 ML-IAP active-learning -- [GitHub](https://github.com/initqp/somd) (🔀 2 · ⏱️ 19.10.2024): +- [GitHub](https://github.com/initqp/somd) (🔀 2 · ⏱️ 22.10.2024): ``` git clone https://github.com/initqp/somd @@ -1966,9 +1973,9 @@ _Projects that offer implementations of representations aka descriptors, fingerp conda install -c conda-forge dscribe ```
-
MODNet (🥇17 · ⭐ 78) - MODNet: a framework for machine learning materials properties. MIT pretrained small-data transfer-learning +
MODNet (🥇17 · ⭐ 79) - MODNet: a framework for machine learning materials properties. MIT pretrained small-data transfer-learning -- [GitHub](https://github.com/ppdebreuck/modnet) (👨‍💻 10 · 🔀 32 · 📦 10 · 📋 53 - 49% open · ⏱️ 24.09.2024): +- [GitHub](https://github.com/ppdebreuck/modnet) (👨‍💻 10 · 🔀 33 · 📦 10 · 📋 53 - 49% open · ⏱️ 24.10.2024): ``` git clone https://github.com/ppdebreuck/modnet @@ -1981,7 +1988,7 @@ _Projects that offer implementations of representations aka descriptors, fingerp ``` git clone https://github.com/drcassar/glasspy ``` -- [PyPi](https://pypi.org/project/glasspy) (📥 1.1K / month · ⏱️ 05.09.2024): +- [PyPi](https://pypi.org/project/glasspy) (📥 1.2K / month · ⏱️ 05.09.2024): ``` pip install glasspy ``` @@ -2067,14 +2074,14 @@ _Projects that offer implementations of representations aka descriptors, fingerp _General models that learn a representations aka embeddings of atomistic systems, such as message-passing neural networks (MPNN)._ -
Deep Graph Library (DGL) (🥇38 · ⭐ 13K · 📉) - Python package built to ease deep learning on graph, on top of existing DL frameworks. Apache-2 +
Deep Graph Library (DGL) (🥇38 · ⭐ 13K) - Python package built to ease deep learning on graph, on top of existing DL frameworks. Apache-2 - [GitHub](https://github.com/dmlc/dgl) (👨‍💻 300 · 🔀 3K · 📦 310 · 📋 2.9K - 18% open · ⏱️ 18.10.2024): ``` git clone https://github.com/dmlc/dgl ``` -- [PyPi](https://pypi.org/project/dgl) (📥 160K / month · 📦 150 · ⏱️ 13.05.2024): +- [PyPi](https://pypi.org/project/dgl) (📥 170K / month · 📦 150 · ⏱️ 13.05.2024): ``` pip install dgl ``` @@ -2105,44 +2112,32 @@ _General models that learn a representations aka embeddings of atomistic systems
e3nn (🥇27 · ⭐ 960) - A modular framework for neural networks with Euclidean symmetry. MIT -- [GitHub](https://github.com/e3nn/e3nn) (👨‍💻 31 · 🔀 140 · 📦 320 · 📋 160 - 14% open · ⏱️ 25.08.2024): +- [GitHub](https://github.com/e3nn/e3nn) (👨‍💻 31 · 🔀 140 · 📦 330 · 📋 160 - 14% open · ⏱️ 21.10.2024): ``` git clone https://github.com/e3nn/e3nn ``` -- [PyPi](https://pypi.org/project/e3nn) (📥 87K / month · 📦 4 · ⏱️ 13.04.2022): +- [PyPi](https://pypi.org/project/e3nn) (📥 89K / month · 📦 4 · ⏱️ 13.04.2022): ``` pip install e3nn ``` -- [Conda](https://anaconda.org/conda-forge/e3nn) (📥 23K · ⏱️ 18.06.2023): +- [Conda](https://anaconda.org/conda-forge/e3nn) (📥 24K · ⏱️ 18.06.2023): ``` conda install -c conda-forge e3nn ```
-
MatGL (Materials Graph Library) (🥇24 · ⭐ 260) - Graph deep learning library for materials. BSD-3 multifidelity +
MatGL (Materials Graph Library) (🥇24 · ⭐ 270) - Graph deep learning library for materials. BSD-3 multifidelity -- [GitHub](https://github.com/materialsvirtuallab/matgl) (👨‍💻 17 · 🔀 60 · 📦 50 · 📋 98 - 7% open · ⏱️ 19.10.2024): +- [GitHub](https://github.com/materialsvirtuallab/matgl) (👨‍💻 17 · 🔀 60 · 📦 51 · 📋 98 - 7% open · ⏱️ 24.10.2024): ``` git clone https://github.com/materialsvirtuallab/matgl ``` -- [PyPi](https://pypi.org/project/m3gnet) (📥 2.1K / month · 📦 5 · ⏱️ 17.11.2022): +- [PyPi](https://pypi.org/project/m3gnet) (📥 1.9K / month · 📦 5 · ⏱️ 17.11.2022): ``` pip install m3gnet ```
-
ALIGNN (🥈22 · ⭐ 220) - Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en. Custom - -- [GitHub](https://github.com/usnistgov/alignn) (👨‍💻 7 · 🔀 80 · 📦 15 · 📋 65 - 61% open · ⏱️ 09.09.2024): - - ``` - git clone https://github.com/usnistgov/alignn - ``` -- [PyPi](https://pypi.org/project/alignn) (📥 16K / month · 📦 6 · ⏱️ 09.09.2024): - ``` - pip install alignn - ``` -
e3nn-jax (🥈22 · ⭐ 180) - jax library for E3 Equivariant Neural Networks. Apache-2 - [GitHub](https://github.com/e3nn/e3nn-jax) (👨‍💻 7 · 🔀 18 · 📦 41 · 📋 23 - 8% open · ⏱️ 28.09.2024): @@ -2150,7 +2145,7 @@ _General models that learn a representations aka embeddings of atomistic systems ``` git clone https://github.com/e3nn/e3nn-jax ``` -- [PyPi](https://pypi.org/project/e3nn-jax) (📥 5.8K / month · 📦 13 · ⏱️ 14.08.2024): +- [PyPi](https://pypi.org/project/e3nn-jax) (📥 6.2K / month · 📦 13 · ⏱️ 14.08.2024): ``` pip install e3nn-jax ``` @@ -2170,11 +2165,23 @@ _General models that learn a representations aka embeddings of atomistic systems ``` git clone https://github.com/divelab/DIG ``` -- [PyPi](https://pypi.org/project/dive-into-graphs) (📥 1.4K / month · ⏱️ 27.06.2022): +- [PyPi](https://pypi.org/project/dive-into-graphs) (📥 1.6K / month · ⏱️ 27.06.2022): ``` pip install dive-into-graphs ```
+
ALIGNN (🥈21 · ⭐ 230 · 📉) - Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en. Custom + +- [GitHub](https://github.com/usnistgov/alignn) (👨‍💻 7 · 🔀 81 · 📦 16 · 📋 65 - 61% open · ⏱️ 09.09.2024): + + ``` + git clone https://github.com/usnistgov/alignn + ``` +- [PyPi](https://pypi.org/project/alignn) (📥 18K / month · 📦 6 · ⏱️ 09.09.2024): + ``` + pip install alignn + ``` +
kgcnn (🥈19 · ⭐ 110) - Graph convolutions in Keras with TensorFlow, PyTorch or Jax. MIT - [GitHub](https://github.com/aimat-lab/gcnn_keras) (👨‍💻 7 · 🔀 30 · 📦 19 · 📋 86 - 13% open · ⏱️ 06.05.2024): @@ -2182,14 +2189,14 @@ _General models that learn a representations aka embeddings of atomistic systems ``` git clone https://github.com/aimat-lab/gcnn_keras ``` -- [PyPi](https://pypi.org/project/kgcnn) (📥 2.1K / month · 📦 3 · ⏱️ 27.02.2024): +- [PyPi](https://pypi.org/project/kgcnn) (📥 2.6K / month · 📦 3 · ⏱️ 27.02.2024): ``` pip install kgcnn ```
Uni-Mol (🥈18 · ⭐ 690) - Official Repository for the Uni-Mol Series Methods. MIT pretrained -- [GitHub](https://github.com/deepmodeling/Uni-Mol) (👨‍💻 17 · 🔀 120 · 📥 15K · 📋 160 - 41% open · ⏱️ 21.10.2024): +- [GitHub](https://github.com/deepmodeling/Uni-Mol) (👨‍💻 17 · 🔀 120 · 📥 16K · 📋 160 - 42% open · ⏱️ 24.10.2024): ``` git clone https://github.com/deepmodeling/Uni-Mol @@ -2202,14 +2209,14 @@ _General models that learn a representations aka embeddings of atomistic systems ``` git clone https://github.com/QUVA-Lab/escnn ``` -- [PyPi](https://pypi.org/project/escnn) (📥 1.9K / month · 📦 6 · ⏱️ 01.04.2022): +- [PyPi](https://pypi.org/project/escnn) (📥 2.1K / month · 📦 6 · ⏱️ 01.04.2022): ``` pip install escnn ```
matsciml (🥈17 · ⭐ 140) - Open MatSci ML Toolkit is a framework for prototyping and scaling out deep learning models for materials discovery.. MIT workflows benchmarking -- [GitHub](https://github.com/IntelLabs/matsciml) (👨‍💻 12 · 🔀 19 · 📋 61 - 32% open · ⏱️ 15.10.2024): +- [GitHub](https://github.com/IntelLabs/matsciml) (👨‍💻 12 · 🔀 20 · 📋 62 - 33% open · ⏱️ 22.10.2024): ``` git clone https://github.com/IntelLabs/matsciml @@ -2238,14 +2245,14 @@ _General models that learn a representations aka embeddings of atomistic systems ``` git clone https://github.com/sparks-baird/CrabNet ``` -- [PyPi](https://pypi.org/project/crabnet) (📥 3K / month · 📦 2 · ⏱️ 10.01.2023): +- [PyPi](https://pypi.org/project/crabnet) (📥 3.4K / month · 📦 2 · ⏱️ 10.01.2023): ``` pip install crabnet ```
hippynn (🥈12 · ⭐ 69) - python library for atomistic machine learning. Custom workflows -- [GitHub](https://github.com/lanl/hippynn) (👨‍💻 14 · 🔀 23 · 📦 2 · 📋 19 - 36% open · ⏱️ 18.10.2024): +- [GitHub](https://github.com/lanl/hippynn) (👨‍💻 14 · 🔀 23 · 📦 2 · 📋 20 - 40% open · ⏱️ 22.10.2024): ``` git clone https://github.com/lanl/hippynn @@ -2258,19 +2265,19 @@ _General models that learn a representations aka embeddings of atomistic systems ``` git clone https://github.com/vict0rsch/faenet ``` -- [PyPi](https://pypi.org/project/faenet) (📥 330 / month · ⏱️ 14.09.2023): +- [PyPi](https://pypi.org/project/faenet) (📥 430 / month · ⏱️ 14.09.2023): ``` pip install faenet ```
Atom2Vec (🥈10 · ⭐ 35 · 💤) - Atom2Vec: a simple way to describe atoms for machine learning. MIT -- [GitHub](https://github.com/idocx/Atom2Vec) (👨‍💻 1 · 🔀 9 · 📦 3 · 📋 3 - 66% open · ⏱️ 23.02.2024): +- [GitHub](https://github.com/idocx/Atom2Vec) (👨‍💻 1 · 🔀 9 · 📦 3 · 📋 4 - 75% open · ⏱️ 23.02.2024): ``` git clone https://github.com/idocx/Atom2Vec ``` -- [PyPi](https://pypi.org/project/atom2vec) (📥 420 / month · ⏱️ 23.02.2024): +- [PyPi](https://pypi.org/project/atom2vec) (📥 490 / month · ⏱️ 23.02.2024): ``` pip install atom2vec ``` @@ -2283,7 +2290,7 @@ _General models that learn a representations aka embeddings of atomistic systems git clone https://github.com/HSE-LAMBDA/ai4material_design ```
-
EquiformerV2 (🥉8 · ⭐ 200) - [ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. MIT +
EquiformerV2 (🥉8 · ⭐ 210) - [ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. MIT - [GitHub](https://github.com/atomicarchitects/equiformer_v2) (👨‍💻 2 · 🔀 26 · 📋 18 - 77% open · ⏱️ 16.07.2024): @@ -2343,7 +2350,7 @@ _General models that learn a representations aka embeddings of atomistic systems - escnn_jax (🥉7 · ⭐ 27 · 💀) - 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 - MACE-Layer (🥉6 · ⭐ 32 · 💀) - Higher order equivariant graph neural networks for 3D point clouds. MIT -- charge_transfer_nnp (🥉6 · ⭐ 31 · 💀) - Graph neural network potential with charge transfer. MIT electrostatics +- charge_transfer_nnp (🥉6 · ⭐ 32 · 💀) - Graph neural network potential with charge transfer. MIT electrostatics - GLAMOUR (🥉6 · ⭐ 21 · 💀) - Graph Learning over Macromolecule Representations. MIT single-paper - Autobahn (🥉5 · ⭐ 30 · 💀) - Repository for Autobahn: Automorphism Based Graph Neural Networks. MIT - FieldSchNet (🥉5 · ⭐ 17 · 💀) - Deep neural network for molecules in external fields. MIT @@ -2375,7 +2382,7 @@ _Machine-learned interatomic potentials (ML-IAP) that have been trained on large
DPA-2 (🥇24 · ⭐ 1.5K) - 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 -- [GitHub](https://github.com/deepmodeling/deepmd-kit) (👨‍💻 69 · 🔀 510 · 📥 41K · 📦 19 · 📋 820 - 12% open · ⏱️ 17.09.2024): +- [GitHub](https://github.com/deepmodeling/deepmd-kit) (👨‍💻 69 · 🔀 510 · 📥 42K · 📦 19 · 📋 820 - 12% open · ⏱️ 17.09.2024): ``` git clone https://github.com/deepmodeling/deepmd-kit @@ -2383,24 +2390,24 @@ _Machine-learned interatomic potentials (ML-IAP) that have been trained on large
CHGNet (🥈23 · ⭐ 240) - Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov. Custom ML-IAP MD pretrained electrostatics magnetism structure-relaxation -- [GitHub](https://github.com/CederGroupHub/chgnet) (👨‍💻 10 · 🔀 65 · 📦 37 · 📋 59 - 5% open · ⏱️ 16.10.2024): +- [GitHub](https://github.com/CederGroupHub/chgnet) (👨‍💻 10 · 🔀 65 · 📦 38 · 📋 61 - 4% open · ⏱️ 16.10.2024): ``` git clone https://github.com/CederGroupHub/chgnet ``` -- [PyPi](https://pypi.org/project/chgnet) (📥 38K / month · 📦 21 · ⏱️ 16.09.2024): +- [PyPi](https://pypi.org/project/chgnet) (📥 45K / month · 📦 21 · ⏱️ 16.09.2024): ``` pip install chgnet ```
MACE-MP (🥈18 · ⭐ 500) - Pretrained foundation models for materials chemistry. MIT ML-IAP pretrained rep-learn MD -- [GitHub](https://github.com/ACEsuit/mace-mp) (👨‍💻 2 · 🔀 190 · 📥 30K · 📋 10 - 10% open · ⏱️ 24.04.2024): +- [GitHub](https://github.com/ACEsuit/mace-mp) (👨‍💻 2 · 🔀 190 · 📥 31K · 📋 10 - 10% open · ⏱️ 24.04.2024): ``` git clone https://github.com/ACEsuit/mace-mp ``` -- [PyPi](https://pypi.org/project/mace-torch) (📥 13K / month · 📦 14 · ⏱️ 16.07.2024): +- [PyPi](https://pypi.org/project/mace-torch) (📥 14K / month · 📦 14 · ⏱️ 16.07.2024): ``` pip install mace-torch ``` @@ -2412,34 +2419,34 @@ _Machine-learned interatomic potentials (ML-IAP) that have been trained on large ``` git clone https://github.com/materialsvirtuallab/m3gnet ``` -- [PyPi](https://pypi.org/project/m3gnet) (📥 2.1K / month · 📦 5 · ⏱️ 17.11.2022): +- [PyPi](https://pypi.org/project/m3gnet) (📥 1.9K / month · 📦 5 · ⏱️ 17.11.2022): ``` pip install m3gnet ```
-
Orb Models (🥉15 · ⭐ 170 · 🐣) - ORB forcefield models from Orbital Materials. Custom ML-IAP pretrained +
Orb Models (🥉16 · ⭐ 170 · 🐣) - ORB forcefield models from Orbital Materials. Custom ML-IAP pretrained -- [GitHub](https://github.com/orbital-materials/orb-models) (👨‍💻 6 · 🔀 20 · 📦 2 · 📋 13 - 7% open · ⏱️ 17.10.2024): +- [GitHub](https://github.com/orbital-materials/orb-models) (👨‍💻 6 · 🔀 20 · 📦 3 · ⏱️ 17.10.2024): ``` git clone https://github.com/orbital-materials/orb-models ``` -- [PyPi](https://pypi.org/project/orb-models) (📥 1.3K / month · ⏱️ 15.10.2024): +- [PyPi](https://pypi.org/project/orb-models) (📥 1.4K / month · ⏱️ 15.10.2024): ``` pip install orb-models ```
-
SevenNet (🥉14 · ⭐ 120) - SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that.. GPL-3.0 ML-IAP MD pretrained +
SevenNet (🥉16 · ⭐ 120 · 📈) - SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that.. GPL-3.0 ML-IAP MD pretrained -- [GitHub](https://github.com/MDIL-SNU/SevenNet) (👨‍💻 12 · 🔀 13 · 📦 4 · 📋 25 - 44% open · ⏱️ 21.10.2024): +- [GitHub](https://github.com/MDIL-SNU/SevenNet) (👨‍💻 12 · 🔀 13 · 📦 6 · 📋 22 - 36% open · ⏱️ 24.10.2024): ``` git clone https://github.com/MDIL-SNU/SevenNet ```
-
MLIP Arena Leaderboard (🥉10 · ⭐ 28 · ➕) - Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics. Apache-2 ML-IAP community-resource +
MLIP Arena Leaderboard (🥉11 · ⭐ 32) - Fair and transparent benchmark of machine-learned interatomic potentials (MLIPs), beyond basic error metrics. Apache-2 ML-IAP community-resource -- [GitHub](https://github.com/atomind-ai/mlip-arena) (👨‍💻 2 · 🔀 1 · 📋 8 - 50% open · ⏱️ 21.10.2024): +- [GitHub](https://github.com/atomind-ai/mlip-arena) (👨‍💻 3 · 🔀 1 · 📦 2 · 📋 7 - 57% open · ⏱️ 24.10.2024): ``` git clone https://github.com/atomind-ai/mlip-arena @@ -2447,7 +2454,7 @@ _Machine-learned interatomic potentials (ML-IAP) that have been trained on large
Joint Multidomain Pre-Training (JMP) (🥉5 · ⭐ 38) - 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 -- [GitHub](https://github.com/facebookresearch/JMP) (👨‍💻 1 · 🔀 6 · ⏱️ 07.05.2024): +- [GitHub](https://github.com/facebookresearch/JMP) (👨‍💻 2 · 🔀 6 · 📋 3 - 66% open · ⏱️ 22.10.2024): ``` git clone https://github.com/facebookresearch/JMP @@ -2468,7 +2475,7 @@ _Projects that focus on unsupervised learning (USL) for atomistic ML, such as di ``` git clone https://github.com/sissa-data-science/DADApy ``` -- [PyPi](https://pypi.org/project/dadapy) (📥 540 / month · ⏱️ 02.07.2024): +- [PyPi](https://pypi.org/project/dadapy) (📥 600 / month · ⏱️ 02.07.2024): ``` pip install dadapy ``` @@ -2497,26 +2504,26 @@ _Projects that focus on unsupervised learning (USL) for atomistic ML, such as di _Projects that focus on visualization (viz.) for atomistic ML._ -
Crystal Toolkit (🥇24 · ⭐ 150) - Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials.. MIT +
Crystal Toolkit (🥇25 · ⭐ 150 · 📈) - Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials.. MIT -- [GitHub](https://github.com/materialsproject/crystaltoolkit) (👨‍💻 28 · 🔀 57 · 📦 39 · 📋 110 - 47% open · ⏱️ 20.09.2024): +- [GitHub](https://github.com/materialsproject/crystaltoolkit) (👨‍💻 28 · 🔀 57 · 📦 39 · 📋 110 - 47% open · ⏱️ 22.10.2024): ``` git clone https://github.com/materialsproject/crystaltoolkit ``` -- [PyPi](https://pypi.org/project/crystal-toolkit) (📥 5.4K / month · 📦 8 · ⏱️ 04.09.2024): +- [PyPi](https://pypi.org/project/crystal-toolkit) (📥 6.1K / month · 📦 10 · ⏱️ 22.10.2024): ``` pip install crystal-toolkit ```
-
pymatviz (🥈22 · ⭐ 160) - A toolkit for visualizations in materials informatics. MIT general-tool probabilistic +
pymatviz (🥈22 · ⭐ 170) - A toolkit for visualizations in materials informatics. MIT general-tool probabilistic - [GitHub](https://github.com/janosh/pymatviz) (👨‍💻 9 · 🔀 15 · 📦 10 · 📋 53 - 30% open · ⏱️ 18.10.2024): ``` git clone https://github.com/janosh/pymatviz ``` -- [PyPi](https://pypi.org/project/pymatviz) (📥 4.7K / month · 📦 2 · ⏱️ 18.10.2024): +- [PyPi](https://pypi.org/project/pymatviz) (📥 5.1K / month · 📦 2 · ⏱️ 18.10.2024): ``` pip install pymatviz ``` @@ -2528,14 +2535,14 @@ _Projects that focus on visualization (viz.) for atomistic ML._ ``` git clone https://github.com/zincware/ZnDraw ``` -- [PyPi](https://pypi.org/project/zndraw) (📥 3.3K / month · 📦 2 · ⏱️ 26.08.2024): +- [PyPi](https://pypi.org/project/zndraw) (📥 3.6K / month · 📦 2 · ⏱️ 26.08.2024): ``` pip install zndraw ```
Chemiscope (🥉18 · ⭐ 130) - An interactive structure/property explorer for materials and molecules. BSD-3 JavaScript -- [GitHub](https://github.com/lab-cosmo/chemiscope) (👨‍💻 23 · 🔀 32 · 📥 330 · 📦 6 · 📋 130 - 27% open · ⏱️ 21.10.2024): +- [GitHub](https://github.com/lab-cosmo/chemiscope) (👨‍💻 24 · 🔀 32 · 📥 330 · 📦 6 · 📋 130 - 27% open · ⏱️ 22.10.2024): ``` git clone https://github.com/lab-cosmo/chemiscope @@ -2552,7 +2559,7 @@ _Projects that focus on visualization (viz.) for atomistic ML._ ``` git clone https://github.com/janosh/elementari ``` -- [npm](https://www.npmjs.com/package/elementari) (📥 240 / month · 📦 1 · ⏱️ 15.01.2024): +- [npm](https://www.npmjs.com/package/elementari) (📥 260 / month · 📦 1 · ⏱️ 15.01.2024): ``` npm install elementari ``` @@ -2569,14 +2576,14 @@ _Projects that focus on visualization (viz.) for atomistic ML._ _Projects and models that focus on quantities of wavefunction theory methods, such as Monte Carlo techniques like deep learning variational Monte Carlo (DL-VMC), quantum chemistry methods, etc._ -
DeepQMC (🥇21 · ⭐ 350) - Deep learning quantum Monte Carlo for electrons in real space. MIT +
DeepQMC (🥇22 · ⭐ 350 · 📈) - Deep learning quantum Monte Carlo for electrons in real space. MIT -- [GitHub](https://github.com/deepqmc/deepqmc) (👨‍💻 13 · 🔀 60 · 📦 3 · 📋 46 - 6% open · ⏱️ 24.09.2024): +- [GitHub](https://github.com/deepqmc/deepqmc) (👨‍💻 13 · 🔀 60 · 📦 3 · 📋 47 - 6% open · ⏱️ 23.10.2024): ``` git clone https://github.com/deepqmc/deepqmc ``` -- [PyPi](https://pypi.org/project/deepqmc) (📥 980 / month · ⏱️ 24.09.2024): +- [PyPi](https://pypi.org/project/deepqmc) (📥 1.1K / month · ⏱️ 24.09.2024): ``` pip install deepqmc ``` @@ -2591,12 +2598,12 @@ _Projects and models that focus on quantities of wavefunction theory methods, su
DeepErwin (🥉9 · ⭐ 49) - DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions.. Custom -- [GitHub](https://github.com/mdsunivie/deeperwin) (👨‍💻 7 · 🔀 7 · 📥 11 · ⏱️ 07.06.2024): +- [GitHub](https://github.com/mdsunivie/deeperwin) (👨‍💻 7 · 🔀 7 · 📥 12 · ⏱️ 07.06.2024): ``` git clone https://github.com/mdsunivie/deeperwin ``` -- [PyPi](https://pypi.org/project/deeperwin) (📥 420 / month · ⏱️ 14.12.2021): +- [PyPi](https://pypi.org/project/deeperwin) (📥 480 / month · ⏱️ 14.12.2021): ``` pip install deeperwin ``` @@ -2604,7 +2611,7 @@ _Projects and models that focus on quantities of wavefunction theory methods, su
Show 2 hidden projects... - ACEpsi.jl (🥉6 · ⭐ 2 · 💤) - ACE wave function parameterizations. MIT rep-eng Julia -- SchNOrb (🥉5 · ⭐ 59 · 💀) - Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. MIT +- SchNOrb (🥉5 · ⭐ 60 · 💀) - Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions. MIT

diff --git a/history/2024-10-24_changes.md b/history/2024-10-24_changes.md new file mode 100644 index 0000000..c0a7ed2 --- /dev/null +++ b/history/2024-10-24_changes.md @@ -0,0 +1,20 @@ +## 📈 Trending Up + +_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._ + +- OPTIMADE Python tools (🥇27 · ⭐ 69 · 📈) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT +- Crystal Toolkit (🥇25 · ⭐ 150 · 📈) - Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials.. MIT +- GPUMD (🥇22 · ⭐ 460 · 📈) - GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials.. GPL-3.0 MD C++ electrostatics +- DeepQMC (🥇22 · ⭐ 350 · 📈) - Deep learning quantum Monte Carlo for electrons in real space. MIT +- SevenNet (🥉16 · ⭐ 120 · 📈) - SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that.. GPL-3.0 ML-IAP MD pretrained + +## 📉 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._ + +- DeepChem (🥇35 · ⭐ 5.5K · 📉) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. MIT +- FAIR Chemistry datasets (🥇24 · ⭐ 830 · 📉) - Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project. MIT catalysis +- fairchem (🥇24 · ⭐ 830 · 📉) - FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project. MIT pretrained UIP rep-learn catalysis +- NequIP (🥇22 · ⭐ 620 · 📉) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT +- ALIGNN (🥈21 · ⭐ 230 · 📉) - Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en. Custom + diff --git a/history/2024-10-24_projects.csv b/history/2024-10-24_projects.csv new file mode 100644 index 0000000..ea19b39 --- /dev/null +++ b/history/2024-10-24_projects.csv @@ -0,0 +1,432 @@ +,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_latest_release_published_at,pypi_dependent_project_count,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,trending,github_release_downloads,updated_github_id,maven_id,maven_url,maven_latest_release_published_at,maven_dependent_project_count,npm_id,npm_url,npm_latest_release_published_at,npm_dependent_project_count,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/,A large scale benchmark of 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 16:48:33,2024-10-12 19:11:30.000,2024-10-12 18:57:04,867.0,51.0,44.0,5.0,328.0,18.0,2.0,59.0,2024-10-12 19:11:30.000,2024.10.10,29.0,33.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +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.",38,True,,dmlc/dgl,https://github.com/dmlc/dgl,2018-04-20 14:49:09,2024-10-18 03:43:55.000,2024-10-18 03:43:55,4410.0,170.0,3010.0,175.0,5049.0,531.0,2357.0,13483.0,2024-09-03 04:16:25.000,2.4.0,453.0,295.0,dgl,dglteam/dgl,455.0,307.0,https://pypi.org/project/dgl,2024-05-13 01:10:39.000,148.0,171495.0,176946.0,https://anaconda.org/dglteam/dgl,2024-09-03 05:08:29.197,381607.0,1.0,,,,,,,,,,,,,,,,,,,,, +33,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 16:03:03,2024-10-23 23:34:11.000,2024-10-21 03:30:12,7633.0,54.0,3639.0,252.0,3147.0,1045.0,2660.0,21223.0,2024-09-26 07:09:50.000,2.6.1,42.0,520.0,,,6719.0,6719.0,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +34,DeepChem,,general-tool,MIT,https://github.com/deepchem/deepchem,"Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology.",35,True,,deepchem/deepchem,https://github.com/deepchem/deepchem,2015-09-24 23:20:28,2024-10-24 17:48:52.000,2024-10-24 17:48:52,10545.0,38.0,1676.0,143.0,2446.0,638.0,1238.0,5467.0,2024-04-03 16:21:23.000,2.8.0,932.0,250.0,deepchem,conda-forge/deepchem,456.0,443.0,https://pypi.org/project/deepchem,2024-10-21 16:58:55.000,13.0,99142.0,101338.0,https://anaconda.org/conda-forge/deepchem,2024-04-05 16:46:45.105,110500.0,1.0,deepchemio/deepchem,https://hub.docker.com/r/deepchemio/deepchem,2024-10-21 17:15:56.027654,5.0,7770.0,-1.0,,,,,,,,,,,,,,, +35,RDKit,,general-tool,BSD-3-Clause,https://github.com/rdkit/rdkit,,35,True,['lang-cpp'],rdkit/rdkit,https://github.com/rdkit/rdkit,2013-05-12 06:19:15,2024-10-24 17:31:04.000,2024-10-24 17:31:04,7905.0,102.0,873.0,81.0,3303.0,958.0,2395.0,2634.0,2024-09-27 11:53:59.000,Release_2024_09_1,100.0,235.0,rdkit,rdkit/rdkit,751.0,3.0,https://pypi.org/project/rdkit,2024-08-07 12:34:25.000,748.0,2074080.0,2094850.0,https://anaconda.org/rdkit/rdkit,2023-06-16 12:54:07.547,2574452.0,1.0,,,,,,,1096.0,,,,,,,,,,,,,, +36,paper-qa,,language-models,Apache-2.0,https://github.com/Future-House/paper-qa,High accuracy RAG for answering questions from scientific documents with citations.,31,True,['ai-agent'],whitead/paper-qa,https://github.com/Future-House/paper-qa,2023-02-05 01:07:25,2024-10-24 05:47:57.000,2024-10-23 19:13:51,454.0,208.0,586.0,58.0,359.0,99.0,153.0,6281.0,2024-10-23 00:15:40.000,5.3.1,121.0,26.0,paper-qa,,84.0,76.0,https://pypi.org/project/paper-qa,2024-10-23 00:15:40.000,8.0,17654.0,17654.0,,,,1.0,,,,,,,,Future-House/paper-qa,,,,,,,,,,,,, +37,Matminer,,general-tool,https://github.com/hackingmaterials/matminer/blob/main/LICENSE,https://github.com/hackingmaterials/matminer,Data mining for materials science.,29,True,,hackingmaterials/matminer,https://github.com/hackingmaterials/matminer,2015-09-24 20:37:00,2024-10-21 08:06:12.000,2024-10-11 14:20:38,4174.0,18.0,194.0,28.0,728.0,31.0,200.0,479.0,2024-10-06 12:46:05.000,0.9.3,72.0,56.0,matminer,conda-forge/matminer,384.0,324.0,https://pypi.org/project/matminer,2024-10-06 12:46:05.000,60.0,21339.0,22851.0,https://anaconda.org/conda-forge/matminer,2024-10-06 16:00:19.611,72581.0,1.0,,,,,,,,,,,,,,,,,,,,, +38,SchNetPack,,rep-learn,MIT,https://github.com/atomistic-machine-learning/schnetpack,SchNetPack - Deep Neural Networks for Atomistic Systems.,28,True,,atomistic-machine-learning/schnetpack,https://github.com/atomistic-machine-learning/schnetpack,2018-09-03 15:44:35,2024-10-24 14:29:05.000,2024-09-24 14:00:57,1698.0,23.0,213.0,32.0,421.0,6.0,245.0,782.0,2024-09-05 11:35:29.000,2.1.1,12.0,36.0,schnetpack,,96.0,92.0,https://pypi.org/project/schnetpack,2024-09-05 11:35:29.000,4.0,1650.0,1650.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +39,e3nn,,rep-learn,MIT,https://github.com/e3nn/e3nn,A modular framework for neural networks with Euclidean symmetry.,27,True,,e3nn/e3nn,https://github.com/e3nn/e3nn,2020-01-31 13:06:42,2024-10-21 16:05:46.000,2024-10-21 16:04:07,2177.0,4.0,138.0,19.0,229.0,23.0,134.0,959.0,2024-10-21 16:05:46.000,0.5.3,30.0,31.0,e3nn,conda-forge/e3nn,330.0,326.0,https://pypi.org/project/e3nn,2022-04-13 19:24:30.000,4.0,89339.0,90151.0,https://anaconda.org/conda-forge/e3nn,2023-06-18 08:41:30.723,23566.0,1.0,,,,,,,,,,,,,,,,,,,,, +40,OPTIMADE Python tools,,datasets,MIT,https://github.com/Materials-Consortia/optimade-python-tools,Tools for implementing and consuming OPTIMADE APIs in Python.,27,True,,Materials-Consortia/optimade-python-tools,https://github.com/Materials-Consortia/optimade-python-tools,2018-06-05 21:00:07,2024-10-22 12:27:57.000,2024-10-22 12:23:47,1682.0,44.0,42.0,7.0,1727.0,107.0,352.0,69.0,2024-10-15 20:37:15.000,1.1.5,111.0,28.0,optimade,conda-forge/optimade,65.0,61.0,https://pypi.org/project/optimade,2024-10-15 20:37:15.000,4.0,14359.0,16381.0,https://anaconda.org/conda-forge/optimade,2024-10-16 01:14:26.563,95045.0,1.0,,,,,,1.0,,,,,,,,,,,,,,, +41,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.,26,True,['lang-cpp'],deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2024-10-24 11:11:58.000,2024-09-17 18:00:40,2534.0,1.0,507.0,47.0,2093.0,103.0,717.0,1478.0,2024-07-03 19:29:34.000,2.2.11,54.0,69.0,deepmd-kit,deepmodeling/deepmd-kit,23.0,19.0,https://pypi.org/project/deepmd-kit,2024-09-25 16:10:59.000,4.0,11378.0,12142.0,https://anaconda.org/deepmodeling/deepmd-kit,2024-04-06 21:22:08.456,1393.0,1.0,deepmodeling/deepmd-kit,https://hub.docker.com/r/deepmodeling/deepmd-kit,2024-07-27 08:24:51.741318,1.0,2920.0,,41709.0,,,,,,,,,,,,,, +42,JAX-MD,,md,Apache-2.0,https://github.com/jax-md/jax-md,"Differentiable, Hardware Accelerated, Molecular Dynamics.",26,True,,jax-md/jax-md,https://github.com/jax-md/jax-md,2019-05-13 21:03:37,2024-09-05 09:24:47.000,2024-09-05 09:24:47,922.0,11.0,190.0,47.0,171.0,73.0,81.0,1176.0,2023-08-09 23:18:24.000,0.2.8,38.0,34.0,jax-md,,63.0,60.0,https://pypi.org/project/jax-md,2023-08-09 23:18:24.000,3.0,5358.0,5358.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +43,cdk,,rep-eng,LGPL-2.1,https://github.com/cdk/cdk,The Chemistry Development Kit.,26,True,"['cheminformatics', 'lang-java']",cdk/cdk,https://github.com/cdk/cdk,2010-05-11 08:30:07,2024-10-22 12:15:23.000,2024-10-21 06:06:07,17795.0,85.0,158.0,40.0,826.0,32.0,261.0,494.0,2023-08-21 19:50:47.000,cdk-2.9,20.0,165.0,,,16.0,,,,,,183.0,,,,1.0,,,,,,,22255.0,,org.openscience.cdk:cdk-bundle,https://search.maven.org/artifact/org.openscience.cdk/cdk-bundle,2023-08-21 08:05:58,16.0,,,,,,,,, +44,QUIP,,general-tool,GPL-2.0,https://github.com/libAtoms/QUIP,libAtoms/QUIP molecular dynamics framework: https://libatoms.github.io.,26,True,"['md', 'ml-iap', 'rep-eng', 'lang-fortran']",libAtoms/QUIP,https://github.com/libAtoms/QUIP,2013-07-02 15:21:59,2024-09-27 15:26:55.000,2024-09-27 15:18:25,10866.0,5.0,122.0,26.0,177.0,105.0,362.0,352.0,2023-06-15 19:54:01.129,0.9.14,15.0,85.0,quippy-ase,,47.0,43.0,https://pypi.org/project/quippy-ase,2023-01-15 16:54:03.041,4.0,11581.0,11665.0,,,,2.0,libatomsquip/quip,https://hub.docker.com/r/libatomsquip/quip,2023-04-24 21:25:17.345957,4.0,9959.0,,687.0,,,,,,,,,,,,,, +45,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 18:42:48,2024-10-21 16:13:51.000,2024-10-21 16:12:57,4684.0,101.0,7854.0,749.0,874.0,1477.0,331.0,34115.0,,,,808.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +46,DScribe,,rep-eng,Apache-2.0,https://github.com/SINGROUP/dscribe,DScribe is a python package for creating machine learning descriptors for atomistic systems.,25,True,,SINGROUP/dscribe,https://github.com/SINGROUP/dscribe,2017-05-08 08:29:51,2024-08-20 17:56:51.000,2024-05-28 18:24:28,1288.0,,87.0,18.0,27.0,12.0,93.0,398.0,2024-05-28 18:22:25.000,2.1.1,32.0,18.0,dscribe,conda-forge/dscribe,239.0,204.0,https://pypi.org/project/dscribe,2024-05-28 18:22:25.000,35.0,25246.0,28068.0,https://anaconda.org/conda-forge/dscribe,2024-05-28 23:16:49.298,143954.0,1.0,,,,,,,,,,,,,,,,,,,,, +47,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 15:04:21,2024-10-22 13:41:21.000,2024-10-10 11:31:53,1769.0,22.0,78.0,21.0,599.0,9.0,62.0,365.0,2024-06-13 15:29:41.000,2024.6.13,16.0,33.0,maml,,13.0,11.0,https://pypi.org/project/maml,2024-06-13 15:29:41.000,2.0,1243.0,1243.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +48,Crystal Toolkit,,visualization,MIT,https://github.com/materialsproject/crystaltoolkit,Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials..,25,True,,materialsproject/crystaltoolkit,https://github.com/materialsproject/crystaltoolkit,2017-07-25 21:06:36,2024-10-22 23:25:31.000,2024-10-22 23:19:33,3280.0,20.0,57.0,10.0,317.0,53.0,59.0,151.0,2024-10-22 23:22:01.000,2024.10.22,61.0,28.0,crystal-toolkit,,49.0,39.0,https://pypi.org/project/crystal-toolkit,2024-10-22 23:25:31.000,10.0,6055.0,6055.0,,,,1.0,,,,,,1.0,,,,,,,,,,,,,,, +49,MPContribs,,datasets,MIT,https://github.com/materialsproject/MPContribs,Platform for materials scientists to contribute and disseminate their materials data through Materials Project.,25,True,,materialsproject/MPContribs,https://github.com/materialsproject/MPContribs,2014-12-11 18:25:27,2024-10-22 22:26:01.000,2024-10-22 20:59:01,5649.0,83.0,20.0,10.0,1747.0,22.0,79.0,35.0,2024-10-17 00:30:46.000,5.9.0,163.0,25.0,mpcontribs-client,,43.0,40.0,https://pypi.org/project/mpcontribs-client,2024-10-17 00:30:46.000,3.0,12490.0,12490.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +50,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.,24,True,"['ml-iap', 'pretrained', 'workflows', 'datasets']",deepmodeling/deepmd-kit,https://github.com/deepmodeling/deepmd-kit,2017-12-12 15:23:44,2024-10-24 11:11:58.000,2024-09-17 18:00:40,2534.0,1.0,507.0,47.0,2093.0,103.0,717.0,1477.0,2024-07-03 19:22:15.000,2.2.11,50.0,69.0,,,19.0,19.0,,,,,706.0,,,,1.0,,,,,,,41709.0,,,,,,,,,,,,,, +51,FAIR Chemistry datasets,,datasets,MIT,https://github.com/FAIR-Chem/fairchem,"Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project.",24,True,['catalysis'],FAIR-Chem/fairchem,https://github.com/FAIR-Chem/fairchem,2019-09-26 04:47:27,2024-10-24 16:14:05.000,2024-10-23 20:23:28,869.0,68.0,244.0,27.0,678.0,31.0,199.0,831.0,2024-10-23 19:31:49.000,fairchem_demo_ocpapi-0.2.0,12.0,42.0,fairchem-core,,1.0,,https://pypi.org/project/fairchem-core,2024-09-14 01:28:48.000,1.0,2172.0,2172.0,,,,1.0,,,,,,-1.0,,,,,,,,,,,,,,, +52,fairchem,,ml-iap,MIT,https://github.com/FAIR-Chem/fairchem,FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project.,24,True,"['pretrained', 'uip', 'rep-learn', 'catalysis']",FAIR-Chem/fairchem,https://github.com/FAIR-Chem/fairchem,2019-09-26 04:47:27,2024-10-24 16:14:05.000,2024-10-23 20:23:28,869.0,68.0,244.0,27.0,678.0,31.0,199.0,831.0,2024-10-23 19:31:49.000,fairchem_demo_ocpapi-0.2.0,12.0,42.0,fairchem-core,,1.0,,https://pypi.org/project/fairchem-core,2024-09-14 01:28:48.000,1.0,2172.0,2172.0,,,,1.0,,,,,,-1.0,,,,,,,,,,,,,,, +53,TorchANI,,ml-iap,MIT,https://github.com/aiqm/torchani,Accurate Neural Network Potential on PyTorch.,24,True,,aiqm/torchani,https://github.com/aiqm/torchani,2018-04-02 15:43:04,2024-09-11 21:03:45.224,2023-11-14 16:32:59,434.0,,126.0,30.0,484.0,24.0,150.0,463.0,2023-11-14 16:41:14.000,2.2.4,24.0,19.0,torchani,conda-forge/torchani,48.0,44.0,https://pypi.org/project/torchani,2023-11-14 16:41:14.000,4.0,5451.0,15885.0,https://anaconda.org/conda-forge/torchani,2024-09-11 21:03:45.224,521748.0,1.0,,,,,,,,,,,,,,,,,,,,, +54,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:..,24,True,,usnistgov/jarvis,https://github.com/usnistgov/jarvis,2017-06-22 19:34:02,2024-10-20 01:55:14.000,2024-10-18 01:37:16,2109.0,3.0,125.0,26.0,238.0,46.0,45.0,306.0,2024-10-18 01:39:58.000,2024.10.10,111.0,15.0,jarvis-tools,conda-forge/jarvis-tools,133.0,102.0,https://pypi.org/project/jarvis-tools,2024-10-18 01:38:05.000,31.0,34449.0,36123.0,https://anaconda.org/conda-forge/jarvis-tools,2024-09-07 20:30:56.460,80399.0,2.0,,,,,,,,,,,,,,,,,,,,, +55,MatGL (Materials Graph Library),,rep-learn,BSD-3-Clause,https://github.com/materialsvirtuallab/matgl,Graph deep learning library for materials.,24,True,['multifidelity'],materialsvirtuallab/matgl,https://github.com/materialsvirtuallab/matgl,2022-08-29 18:36:05,2024-10-24 13:58:05.000,2024-10-24 01:07:37,1092.0,98.0,60.0,11.0,294.0,7.0,91.0,266.0,2024-08-07 12:24:58.000,1.1.3,31.0,17.0,m3gnet,,56.0,51.0,https://pypi.org/project/m3gnet,2022-11-17 23:25:34.805,5.0,1868.0,1868.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +56,dpdata,,data-structures,LGPL-3.0,https://github.com/deepmodeling/dpdata,A Python package for manipulating atomistic data of software in computational science.,24,True,,deepmodeling/dpdata,https://github.com/deepmodeling/dpdata,2019-04-12 13:24:23,2024-10-23 19:47:04.000,2024-09-20 18:23:06,761.0,28.0,130.0,9.0,513.0,33.0,82.0,196.0,2024-09-20 18:25:00.000,0.2.21,48.0,61.0,dpdata,deepmodeling/dpdata,166.0,126.0,https://pypi.org/project/dpdata,2024-09-20 18:25:00.000,40.0,52633.0,52639.0,https://anaconda.org/deepmodeling/dpdata,2023-09-27 20:07:36.945,236.0,1.0,,,,,,,,,,,,,,,,,,,,, +57,Best-of Machine Learning with Python,,community,CC-BY-4.0,https://github.com/ml-tooling/best-of-ml-python,A ranked list of awesome machine learning Python libraries. Updated weekly.,23,True,"['general-ml', 'lang-py']",ml-tooling/best-of-ml-python,https://github.com/ml-tooling/best-of-ml-python,2020-11-29 19:41:36,2024-10-24 16:27:44.000,2024-10-24 16:27:42,511.0,15.0,2360.0,406.0,276.0,27.0,34.0,16407.0,2024-10-24 16:27:48.000,2024.10.24,100.0,47.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +58,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 19:32:07.000,2023-04-16 03:55:52,236.0,,146.0,17.0,141.0,27.0,57.0,718.0,2023-02-13 08:45:17.000,0.3.2,17.0,22.0,dgllife,,255.0,251.0,https://pypi.org/project/dgllife,2022-12-21 13:18:00.570,4.0,13444.0,13444.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +59,MEGNet,,ml-iap,BSD-3-Clause,https://github.com/materialsvirtuallab/megnet,Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals.,23,False,['multifidelity'],materialsvirtuallab/megnet,https://github.com/materialsvirtuallab/megnet,2018-12-12 21:31:28,2023-04-27 02:39:17.000,2023-04-27 02:39:17,1146.0,,153.0,25.0,314.0,21.0,57.0,504.0,2022-11-16 21:25:01.818,1.3.2,37.0,14.0,megnet,,90.0,86.0,https://pypi.org/project/megnet,2022-11-16 21:25:01.818,4.0,3614.0,3614.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +60,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.,23,True,"['ml-iap', 'md', 'pretrained', 'electrostatics', 'magnetism', 'structure-relaxation']",CederGroupHub/chgnet,https://github.com/CederGroupHub/chgnet,2023-02-24 23:44:24,2024-10-16 10:11:18.000,2024-10-16 10:11:18,427.0,17.0,65.0,6.0,98.0,3.0,58.0,239.0,2024-09-16 22:18:58.000,0.4.0,17.0,10.0,chgnet,,59.0,38.0,https://pypi.org/project/chgnet,2024-09-16 22:18:58.000,21.0,45358.0,45358.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +61,NequIP,,ml-iap,MIT,https://github.com/mir-group/nequip,NequIP is a code for building E(3)-equivariant interatomic potentials.,22,True,,mir-group/nequip,https://github.com/mir-group/nequip,2021-03-15 23:44:39,2024-10-23 00:21:45.000,2024-10-21 20:13:55,1875.0,2.0,136.0,22.0,163.0,25.0,71.0,618.0,2024-07-09 16:05:06.000,0.6.1,16.0,12.0,nequip,conda-forge/nequip,29.0,28.0,https://pypi.org/project/nequip,2024-07-09 16:05:26.000,1.0,4826.0,5041.0,https://anaconda.org/conda-forge/nequip,2024-07-10 05:13:00.157,6258.0,1.0,,,,,,-1.0,,,,,,,,,,,,,,, +62,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 18:44:34,2024-10-24 10:28:44.000,2024-10-10 16:32:53,905.0,129.0,187.0,24.0,251.0,60.0,213.0,507.0,2024-10-02 18:02:28.000,0.3.7,8.0,42.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +63,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 15:32:56,2024-10-24 15:08:21.000,2024-10-24 15:08:21,4134.0,239.0,116.0,26.0,566.0,18.0,166.0,463.0,2024-08-18 13:16:02.000,3.9.5,42.0,35.0,,,,,,,,,,,,,1.0,,,,,,1.0,,,,,,,,,,,,,,, +64,DeepQMC,,ml-wft,MIT,https://github.com/deepqmc/deepqmc,Deep learning quantum Monte Carlo for electrons in real space.,22,True,,deepqmc/deepqmc,https://github.com/deepqmc/deepqmc,2019-12-06 14:50:59,2024-10-23 19:12:56.000,2024-10-23 19:12:54,1473.0,13.0,60.0,22.0,162.0,3.0,44.0,350.0,2024-09-24 11:12:20.000,1.2.0,12.0,13.0,deepqmc,,3.0,3.0,https://pypi.org/project/deepqmc,2024-09-24 11:12:20.000,,1072.0,1072.0,,,,1.0,,,,,,1.0,,,,,,,,,,,,,,, +65,TorchMD-NET,,ml-iap,MIT,https://github.com/torchmd/torchmd-net,Training neural network potentials.,22,True,"['md', 'rep-learn', 'transformer', 'pretrained']",torchmd/torchmd-net,https://github.com/torchmd/torchmd-net,2021-04-09 16:16:32,2024-09-12 06:52:04.422,2024-08-28 14:56:04,1287.0,13.0,72.0,10.0,231.0,34.0,84.0,327.0,2024-08-29 06:54:59.000,2.4.1,27.0,16.0,,conda-forge/torchmd-net,,,,,,,16412.0,https://anaconda.org/conda-forge/torchmd-net,2024-09-12 06:52:04.422,196952.0,1.0,,,,,,,,,,,,,,,,,,,,, +66,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 11:43:56,2024-10-22 06:25:12.000,2024-04-10 06:31:36,2083.0,,172.0,13.0,849.0,36.0,264.0,303.0,2024-04-10 06:37:54.000,0.12.1,18.0,64.0,dpgen,deepmodeling/dpgen,8.0,7.0,https://pypi.org/project/dpgen,2024-04-10 06:37:54.000,1.0,1725.0,1760.0,https://anaconda.org/deepmodeling/dpgen,2023-06-16 19:27:03.566,211.0,1.0,,,,,,,1803.0,,,,,,,,,,,,,, +67,FLARE,,active-learning,MIT,https://github.com/mir-group/flare,An open-source Python package for creating fast and accurate interatomic potentials.,22,True,"['lang-cpp', 'ml-iap']",mir-group/flare,https://github.com/mir-group/flare,2018-08-30 23:40:56,2024-10-23 19:13:10.000,2024-10-23 19:13:10,4567.0,72.0,68.0,21.0,199.0,36.0,180.0,290.0,2024-03-25 15:48:12.000,1.3.0,6.0,43.0,,,12.0,12.0,,,,,0.0,,,,1.0,,,,,,,8.0,,,,,,,,,,,,,, +68,e3nn-jax,,rep-learn,Apache-2.0,https://github.com/e3nn/e3nn-jax,jax library for E3 Equivariant Neural Networks.,22,True,,e3nn/e3nn-jax,https://github.com/e3nn/e3nn-jax,2021-06-08 13:21:51,2024-09-28 21:53:27.000,2024-09-28 21:53:25,1004.0,18.0,18.0,12.0,48.0,2.0,21.0,180.0,2024-08-14 05:14:56.000,0.20.7,43.0,7.0,e3nn-jax,,54.0,41.0,https://pypi.org/project/e3nn-jax,2024-08-14 05:15:15.000,13.0,6225.0,6225.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +69,pymatviz,,visualization,MIT,https://github.com/janosh/pymatviz,A toolkit for visualizations in materials informatics.,22,True,"['general-tool', 'probabilistic']",janosh/pymatviz,https://github.com/janosh/pymatviz,2021-02-21 12:40:34,2024-10-18 20:39:41.000,2024-10-18 20:33:54,365.0,48.0,15.0,7.0,185.0,16.0,37.0,168.0,2024-10-18 20:39:41.000,0.13.1,31.0,9.0,pymatviz,,12.0,10.0,https://pypi.org/project/pymatviz,2024-10-18 20:39:41.000,2.0,5114.0,5114.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +70,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 14:01:29.000,2024-04-04 13:37:56,1437.0,,3202.0,296.0,542.0,341.0,570.0,13463.0,,,,115.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +71,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 07:18:56.000,2024-02-04 20:37:53,1083.0,,281.0,31.0,41.0,34.0,176.0,1865.0,2023-04-07 20:33:15.000,1.1.0,10.0,50.0,dive-into-graphs,,,,https://pypi.org/project/dive-into-graphs,2022-06-27 05:08:24.000,,1601.0,1601.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +72,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.,21,True,,usnistgov/alignn,https://github.com/usnistgov/alignn,2021-04-19 20:08:09,2024-09-09 21:38:54.000,2024-09-09 21:38:06,717.0,8.0,81.0,11.0,109.0,40.0,25.0,226.0,2024-09-09 21:38:54.000,2024.8.30,48.0,7.0,alignn,,22.0,16.0,https://pypi.org/project/alignn,2024-09-09 21:37:57.000,6.0,18093.0,18093.0,,,,2.0,,,,,,-1.0,,,,,,,,,,,,,,, +73,mlcolvar,,md,MIT,https://github.com/luigibonati/mlcolvar,A unified framework for machine learning collective variables for enhanced sampling simulations.,21,True,['sampling'],luigibonati/mlcolvar,https://github.com/luigibonati/mlcolvar,2021-09-21 21:32:04,2024-10-09 10:22:29.000,2024-10-09 10:22:26,1138.0,49.0,24.0,7.0,85.0,13.0,61.0,89.0,2024-06-12 17:08:54.000,1.1.1,10.0,8.0,mlcolvar,,3.0,3.0,https://pypi.org/project/mlcolvar,2024-06-12 17:08:54.000,,467.0,467.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +74,Metatensor,,data-structures,BSD-3-Clause,https://github.com/metatensor/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/metatensor/metatensor,2022-03-01 15:58:28,2024-10-24 18:04:57.000,2024-10-24 16:12:36,827.0,63.0,17.0,18.0,556.0,70.0,145.0,52.0,2024-10-23 16:31:48.000,metatensor-core-v0.1.11,45.0,24.0,,,13.0,13.0,,,,,2381.0,,,,2.0,,,,,,,28583.0,metatensor/metatensor,,,,,,,,,,,,, +75,apax,,ml-iap,MIT,https://github.com/apax-hub/apax,A flexible and performant framework for training machine learning potentials.,21,True,,apax-hub/apax,https://github.com/apax-hub/apax,2022-11-18 12:31:19,2024-10-23 06:44:21.000,2024-10-22 15:22:44,1850.0,244.0,3.0,4.0,235.0,11.0,112.0,15.0,2024-09-17 10:55:44.000,0.7.0,7.0,7.0,apax,,3.0,3.0,https://pypi.org/project/apax,2024-09-17 10:55:44.000,,1508.0,1508.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +76,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 09:54:13,2024-08-30 23:52:15.000,2023-06-02 17:04:50,369.0,,2592.0,325.0,237.0,266.0,139.0,13164.0,,,,92.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +77,ATOM3D,,datasets,MIT,https://github.com/drorlab/atom3d,ATOM3D: tasks on molecules in three dimensions.,20,False,"['biomolecules', 'benchmarking']",drorlab/atom3d,https://github.com/drorlab/atom3d,2020-04-03 22:53:11,2023-03-02 18:21:02.000,2023-03-02 18:20:29,798.0,,35.0,17.0,6.0,21.0,40.0,301.0,2022-07-20 00:58:03.115,0.2.6,15.0,10.0,atom3d,,43.0,43.0,https://pypi.org/project/atom3d,2022-07-20 00:58:03.115,,3908.0,3908.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +78,FitSNAP,,md,GPL-2.0,https://github.com/FitSNAP/FitSNAP,Software for generating machine-learning interatomic potentials for LAMMPS.,20,True,,FitSNAP/FitSNAP,https://github.com/FitSNAP/FitSNAP,2019-09-12 14:46:18,2024-09-19 15:26:23.000,2024-09-19 15:26:23,1399.0,15.0,51.0,7.0,184.0,16.0,57.0,149.0,2023-06-28 16:00:48.000,3.1.0,7.0,24.0,,conda-forge/fitsnap3,3.0,3.0,,,,,189.0,https://anaconda.org/conda-forge/fitsnap3,2023-06-16 00:19:04.615,8889.0,2.0,,,,,,,13.0,,,,,,,,,,,,,, +79,DADApy,,unsupervised,Apache-2.0,https://github.com/sissa-data-science/DADApy,Distance-based Analysis of DAta-manifolds in python.,20,True,,sissa-data-science/DADApy,https://github.com/sissa-data-science/DADApy,2021-02-16 17:45:23,2024-09-24 09:05:59.000,2024-09-16 13:39:30,873.0,48.0,18.0,7.0,112.0,9.0,27.0,105.0,2024-07-02 15:52:45.000,0.3.1,6.0,20.0,dadapy,,10.0,10.0,https://pypi.org/project/dadapy,2024-07-02 15:49:35.000,,600.0,600.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +80,MAST-ML,,general-tool,MIT,https://github.com/uw-cmg/MAST-ML,MAterials Simulation Toolkit for Machine Learning (MAST-ML).,20,True,,uw-cmg/MAST-ML,https://github.com/uw-cmg/MAST-ML,2017-02-16 17:03:57,2024-10-09 01:19:32.000,2024-10-09 01:19:32,3312.0,16.0,60.0,14.0,37.0,32.0,191.0,104.0,2024-09-27 21:44:25.000,3.2.1,8.0,19.0,,,44.0,44.0,,,,,2.0,,,,2.0,,,,,,,123.0,,,,,,,,,,,,,, +81,ZnDraw,,visualization,EPL-2.0,https://github.com/zincware/ZnDraw,"A powerful tool for visualizing, modifying, and analysing atomistic systems.",20,True,"['md', 'generative', 'lang-js']",zincware/ZnDraw,https://github.com/zincware/ZnDraw,2023-04-12 15:01:21,2024-10-21 22:00:26.000,2024-10-12 09:23:06,396.0,67.0,3.0,1.0,357.0,90.0,218.0,30.0,2024-08-26 14:32:18.000,0.4.7,56.0,7.0,zndraw,,8.0,6.0,https://pypi.org/project/zndraw,2024-08-26 14:33:31.000,2.0,3570.0,3570.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +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.,19,True,"['general-ml', 'rep-learn']",naganandy/graph-based-deep-learning-literature,https://github.com/naganandy/graph-based-deep-learning-literature,2017-12-01 14:48:35,2024-10-09 14:53:41.000,2024-10-09 14:53:38,7733.0,53.0,774.0,252.0,21.0,,14.0,4781.0,,,,12.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +83,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 00:38:18.000,2023-12-04 18:03:57,189.0,,41.0,8.0,77.0,11.0,58.0,285.0,2023-06-19 20:58:01.262,3.0.3,27.0,7.0,exmol,,22.0,21.0,https://pypi.org/project/exmol,2022-06-03 18:52:10.000,1.0,2951.0,2951.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +84,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 10:19:24,2024-10-24 14:12:09.000,2024-10-24 14:12:04,243.0,27.0,20.0,11.0,263.0,9.0,10.0,243.0,2024-04-04 10:59:13.000,0.0.6,5.0,16.0,kfac-jax,,12.0,11.0,https://pypi.org/project/kfac-jax,2024-04-04 10:59:13.000,1.0,1292.0,1292.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +85,gpax,,math,MIT,https://github.com/ziatdinovmax/gpax,Gaussian Processes for Experimental Sciences.,19,True,"['probabilistic', 'active-learning']",ziatdinovmax/gpax,https://github.com/ziatdinovmax/gpax,2021-10-28 13:43:18,2024-10-21 06:29:28.000,2024-05-21 08:13:54,787.0,,25.0,7.0,69.0,8.0,32.0,205.0,2024-03-20 06:39:54.000,0.1.8,16.0,6.0,gpax,,3.0,3.0,https://pypi.org/project/gpax,2024-03-20 06:39:54.000,,1116.0,1116.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +86,kgcnn,,rep-learn,MIT,https://github.com/aimat-lab/gcnn_keras,"Graph convolutions in Keras with TensorFlow, PyTorch or Jax.",19,True,,aimat-lab/gcnn_keras,https://github.com/aimat-lab/gcnn_keras,2020-07-17 11:12:46,2024-05-06 10:08:41.000,2024-05-06 10:08:14,3099.0,,30.0,7.0,30.0,12.0,74.0,109.0,2024-02-27 12:33:28.000,4.0.1,28.0,7.0,kgcnn,,22.0,19.0,https://pypi.org/project/kgcnn,2024-02-27 12:33:28.000,3.0,2606.0,2606.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +87,MatBench Discovery,,community,MIT,https://github.com/janosh/matbench-discovery,An evaluation framework for machine learning models simulating high-throughput materials discovery.,19,True,"['datasets', 'benchmarking', 'model-repository']",janosh/matbench-discovery,https://github.com/janosh/matbench-discovery,2022-06-20 18:32:44,2024-10-19 20:49:24.000,2024-10-19 20:49:22,385.0,34.0,15.0,8.0,97.0,4.0,35.0,99.0,2024-09-11 19:00:12.000,1.3.1,10.0,9.0,matbench-discovery,,3.0,3.0,https://pypi.org/project/matbench-discovery,2024-09-11 19:00:12.000,,2104.0,2104.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +88,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 11:40:38,2024-10-24 09:40:06.000,2024-10-24 09:39:10,2390.0,71.0,24.0,9.0,314.0,39.0,240.0,81.0,2024-02-01 08:57:56.000,1.2.1,9.0,44.0,,,2.0,2.0,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +89,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 13:26:41,2024-10-24 07:35:09.000,2024-10-24 07:35:08,144.0,15.0,123.0,17.0,119.0,69.0,95.0,691.0,2024-07-06 07:05:10.000,0.2.1,3.0,17.0,,,,,,,,,647.0,,,,2.0,,,,,,,15541.0,,,,,,,,,,,,,, +90,MACE-MP,,uip,MIT,https://github.com/ACEsuit/mace-mp,Pretrained foundation models for materials chemistry.,18,True,"['ml-iap', 'pretrained', 'rep-learn', 'md']",ACEsuit/mace-mp,https://github.com/ACEsuit/mace-mp,2024-01-11 10:55:55,2024-07-16 11:55:19.000,2024-04-24 14:56:12,10.0,,187.0,11.0,1.0,1.0,9.0,497.0,2024-07-16 11:55:19.000,0.3.6,4.0,2.0,mace-torch,,14.0,,https://pypi.org/project/mace-torch,2024-07-16 11:55:19.000,14.0,14351.0,17812.0,,,,2.0,,,,,,,31156.0,,,,,,,,,,,,,, +91,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 19:06:58,2024-09-12 14:04:43.000,2024-09-12 13:43:18,298.0,2.0,69.0,17.0,150.0,14.0,98.0,336.0,2024-06-13 15:18:45.000,1.4.1,86.0,20.0,gt4sd,,,,https://pypi.org/project/gt4sd,2024-09-12 13:44:36.000,,6096.0,6096.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +92,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..,18,True,"['ml-iap', 'pretrained']",materialsvirtuallab/m3gnet,https://github.com/materialsvirtuallab/m3gnet,2022-01-18 18:10:58,2024-10-07 23:34:28.000,2024-10-04 19:40:10,263.0,2.0,61.0,11.0,37.0,15.0,20.0,237.0,2022-11-17 23:25:35.000,0.2.4,16.0,16.0,m3gnet,,32.0,27.0,https://pypi.org/project/m3gnet,2022-11-17 23:25:34.805,5.0,1868.0,1868.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +93,OpenBioML ChemNLP,,language-models,MIT,https://github.com/OpenBioML/chemnlp,ChemNLP project.,18,True,['datasets'],OpenBioML/chemnlp,https://github.com/OpenBioML/chemnlp,2023-02-13 16:20:23,2024-10-21 17:32:18.000,2024-08-19 19:00:21,372.0,41.0,46.0,3.0,285.0,111.0,140.0,148.0,2023-08-07 12:49:57.000,2023.7.1,6.0,27.0,chemnlp,,1.0,,https://pypi.org/project/chemnlp,2023-08-07 12:49:57.000,1.0,596.0,596.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +94,Chemiscope,,visualization,BSD-3-Clause,https://github.com/lab-cosmo/chemiscope,An interactive structure/property explorer for materials and molecules.,18,True,['lang-js'],lab-cosmo/chemiscope,https://github.com/lab-cosmo/chemiscope,2019-10-03 09:59:42,2024-10-22 12:44:23.000,2024-10-22 12:36:19,757.0,31.0,32.0,20.0,255.0,36.0,97.0,132.0,2024-10-15 13:11:45.000,0.8.2,21.0,24.0,,,9.0,6.0,,,,,14.0,,,,3.0,,,,,,,331.0,,,,,,chemiscope,https://www.npmjs.com/package/chemiscope,2023-03-15 15:39:26.701,3.0,9.0,,,, +95,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 03:58:42,2024-08-20 17:26:52.000,2024-01-20 09:41:36,772.0,,46.0,8.0,299.0,39.0,26.0,118.0,2022-07-27 04:40:26.000,0.6,5.0,25.0,matbench,,19.0,17.0,https://pypi.org/project/matbench,2022-07-27 04:44:21.961,2.0,728.0,728.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +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.,18,True,,Materials-Consortia/OPTIMADE,https://github.com/Materials-Consortia/OPTIMADE,2018-01-08 23:32:29,2024-08-28 09:04:20.000,2024-06-12 09:31:09,1786.0,,35.0,21.0,297.0,68.0,170.0,83.0,2024-06-10 16:32:29.000,1.2.0,9.0,21.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +97,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..,18,True,['scikit-learn'],scikit-learn-contrib/scikit-matter,https://github.com/scikit-learn-contrib/scikit-matter,2020-10-12 19:23:26,2024-10-09 12:01:55.000,2024-10-09 12:01:55,387.0,5.0,20.0,17.0,162.0,14.0,56.0,76.0,2023-08-24 17:18:49.000,0.2.0,7.0,15.0,skmatter,conda-forge/skmatter,11.0,11.0,https://pypi.org/project/skmatter,2023-08-24 17:18:19.000,,2728.0,2849.0,https://anaconda.org/conda-forge/skmatter,2023-08-24 19:08:29.551,2309.0,2.0,,,,,,,,,,,,,,,,,,,,, +98,SpheriCart,,math,MIT,https://github.com/lab-cosmo/sphericart,Multi-language library for the calculation of spherical harmonics in Cartesian coordinates.,18,True,,lab-cosmo/sphericart,https://github.com/lab-cosmo/sphericart,2023-02-04 15:15:25,2024-10-17 20:17:28.000,2024-10-17 20:14:16,382.0,19.0,11.0,5.0,117.0,23.0,18.0,72.0,2024-09-04 06:56:55.000,0.5.0,11.0,10.0,sphericart,,5.0,5.0,https://pypi.org/project/sphericart,2024-09-04 06:56:55.000,,1687.0,1691.0,,,,2.0,,,,,,,89.0,,,,,,,,,,,,,, +99,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.,18,True,['ml-dft'],materialsproject/pyrho,https://github.com/materialsproject/pyrho,2020-05-25 22:44:02,2024-10-22 22:22:08.000,2024-10-22 22:19:21,292.0,5.0,7.0,9.0,116.0,2.0,3.0,37.0,2024-10-22 22:21:31.000,0.4.5,29.0,10.0,mp-pyrho,,30.0,25.0,https://pypi.org/project/mp-pyrho,2024-10-22 22:21:31.000,5.0,17421.0,17421.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +100,OpenML,,community,BSD-3,https://github.com/openml/OpenML,Open Machine Learning.,17,True,['datasets'],openml/OpenML,https://github.com/openml/OpenML,2012-12-11 11:27:40,2024-10-20 20:16:17.000,2024-10-15 20:16:09,2321.0,4.0,90.0,48.0,204.0,367.0,560.0,665.0,,,,35.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +101,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/.,17,True,,QUVA-Lab/escnn,https://github.com/QUVA-Lab/escnn,2022-03-16 10:15:02,2024-09-18 09:29:54.000,2024-09-18 09:29:54,246.0,2.0,44.0,19.0,33.0,38.0,37.0,355.0,2023-07-17 22:58:13.120,1.0.11,16.0,10.0,escnn,,6.0,,https://pypi.org/project/escnn,2022-04-01 11:46:00.000,6.0,2070.0,2070.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +102,ChemDataExtractor,,language-models,MIT,https://github.com/mcs07/ChemDataExtractor,Automatically extract chemical information from scientific documents.,17,False,['literature-data'],mcs07/ChemDataExtractor,https://github.com/mcs07/ChemDataExtractor,2016-10-02 23:50:01,2023-07-27 18:05:13.000,2017-02-21 23:20:23,106.0,,108.0,18.0,16.0,21.0,9.0,308.0,2017-02-03 00:28:29.000,1.3.0,8.0,2.0,chemdataextractor,chemdataextractor/chemdataextractor,125.0,117.0,https://pypi.org/project/chemdataextractor,2017-02-03 00:12:36.000,8.0,1181.0,1246.0,https://anaconda.org/chemdataextractor/chemdataextractor,2023-06-16 13:17:47.249,3176.0,1.0,,,,,,,3087.0,,,,,,,,,,,,,, +103,QML,,general-tool,MIT,https://github.com/qmlcode/qml,QML: Quantum Machine Learning.,17,False,,qmlcode/qml,https://github.com/qmlcode/qml,2017-04-22 04:48:38,2024-04-12 13:38:21.000,2018-09-10 11:14:35,75.0,,81.0,23.0,101.0,31.0,19.0,199.0,2018-03-02 11:36:41.000,0.4.0,34.0,2.0,qml,,32.0,32.0,https://pypi.org/project/qml,2018-08-13 10:37:42.000,,1288.0,1288.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +104,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 20:27:28,2024-10-22 22:04:08.000,2024-10-22 22:04:08,2552.0,217.0,20.0,5.0,244.0,21.0,41.0,145.0,2023-08-31 23:59:40.000,1.0.0,2.0,12.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +105,sGDML,,ml-iap,MIT,https://github.com/stefanch/sGDML,sGDML - Reference implementation of the Symmetric Gradient Domain Machine Learning model.,17,False,,stefanch/sGDML,https://github.com/stefanch/sGDML,2018-07-11 15:20:30,2023-08-31 12:59:32.000,2023-08-31 12:57:53,205.0,,36.0,8.0,12.0,11.0,11.0,141.0,2023-08-31 12:58:49.000,1.0.2,21.0,8.0,sgdml,,11.0,10.0,https://pypi.org/project/sgdml,2023-08-31 12:59:32.000,1.0,1628.0,1628.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +106,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 07:53:45.000,2023-02-07 09:31:25,1960.0,,57.0,19.0,80.0,10.0,17.0,102.0,2020-03-27 09:27:26.000,0.6.2,27.0,22.0,catlearn,,7.0,6.0,https://pypi.org/project/catlearn,2020-03-27 09:27:26.000,1.0,1861.0,1861.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +107,MODNet,,rep-eng,MIT,https://github.com/ppdebreuck/modnet,MODNet: a framework for machine learning materials properties.,17,True,"['pretrained', 'small-data', 'transfer-learning']",ppdebreuck/modnet,https://github.com/ppdebreuck/modnet,2020-03-13 07:39:21,2024-10-24 09:06:47.000,2024-10-24 09:06:47,283.0,6.0,33.0,7.0,178.0,26.0,27.0,79.0,2024-05-07 14:09:13.000,0.4.4,21.0,10.0,,,10.0,10.0,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +108,IPSuite,,active-learning,EPL-2.0,https://github.com/zincware/IPSuite,A Python toolkit for FAIR development and deployment of machine-learned interatomic potentials.,17,True,"['ml-iap', 'md', 'workflows', 'htc', 'FAIR']",zincware/IPSuite,https://github.com/zincware/IPSuite,2023-03-01 16:34:45,2024-10-22 12:09:27.000,2024-09-19 19:38:38,447.0,15.0,10.0,3.0,208.0,68.0,65.0,18.0,2024-08-08 20:37:20.000,0.1.3,7.0,8.0,ipsuite,,7.0,7.0,https://pypi.org/project/ipsuite,2024-08-08 20:37:48.000,,396.0,396.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +109,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 19:49:19.000,2024-03-27 04:32:41,110.0,,88.0,18.0,23.0,8.0,14.0,611.0,2024-03-27 04:30:13.000,0.3.24,27.0,3.0,chemcrow,,6.0,6.0,https://pypi.org/project/chemcrow,2024-03-27 04:30:13.000,,2301.0,2301.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +110,Neural Force Field,,ml-iap,MIT,https://github.com/learningmatter-mit/NeuralForceField,Neural Network Force Field based on PyTorch.,16,True,['pretrained'],learningmatter-mit/NeuralForceField,https://github.com/learningmatter-mit/NeuralForceField,2020-10-04 15:17:41,2024-09-24 20:39:33.000,2024-09-24 20:39:32,3124.0,3.0,48.0,7.0,6.0,2.0,18.0,235.0,2024-05-29 21:15:00.000,1.0.0,1.0,41.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +111,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 18:15:19,2024-09-30 23:48:17.883,2024-08-23 21:20:06,74.0,3.0,23.0,11.0,63.0,27.0,66.0,181.0,2023-10-09 08:49:10.000,1.4,16.0,8.0,,conda-forge/openmm-torch,,,,,,,11077.0,https://anaconda.org/conda-forge/openmm-torch,2024-09-30 23:48:17.883,498480.0,2.0,,,,,,,,,,,,,,,,,,,,, +112,Orb Models,,uip,https://github.com/orbital-materials/orb-models/blob/main/LICENSE,https://github.com/orbital-materials/orb-models,ORB forcefield models from Orbital Materials.,16,True,"['ml-iap', 'pretrained']",orbital-materials/orb-models,https://github.com/orbital-materials/orb-models,2024-08-30 15:27:25,2024-10-17 21:11:39.000,2024-10-17 21:11:39,21.0,21.0,20.0,7.0,19.0,,13.0,172.0,2024-10-15 20:43:32.000,0.4.0,5.0,6.0,orb-models,,3.0,3.0,https://pypi.org/project/orb-models,2024-10-15 20:43:32.000,,1365.0,1365.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +113,Automatminer,,general-tool,https://github.com/hackingmaterials/automatminer/blob/main/LICENSE,https://github.com/hackingmaterials/automatminer,An automatic engine for predicting materials properties.,16,False,['automl'],hackingmaterials/automatminer,https://github.com/hackingmaterials/automatminer,2018-05-10 18:27:08,2023-11-12 10:09:39.000,2022-01-06 19:39:49,1666.0,,50.0,12.0,233.0,41.0,138.0,138.0,2020-07-28 02:19:07.000,1.0.3.20200727,17.0,13.0,automatminer,,9.0,9.0,https://pypi.org/project/automatminer,2020-07-28 02:23:45.000,,1975.0,1975.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +114,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 21:14:48.000,2024-04-21 06:58:38,693.0,,58.0,11.0,184.0,21.0,66.0,136.0,2023-05-21 15:54:32.000,0.6.8,54.0,10.0,xenonpy,,1.0,,https://pypi.org/project/xenonpy,2022-10-31 15:40:18.355,1.0,2462.0,2480.0,,,,3.0,,,,,,,1439.0,,,,,,,,,,,,,, +115,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..,16,True,"['ml-iap', 'md', 'pretrained']",MDIL-SNU/SevenNet,https://github.com/MDIL-SNU/SevenNet,2023-02-16 06:31:53,2024-10-24 07:22:45.000,2024-10-24 02:49:33,612.0,237.0,13.0,5.0,80.0,8.0,14.0,120.0,2024-10-22 02:02:38.000,0.10.0,7.0,12.0,,,6.0,6.0,,,,,,,,,3.0,,,,,,2.0,,,,,,,,,,,,,,, +116,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 07:01:44.000,2024-06-20 06:57:57,410.0,,12.0,5.0,127.0,,37.0,85.0,2024-06-20 07:02:24.000,2.2.0,17.0,2.0,moftransformer,,9.0,8.0,https://pypi.org/project/moftransformer,2024-06-20 07:01:44.000,1.0,1290.0,1290.0,,,,1.0,,,,,,,,,,,,,,,,,,,,, +117,MLatom,,general-tool,MIT,https://github.com/dralgroup/mlatom,AI-enhanced computational chemistry.,16,True,"['uip', 'ml-iap', 'md', 'ml-dft', 'ml-esm', 'transfer-learning', 'active-learning', 'spectroscopy', 'structure-optimization']",dralgroup/mlatom,https://github.com/dralgroup/mlatom,2023-08-16 13:47:48,2024-10-09 05:51:55.000,2024-10-09 05:51:55,68.0,16.0,10.0,3.0,23.0,2.0,3.0,54.0,2024-09-23 01:35:47.000,3.11.0,19.0,3.0,mlatom,,,,https://pypi.org/project/mlatom,2024-10-09 00:56:21.000,,3185.0,3185.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +118,load-atoms,,datasets,MIT,https://github.com/jla-gardner/load-atoms,download and manipulate atomistic datasets.,16,True,['data-structures'],jla-gardner/load-atoms,https://github.com/jla-gardner/load-atoms,2022-11-21 21:59:15,2024-10-16 10:37:59.000,2024-10-16 10:37:59,280.0,9.0,2.0,1.0,37.0,1.0,30.0,38.0,2024-10-04 11:25:06.000,0.3.2,39.0,3.0,load-atoms,,4.0,4.0,https://pypi.org/project/load-atoms,2024-10-04 11:25:06.000,,3607.0,3607.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +119,KLIFF,,ml-iap,LGPL-2.1,https://github.com/openkim/kliff,KIM-based Learning-Integrated Fitting Framework for interatomic potentials.,16,True,"['probabilistic', 'workflows']",openkim/kliff,https://github.com/openkim/kliff,2017-08-01 20:33:58,2024-10-14 00:02:59.000,2024-10-08 03:16:39,1085.0,12.0,20.0,4.0,153.0,22.0,19.0,34.0,2023-12-17 02:12:19.000,0.4.3,18.0,9.0,kliff,conda-forge/kliff,4.0,4.0,https://pypi.org/project/kliff,2023-12-17 02:12:19.000,,982.0,3290.0,https://anaconda.org/conda-forge/kliff,2024-09-10 06:39:09.645,110797.0,2.0,,,,,,,,,,,,,,,,,,,,, +120,GlassPy,,rep-eng,GPL-3.0,https://github.com/drcassar/glasspy,Python module for scientists working with glass materials.,16,True,,drcassar/glasspy,https://github.com/drcassar/glasspy,2019-07-18 23:15:43,2024-10-13 22:55:06.000,2024-10-13 21:52:07,374.0,40.0,7.0,6.0,14.0,7.0,8.0,27.0,2024-10-13 22:55:06.000,0.5.3,15.0,2.0,glasspy,,7.0,7.0,https://pypi.org/project/glasspy,2024-09-05 19:43:43.000,,1240.0,1240.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +121,Graphormer,,rep-learn,MIT,https://github.com/microsoft/Graphormer,Graphormer is a general-purpose deep learning backbone for molecular modeling.,15,True,"['transformer', 'pretrained']",microsoft/Graphormer,https://github.com/microsoft/Graphormer,2021-05-27 05:31:18,2024-06-07 17:01:35.000,2024-05-28 06:22:34,77.0,,331.0,31.0,46.0,92.0,66.0,2108.0,2024-04-03 08:23:10.000,dig-v1.0,2.0,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +122,dlpack,,data-structures,Apache-2.0,https://github.com/dmlc/dlpack,common in-memory tensor structure.,15,True,['lang-cpp'],dmlc/dlpack,https://github.com/dmlc/dlpack,2017-02-24 16:56:47,2024-09-28 19:37:33.000,2024-09-28 19:37:03,76.0,1.0,129.0,48.0,78.0,29.0,42.0,899.0,2024-09-09 15:40:21.000,1.0,10.0,24.0,,,,,,,,,,,,,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.,15,True,['transformer'],google-deepmind/ferminet,https://github.com/google-deepmind/ferminet,2020-10-06 12:21:06,2024-10-03 13:12:01.000,2024-10-03 13:11:21,243.0,14.0,125.0,34.0,30.0,1.0,52.0,731.0,,,,18.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +124,GT4SD - Generative Toolkit for Scientific Discovery,,community,MIT,https://huggingface.co/GT4SD,Gradio apps of generative models in GT4SD.,15,True,"['generative', 'pretrained', 'drug-discovery', 'model-repository']",GT4SD/gt4sd-core,https://github.com/GT4SD/gt4sd-core,2022-02-11 19:06:58,2024-09-12 14:04:43.000,2024-09-12 13:43:18,298.0,2.0,69.0,17.0,150.0,14.0,98.0,336.0,2024-06-13 15:18:45.000,1.4.1,57.0,20.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +125,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 19:16:29,2024-01-05 14:31:05.000,2024-01-03 14:28:02,67.0,,42.0,11.0,37.0,8.0,31.0,268.0,2024-01-05 14:31:05.000,0.4.1,5.0,5.0,molecule-generation,,1.0,,https://pypi.org/project/molecule-generation,2024-01-05 14:31:05.000,1.0,525.0,525.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +126,PyXtalFF,,ml-iap,MIT,https://github.com/MaterSim/PyXtal_FF,Machine Learning Interatomic Potential Predictions.,15,True,,MaterSim/PyXtal_FF,https://github.com/MaterSim/PyXtal_FF,2019-01-08 08:43:35,2024-02-15 16:12:06.000,2024-01-07 14:27:45,561.0,,23.0,9.0,4.0,12.0,51.0,85.0,2023-06-09 17:17:24.000,0.2.3,19.0,9.0,pyxtal_ff,,,,https://pypi.org/project/pyxtal_ff,2022-12-21 20:21:00.409,,698.0,698.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +127,NNPOps,,ml-iap,MIT,https://github.com/openmm/NNPOps,High-performance operations for neural network potentials.,15,True,"['md', 'lang-cpp']",openmm/NNPOps,https://github.com/openmm/NNPOps,2020-09-10 21:02:00,2024-09-11 06:01:38.318,2024-07-10 15:29:02,95.0,,18.0,8.0,63.0,22.0,35.0,83.0,2023-07-26 11:21:58.000,0.6,7.0,9.0,,conda-forge/nnpops,,,,,,,8145.0,https://anaconda.org/conda-forge/nnpops,2024-09-11 06:01:38.318,252514.0,2.0,,,,,,,,,,,,,,,,,,,,, +128,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.,15,True,"['workflows', 'htc']",libAtoms/workflow,https://github.com/libAtoms/workflow,2021-11-04 17:03:34,2024-10-24 15:44:56.000,2024-10-23 18:38:50,1202.0,85.0,18.0,9.0,189.0,67.0,93.0,32.0,2024-04-25 15:07:11.000,0.2.4,4.0,19.0,,,2.0,2.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +129,benchmarking-gnns,,rep-learn,MIT,https://github.com/graphdeeplearning/benchmarking-gnns,Repository for benchmarking graph neural networks.,14,False,"['single-paper', 'benchmarking']",graphdeeplearning/benchmarking-gnns,https://github.com/graphdeeplearning/benchmarking-gnns,2020-03-03 03:42:50,2023-06-22 04:03:53.000,2022-05-10 13:22:20,45.0,,452.0,60.0,20.0,7.0,63.0,2509.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +130,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.,14,True,['lang-fortran'],rouyang2017/SISSO,https://github.com/rouyang2017/SISSO,2017-10-16 11:31:57,2024-09-20 09:23:54.000,2024-09-20 09:23:54,199.0,33.0,78.0,6.0,3.0,18.0,58.0,239.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +131,n2p2,,ml-iap,GPL-3.0,https://github.com/CompPhysVienna/n2p2,n2p2 - A Neural Network Potential Package.,14,False,['lang-cpp'],CompPhysVienna/n2p2,https://github.com/CompPhysVienna/n2p2,2018-07-25 12:29:17,2024-10-02 15:42:34.000,2022-09-05 10:56:20,387.0,,77.0,12.0,53.0,68.0,85.0,223.0,2022-05-23 12:53:39.000,2.2.0,11.0,9.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +132,So3krates (MLFF),,ml-iap,MIT,https://github.com/thorben-frank/mlff,Build neural networks for machine learning force fields with JAX.,14,True,,thorben-frank/mlff,https://github.com/thorben-frank/mlff,2022-09-30 07:40:17,2024-10-03 09:06:35.000,2024-08-23 09:41:03,150.0,17.0,16.0,7.0,21.0,4.0,6.0,87.0,2024-06-24 11:09:20.000,0.3.0,2.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +133,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 03:32:03,2024-10-21 14:58:25.000,2024-10-15 01:02:51,699.0,24.0,27.0,10.0,248.0,17.0,32.0,65.0,2023-11-10 15:25:43.000,3.0,2.0,14.0,,,2.0,2.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +134,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.,14,True,,uf3/uf3,https://github.com/uf3/uf3,2021-10-01 13:21:44,2024-10-10 19:03:09.000,2024-10-04 15:08:06,731.0,6.0,20.0,6.0,84.0,19.0,31.0,60.0,2023-10-27 16:37:16.000,0.4.0,4.0,10.0,uf3,,2.0,2.0,https://pypi.org/project/uf3,2023-10-27 16:37:16.000,,137.0,137.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +135,aviary,,materials-discovery,MIT,https://github.com/CompRhys/aviary,The Wren sits on its Roost in the Aviary.,14,True,,CompRhys/aviary,https://github.com/CompRhys/aviary,2021-09-28 12:29:05,2024-10-16 20:16:42.000,2024-10-16 20:16:41,642.0,8.0,11.0,3.0,62.0,4.0,25.0,48.0,2024-07-22 19:03:03.000,1.0.0,5.0,4.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +136,flare++,,active-learning,MIT,https://github.com/mir-group/flare_pp,A many-body extension of the FLARE code.,14,False,"['lang-cpp', 'ml-iap']",mir-group/flare_pp,https://github.com/mir-group/flare_pp,2019-11-20 22:46:32,2022-02-27 21:05:09.000,2022-02-24 19:00:50,989.0,,7.0,7.0,29.0,8.0,17.0,35.0,2021-12-23 05:02:12.000,0.1.1,25.0,10.0,flare_pp,,2.0,,https://pypi.org/project/flare_pp,2021-12-23 05:02:12.000,2.0,1814.0,1814.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +137,Atomvision,,visualization,https://github.com/usnistgov/atomvision/blob/master/LICENSE.md,https://github.com/usnistgov/atomvision,Deep learning framework for atomistic image data.,14,False,"['computer-vision', 'experimental-data', 'rep-learn']",usnistgov/atomvision,https://github.com/usnistgov/atomvision,2021-09-16 20:33:46,2024-08-23 22:09:27.000,2023-05-08 03:21:25,123.0,,17.0,10.0,15.0,4.0,4.0,29.0,2023-05-08 03:15:44.402,2023.5.6,6.0,3.0,atomvision,,3.0,3.0,https://pypi.org/project/atomvision,2023-05-08 03:15:44.402,,1450.0,1450.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +138,BOSS,,materials-discovery,Apache-2.0,https://gitlab.com/cest-group/boss,Bayesian Optimization Structure Search (BOSS).,14,True,['probabilistic'],,,2020-02-12 08:48:33,2024-10-09 15:57:12.000,,,,11.0,,,2.0,29.0,20.0,2024-07-20 17:27:04.000,1.11.0,50.0,,aalto-boss,,,,https://pypi.org/project/aalto-boss,2024-10-09 15:57:12.000,,21115.0,21115.0,,,,1.0,,,,,,,,,,,,,,,,,,cest-group/boss,https://gitlab.com/cest-group/boss,, +139,Compositionally-Restricted Attention-Based Network (CrabNet),,rep-learn,MIT,https://github.com/sparks-baird/CrabNet,Predict materials properties using only the composition information!.,14,True,,sparks-baird/CrabNet,https://github.com/sparks-baird/CrabNet,2021-09-17 07:58:15,2024-09-09 20:13:07.000,2024-09-09 20:13:07,429.0,2.0,5.0,1.0,56.0,16.0,3.0,12.0,2023-06-07 01:07:33.000,2.0.8,37.0,6.0,crabnet,,16.0,14.0,https://pypi.org/project/crabnet,2023-01-10 04:27:02.444,2.0,3411.0,3411.0,,,,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.",13,True,,divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-09-03 06:45:42.000,2024-09-03 06:44:00,439.0,14.0,58.0,18.0,5.0,2.0,14.0,509.0,,,,29.0,,,,,,,,,,,,,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.,13,True,['ml-dft'],divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-09-03 06:45:42.000,2024-09-03 06:44:00,439.0,14.0,58.0,18.0,5.0,2.0,14.0,509.0,,,,29.0,,,,,,,,,,,,,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).,13,True,"['rep-learn', 'generative', 'ml-iap', 'md', 'ml-dft', 'ml-wft', 'biomolecules']",divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-09-03 06:45:42.000,2024-09-03 06:44:00,439.0,14.0,58.0,18.0,5.0,2.0,14.0,509.0,,,,29.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +143,QHNet,,ml-dft,GPL-3.0,https://github.com/divelab/AIRS/tree/main/OpenDFT/QHNet,Artificial Intelligence Research for Science (AIRS).,13,True,['rep-learn'],divelab/AIRS,https://github.com/divelab/AIRS,2023-02-01 17:05:09,2024-09-03 06:45:42.000,2024-09-03 06:44:00,439.0,14.0,58.0,18.0,5.0,2.0,14.0,509.0,,,,29.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +144,gptchem,,language-models,MIT,https://github.com/kjappelbaum/gptchem,Use GPT-3 to solve chemistry problems.,13,True,,kjappelbaum/gptchem,https://github.com/kjappelbaum/gptchem,2023-01-06 15:34:32,2024-05-17 19:25:11.000,2023-10-04 11:27:09,147.0,,41.0,9.0,5.0,19.0,2.0,228.0,2023-11-30 09:31:51.000,0.0.4,4.0,4.0,gptchem,,,,https://pypi.org/project/gptchem,2023-10-04 11:28:07.000,,351.0,351.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +145,DMFF,,ml-iap,LGPL-3.0,https://github.com/deepmodeling/DMFF,DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable..,13,True,,deepmodeling/DMFF,https://github.com/deepmodeling/DMFF,2022-02-14 01:35:50,2024-10-19 08:37:14.000,2024-01-12 00:58:20,431.0,,42.0,9.0,163.0,10.0,16.0,152.0,2023-11-09 14:32:37.000,1.0.0,4.0,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +146,Elementari,,visualization,MIT,https://github.com/janosh/elementari,"Interactive browser visualizations for materials science: periodic tables, 3d crystal structures, Bohr atoms, nuclei,..",13,True,['lang-js'],janosh/elementari,https://github.com/janosh/elementari,2022-06-01 15:29:36,2024-10-07 16:33:57.000,2024-10-07 00:37:04,181.0,1.0,12.0,5.0,45.0,2.0,5.0,136.0,2024-01-15 14:26:00.710,0.2.3,11.0,2.0,,,4.0,3.0,,,,,265.0,,,,3.0,,,,,,,,,,,,,elementari,https://www.npmjs.com/package/elementari,2024-01-15 14:26:00.710,1.0,265.0,,,, +147,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 17:38:31.000,2023-11-30 14:48:28,2931.0,,20.0,22.0,201.0,115.0,132.0,80.0,,,3.0,30.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +148,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 19:01:36.000,2024-08-06 19:01:36,46.0,1.0,19.0,13.0,33.0,20.0,35.0,80.0,2024-06-06 16:49:09.000,1.2,6.0,5.0,,conda-forge/openmm-ml,,,,,,,217.0,https://anaconda.org/conda-forge/openmm-ml,2024-06-07 16:52:07.157,5645.0,3.0,,,,,,,,,,,,,,,,,,,,, +149,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.,13,True,,ICAMS/python-ace,https://github.com/ICAMS/python-ace,2021-11-19 11:39:54,2024-10-10 13:48:29.000,2024-10-10 13:48:28,166.0,6.0,18.0,5.0,24.0,20.0,37.0,70.0,2022-10-24 21:50:17.233,0.2.8,2.0,6.0,python-ace,,,,https://pypi.org/project/python-ace,2022-10-24 21:50:17.233,,41.0,41.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +150,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 05:01:35.000,2024-07-01 04:57:36,72.0,,8.0,1.0,12.0,,5.0,63.0,2024-06-14 09:56:27.000,0.2.1,17.0,1.0,chatmof,,3.0,3.0,https://pypi.org/project/chatmof,2024-07-01 05:01:35.000,,1592.0,1592.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +151,SchNetPack G-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/schnetpack-gschnet,G-SchNet extension for SchNetPack.,13,True,,atomistic-machine-learning/schnetpack-gschnet,https://github.com/atomistic-machine-learning/schnetpack-gschnet,2022-04-21 12:34:13,2024-10-22 11:40:29.000,2024-10-22 11:40:28,171.0,7.0,8.0,4.0,1.0,,15.0,48.0,2024-07-03 16:43:48.000,1.1.0,3.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +152,synspace,,generative,MIT,https://github.com/whitead/synspace,Synthesis generative model.,13,False,,whitead/synspace,https://github.com/whitead/synspace,2022-12-28 00:59:14,2023-04-15 22:42:57.000,2023-04-15 18:04:16,27.0,,3.0,3.0,1.0,3.0,1.0,35.0,2023-04-15 22:48:00.713,0.3.0,3.0,2.0,synspace,,23.0,22.0,https://pypi.org/project/synspace,2023-01-16 17:29:00.461,1.0,1336.0,1336.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +153,SALTED,,ml-dft,GPL-3.0,https://github.com/andreagrisafi/SALTED,Symmetry-Adapted Learning of Three-dimensional Electron Densities.,13,True,,andreagrisafi/SALTED,https://github.com/andreagrisafi/SALTED,2020-01-22 10:24:29,2024-10-20 10:16:20.000,2024-09-27 15:31:59,716.0,17.0,4.0,3.0,46.0,1.0,5.0,30.0,2024-09-26 12:33:31.000,3.0.1,3.0,17.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +154,AtomGPT,,language-models,https://github.com/usnistgov/atomgpt/blob/main/LICENSE.rst,https://github.com/usnistgov/atomgpt,AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design.,13,True,"['generative', 'pretrained', 'transformer']",usnistgov/atomgpt,https://github.com/usnistgov/atomgpt,2023-07-17 02:20:53,2024-10-04 16:38:24.000,2024-10-04 16:38:24,113.0,18.0,4.0,3.0,9.0,2.0,,27.0,2024-09-22 05:47:21.000,2024.9.18,3.0,2.0,atomgpt,,2.0,2.0,https://pypi.org/project/atomgpt,2024-09-22 05:47:21.000,,788.0,788.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +155,Polynomials4ML.jl,,math,MIT,https://github.com/ACEsuit/Polynomials4ML.jl,"Polynomials for ML: fast evaluation, batching, differentiation.",13,True,['lang-julia'],ACEsuit/Polynomials4ML.jl,https://github.com/ACEsuit/Polynomials4ML.jl,2022-09-20 23:05:53,2024-09-12 16:22:52.000,2024-06-22 16:18:31,410.0,,5.0,4.0,41.0,17.0,34.0,12.0,2024-06-22 16:34:35.000,0.3.1,17.0,10.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +156,CCS_fit,,ml-iap,GPL-3.0,https://github.com/Teoroo-CMC/CCS,Curvature Constrained Splines.,13,True,,Teoroo-CMC/CCS,https://github.com/Teoroo-CMC/CCS,2021-12-13 14:29:53,2024-05-14 08:53:09.000,2024-02-16 09:31:25,762.0,,11.0,3.0,13.0,8.0,6.0,8.0,2024-02-16 09:31:34.000,0.22.5,100.0,8.0,ccs_fit,,,,https://pypi.org/project/ccs_fit,2024-02-16 09:31:34.000,,8998.0,9026.0,,,,2.0,,,,,,,650.0,,,,,,,,,,,,,, +157,Crystal Graph Convolutional Neural Networks (CGCNN),,rep-learn,MIT,https://github.com/txie-93/cgcnn,Crystal graph convolutional neural networks for predicting material properties.,12,False,,txie-93/cgcnn,https://github.com/txie-93/cgcnn,2018-03-14 20:41:21,2021-09-06 05:23:51.000,2021-09-06 05:23:38,25.0,,277.0,23.0,7.0,18.0,20.0,645.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +158,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 22:45:49.000,2023-05-06 22:45:49,55.0,,174.0,40.0,7.0,6.0,18.0,618.0,,,,5.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +159,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 11:06:03.000,2023-06-18 23:17:32,26.0,,41.0,10.0,5.0,3.0,6.0,467.0,2023-06-18 23:20:44.000,0.2.0,2.0,3.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +160,DeepLearningLifeSciences,,educational,MIT,https://github.com/deepchem/DeepLearningLifeSciences,Example code from the book Deep Learning for the Life Sciences.,12,False,,deepchem/DeepLearningLifeSciences,https://github.com/deepchem/DeepLearningLifeSciences,2019-02-05 17:16:18,2021-09-17 05:10:37.000,2021-09-17 05:10:37,52.0,,151.0,25.0,15.0,11.0,10.0,351.0,2019-10-28 18:46:28.000,1.0,1.0,10.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +161,TensorMol,,ml-iap,GPL-3.0,https://github.com/jparkhill/TensorMol,Tensorflow + Molecules = TensorMol.,12,False,['single-paper'],jparkhill/TensorMol,https://github.com/jparkhill/TensorMol,2016-10-28 19:40:11,2021-02-11 00:12:00.000,2018-03-30 12:26:14,1724.0,,74.0,45.0,8.0,18.0,19.0,271.0,2017-11-08 18:05:50.000,0.1,1.0,12.0,,,2.0,2.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +162,DeepH-pack,,ml-dft,LGPL-3.0,https://github.com/mzjb/DeepH-pack,Deep neural networks for density functional theory Hamiltonian.,12,True,['lang-julia'],mzjb/DeepH-pack,https://github.com/mzjb/DeepH-pack,2022-05-13 02:51:32,2024-10-07 10:24:16.000,2024-10-07 10:24:16,68.0,2.0,44.0,7.0,18.0,13.0,38.0,232.0,2023-07-11 08:13:06.000,0.2.2,2.0,8.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +163,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 21:50:26.000,2024-03-11 21:50:26,112.0,,55.0,33.0,9.0,16.0,21.0,221.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +164,PiNN,,ml-iap,BSD-3-Clause,https://github.com/Teoroo-CMC/PiNN,A Python library for building atomic neural networks.,12,True,,Teoroo-CMC/PiNN,https://github.com/Teoroo-CMC/PiNN,2019-10-04 08:13:18,2024-08-02 06:55:47.000,2024-06-27 11:23:15,160.0,,32.0,6.0,15.0,1.0,5.0,106.0,2019-10-09 09:21:30.000,0.3.0,1.0,5.0,,,,,,,,,4.0,,,,2.0,teoroo/pinn,https://hub.docker.com/r/teoroo/pinn,2024-06-27 11:32:07.538231,,246.0,,,,,,,,,,,,,,,, +165,NIST ChemNLP,,language-models,MIT,https://github.com/usnistgov/chemnlp,ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data.,12,True,['literature-data'],usnistgov/chemnlp,https://github.com/usnistgov/chemnlp,2022-08-10 11:43:44,2024-08-19 19:40:05.000,2024-08-19 19:40:04,81.0,1.0,16.0,9.0,15.0,1.0,,73.0,2023-08-07 12:49:57.000,2023.7.1,6.0,2.0,chemnlp,,5.0,4.0,https://pypi.org/project/chemnlp,2023-08-07 12:49:57.000,1.0,596.0,596.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +166,hippynn,,rep-learn,https://github.com/lanl/hippynn/blob/main/LICENSE.txt,https://github.com/lanl/hippynn,python library for atomistic machine learning.,12,True,['workflows'],lanl/hippynn,https://github.com/lanl/hippynn,2021-11-17 00:45:13,2024-10-22 17:57:29.000,2024-10-22 17:55:57,167.0,27.0,23.0,9.0,96.0,8.0,12.0,69.0,2024-01-29 22:04:53.000,hippynn-0.0.3,3.0,14.0,,,2.0,2.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +167,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 20:11:49.000,2022-06-14 22:18:28,143.0,,27.0,7.0,19.0,2.0,6.0,57.0,2022-04-27 17:05:25.000,0.3.12,13.0,4.0,,,14.0,14.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +168,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 14:28:34,2024-10-10 14:02:24.000,2024-10-10 13:55:50,575.0,16.0,13.0,7.0,265.0,32.0,37.0,44.0,,,,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +169,nlcc,,language-models,MIT,https://github.com/whitead/nlcc,Natural language computational chemistry command line interface.,12,False,['single-paper'],whitead/nlcc,https://github.com/whitead/nlcc,2021-08-19 18:23:52,2023-02-04 03:07:56.000,2023-02-04 03:06:33,144.0,,7.0,5.0,1.0,,9.0,44.0,2023-02-04 03:11:01.949,0.6.0,10.0,3.0,nlcc,,2.0,2.0,https://pypi.org/project/nlcc,2022-12-07 05:07:49.878,,681.0,681.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +170,cmlkit,,rep-eng,MIT,https://github.com/sirmarcel/cmlkit,tools for machine learning in condensed matter physics and quantum chemistry.,12,False,['benchmarking'],sirmarcel/cmlkit,https://github.com/sirmarcel/cmlkit,2018-05-31 07:56:52,2022-04-01 00:39:14.000,2022-03-25 22:27:04,526.0,,6.0,3.0,1.0,6.0,2.0,34.0,,,25.0,1.0,cmlkit,,7.0,6.0,https://pypi.org/project/cmlkit,2022-03-25 22:27:16.000,1.0,2750.0,2750.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +171,FAENet,,rep-learn,MIT,https://github.com/vict0rsch/faenet,Frame Averaging Equivariant GNN for materials modeling.,12,True,,vict0rsch/faenet,https://github.com/vict0rsch/faenet,2023-02-10 22:10:27,2023-10-12 08:46:26.000,2023-10-12 08:46:22,125.0,,2.0,3.0,5.0,,,33.0,2023-09-12 04:00:49.000,0.1.2,3.0,3.0,faenet,,3.0,3.0,https://pypi.org/project/faenet,2023-09-14 21:06:36.000,,427.0,427.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +172,CBFV,,rep-eng,,https://github.com/Kaaiian/CBFV,Tool to quickly create a composition-based feature vector.,12,False,,kaaiian/CBFV,https://github.com/Kaaiian/CBFV,2019-09-05 23:07:46,2022-03-30 05:47:53.000,2021-10-24 17:10:17,49.0,,6.0,4.0,7.0,5.0,5.0,25.0,2021-10-24 17:22:06.000,1.1.0,3.0,3.0,CBFV,,14.0,14.0,https://pypi.org/project/CBFV,2021-10-24 17:22:06.000,,536.0,536.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +173,BenchML,,rep-eng,Apache-2.0,https://github.com/capoe/benchml,ML benchmarking and pipeling framework.,12,False,['benchmarking'],capoe/benchml,https://github.com/capoe/benchml,2020-04-28 13:26:29,2023-05-24 15:13:06.000,2023-05-24 15:04:57,341.0,,6.0,5.0,9.0,3.0,10.0,15.0,2022-07-14 08:49:29.365,0.3.4,3.0,9.0,benchml,,,,https://pypi.org/project/benchml,2022-07-14 08:49:29.365,,679.0,679.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +174,ACEfit,,ml-iap,MIT,https://github.com/ACEsuit/ACEfit.jl,,12,True,['lang-julia'],ACEsuit/ACEfit.jl,https://github.com/ACEsuit/ACEfit.jl,2022-01-01 00:09:17,2024-09-14 11:29:30.000,2024-09-14 11:17:37,266.0,45.0,7.0,4.0,31.0,22.0,35.0,7.0,,,,8.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +175,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 18:02:57.000,2023-07-02 18:02:56,558.0,,115.0,16.0,92.0,28.0,130.0,612.0,,,,19.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +176,ReLeaSE,,reinforcement-learning,MIT,https://github.com/isayev/ReLeaSE,Deep Reinforcement Learning for de-novo Drug Design.,11,False,['drug-discovery'],isayev/ReLeaSE,https://github.com/isayev/ReLeaSE,2018-04-26 14:50:34,2021-12-08 19:49:36.000,2021-12-08 19:49:36,160.0,,131.0,19.0,9.0,27.0,8.0,351.0,,,,5.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +177,SPICE,,datasets,MIT,https://github.com/openmm/spice-dataset,A collection of QM data for training potential functions.,11,True,"['ml-iap', 'md']",openmm/spice-dataset,https://github.com/openmm/spice-dataset,2021-08-31 18:52:05,2024-08-19 16:56:05.000,2024-08-19 16:56:05,43.0,1.0,9.0,17.0,47.0,17.0,48.0,152.0,2024-04-15 20:17:14.000,2.0.1,8.0,1.0,,,,,,,,,9.0,,,,2.0,,,,,,,264.0,,,,,,,,,,,,,, +178,ASAP,,unsupervised,MIT,https://github.com/BingqingCheng/ASAP,ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.,11,True,,BingqingCheng/ASAP,https://github.com/BingqingCheng/ASAP,2019-08-11 12:45:14,2024-06-27 12:53:17.000,2024-06-27 12:53:00,763.0,,28.0,7.0,37.0,6.0,19.0,144.0,2023-08-30 13:54:23.000,1,1.0,6.0,,,7.0,7.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +179,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 06:35:09,2024-10-22 03:43:25.000,2024-10-22 03:43:25,218.0,10.0,12.0,3.0,22.0,1.0,1.0,85.0,2024-07-17 02:01:45.000,0.0.5,5.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +180,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 12:15:51,2024-09-11 10:37:13.000,2024-09-11 10:36:50,570.0,17.0,22.0,13.0,2.0,3.0,17.0,80.0,2019-11-24 16:21:50.000,published-version-V1,1.0,12.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +181,jarvis-tools-notebooks,,educational,NIST,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,A Google-Colab Notebook Collection for Materials Design: https://jarvis.nist.gov/.,11,True,,JARVIS-Materials-Design/jarvis-tools-notebooks,https://github.com/JARVIS-Materials-Design/jarvis-tools-notebooks,2020-06-27 20:22:02,2024-08-14 02:50:36.000,2024-08-14 02:50:35,753.0,26.0,27.0,4.0,47.0,,,64.0,,,,5.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +182,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 02:11:38.000,2023-07-16 02:08:13,759.0,,32.0,10.0,99.0,7.0,26.0,59.0,2023-07-16 02:11:38.000,1.0,3.0,14.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +183,SIMPLE-NN,,ml-iap,GPL-3.0,https://github.com/MDIL-SNU/SIMPLE-NN,SIMPLE-NN(SNU Interatomic Machine-learning PotentiaL packagE version Neural Network).,11,False,,MDIL-SNU/SIMPLE-NN,https://github.com/MDIL-SNU/SIMPLE-NN,2018-03-26 23:53:35,2022-01-27 05:04:05.000,2022-01-27 05:04:05,586.0,,19.0,12.0,91.0,4.0,26.0,47.0,2021-09-23 01:41:42.000,1.1.1,9.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +184,MLIP Arena Leaderboard,,uip,Apache-2.0,https://huggingface.co/spaces/atomind/mlip-arena,"Fair and transparent benchmark of machine-learned interatomic potentials (MLIPs), beyond basic error metrics.",11,True,"['ml-iap', 'community']",atomind-ai/mlip-arena,https://github.com/atomind-ai/mlip-arena,2024-03-24 20:36:55,2024-10-24 17:43:55.000,2024-10-24 17:43:55,143.0,84.0,1.0,1.0,8.0,4.0,3.0,32.0,2024-10-21 18:46:54.000,0.0.1a1,2.0,3.0,,,2.0,2.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +185,AGOX,,materials-discovery,GPL-3.0,https://agox.gitlab.io/agox/,AGOX is a package for global optimization of atomic system using e.g. the energy calculated from density functional..,11,True,['structure-optimization'],,,2022-03-08 09:08:13,2024-10-23 11:38:40.000,,,,5.0,,,9.0,15.0,13.0,2024-08-26 18:44:50.000,3.8.0,6.0,,agox,,,,https://pypi.org/project/agox,2024-10-23 11:38:40.000,,1637.0,1637.0,,,,2.0,,,,,,,,,,,,,,,,,,agox/agox,https://gitlab.com/agox/agox,, +186,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.",11,False,,aimat-lab/NNsForMD,https://github.com/aimat-lab/NNsForMD,2020-08-31 11:14:18,2022-11-10 13:04:49.000,2022-11-10 13:04:45,265.0,,6.0,3.0,,,,10.0,2022-04-12 15:15:00.183,2.0.0,5.0,2.0,pyNNsMD,,2.0,2.0,https://pypi.org/project/pyNNsMD,2022-04-12 15:15:00.183,,214.0,214.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +187,fplib,,rep-eng,MIT,https://github.com/Rutgers-ZRG/libfp,libfp is a library for calculating crystalline fingerprints and measuring similarities of materials.,11,True,"['lang-c', 'single-paper']",zhuligs/fplib,https://github.com/Rutgers-ZRG/libfp,2015-09-07 08:18:27,2024-10-15 19:19:54.000,2024-10-15 19:19:49,57.0,20.0,1.0,3.0,1.0,,3.0,7.0,2024-09-26 20:12:39.000,3.1.2,3.0,,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,Rutgers-ZRG/libfp,,,,,,,,,,,,, +188,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 06:21:39.000,2023-07-29 06:21:39,13.0,,161.0,18.0,8.0,34.0,29.0,965.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +189,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 05:03:36.000,2022-04-27 19:27:40,444.0,,112.0,36.0,12.0,15.0,2.0,674.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +190,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 23:50:40,2024-10-22 20:30:24.000,2023-05-08 21:16:45,38.0,,44.0,20.0,5.0,22.0,15.0,334.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +191,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 14:22:40.000,2023-04-26 14:22:40,28.0,,42.0,3.0,1.0,,11.0,266.0,,,,3.0,,,1.0,1.0,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +192,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 20:41:12.000,2024-05-02 20:41:12,232.0,,33.0,4.0,9.0,1.0,3.0,137.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +193,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 03:27:50,2024-09-28 05:44:36.000,2024-04-13 03:44:40,384.0,,35.0,14.0,46.0,6.0,14.0,100.0,,,,7.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +194,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 16:05:53.000,2024-02-13 16:05:51,419.0,,6.0,5.0,43.0,11.0,43.0,75.0,,,,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +195,PyNEP,,ml-iap,MIT,https://github.com/bigd4/PyNEP,A python interface of the machine learning potential NEP used in GPUMD.,10,True,,bigd4/PyNEP,https://github.com/bigd4/PyNEP,2022-03-21 06:27:13,2024-10-19 05:28:19.000,2024-06-01 09:06:22,80.0,,16.0,2.0,15.0,4.0,7.0,48.0,2024-10-19 05:28:19.000,1.0.0,1.0,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +196,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 17:26:24.000,2024-05-15 17:25:23,1200.0,,11.0,3.0,40.0,5.0,15.0,45.0,,,,11.0,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +197,pair_nequip,,md,MIT,https://github.com/mir-group/pair_nequip,LAMMPS pair style for NequIP.,10,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 17:06:39.000,2024-06-05 17:06:39,101.0,,12.0,9.0,8.0,11.0,20.0,41.0,2022-05-20 00:39:04.000,0.5.2,4.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +198,Atom2Vec,,rep-learn,MIT,https://github.com/idocx/Atom2Vec,Atom2Vec: a simple way to describe atoms for machine learning.,10,True,,idocx/Atom2Vec,https://github.com/idocx/Atom2Vec,2020-01-18 23:31:47,2024-02-23 21:44:03.000,2024-02-23 21:43:58,4.0,,9.0,1.0,1.0,3.0,1.0,35.0,2024-02-23 21:43:41.000,1.1.0,2.0,1.0,atom2vec,,3.0,3.0,https://pypi.org/project/atom2vec,2024-02-23 21:43:41.000,,492.0,492.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +199,NeuralXC,,ml-dft,BSD-3-Clause,https://github.com/semodi/neuralxc,Implementation of a machine learned density functional.,10,False,,semodi/neuralxc,https://github.com/semodi/neuralxc,2019-03-14 18:13:40,2024-06-17 22:55:40.000,2021-07-05 21:36:23,337.0,,10.0,5.0,10.0,5.0,5.0,33.0,,,3.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +200,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 00:09:44.000,2022-03-17 23:01:36,2371.0,,20.0,12.0,55.0,18.0,18.0,31.0,,,,24.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +201,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,..",10,True,,ICAMS/lammps-user-pace,https://github.com/ICAMS/lammps-user-pace,2021-02-25 10:04:48,2024-09-11 16:13:06.000,2023-11-27 21:28:13,59.0,,10.0,6.0,16.0,2.0,6.0,24.0,2024-09-11 16:13:06.000,.2024.9.11,8.0,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +202,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 20:54:22,2024-10-14 12:57:35.000,2023-04-12 15:04:14,33.0,,7.0,5.0,42.0,2.0,3.0,13.0,2024-02-07 16:35:47.000,0.1.0,2.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +203,calorine,,ml-iap,https://gitlab.com/materials-modeling/calorine/-/blob/master/LICENSE,https://calorine.materialsmodeling.org/,A Python package for constructing and sampling neuroevolution potential models. https://doi.org/10.21105/joss.06264.,10,True,,,,2021-04-23 16:12:56,2024-07-26 09:35:09.000,,,,4.0,,,8.0,78.0,12.0,2024-02-28 15:53:18.000,2.2.1,14.0,,calorine,,2.0,,https://pypi.org/project/calorine,2024-07-26 09:35:09.000,2.0,3244.0,3244.0,,,,3.0,,,,,,,,,,,,,,,,,,materials-modeling/calorine,https://gitlab.com/materials-modeling/calorine,, +204,GNoME Explorer,,community,Apache-2.0,https://next-gen.materialsproject.org/materials/gnome,Graph Networks for Materials Exploration Database.,9,True,"['datasets', 'materials-discovery']",google-deepmind/materials_discovery,https://github.com/google-deepmind/materials_discovery,2023-11-28 10:29:51,2024-09-04 18:54:00.000,2024-09-04 18:51:58,10.0,2.0,138.0,45.0,7.0,18.0,4.0,883.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +205,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.",9,True,"['uip', 'datasets', 'rep-learn', 'proprietary']",google-deepmind/materials_discovery,https://github.com/google-deepmind/materials_discovery,2023-11-28 10:29:51,2024-09-04 18:54:00.000,2024-09-04 18:51:58,10.0,2.0,138.0,45.0,7.0,18.0,4.0,883.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +206,SE(3)-Transformers,,rep-learn,MIT,https://github.com/FabianFuchsML/se3-transformer-public,code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503.,9,False,"['single-paper', 'transformer']",FabianFuchsML/se3-transformer-public,https://github.com/FabianFuchsML/se3-transformer-public,2020-08-31 10:36:57,2023-07-10 05:13:25.000,2021-11-18 09:11:56,63.0,,68.0,17.0,5.0,11.0,17.0,492.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +207,EDM,,generative,MIT,https://github.com/ehoogeboom/e3_diffusion_for_molecules,E(3) Equivariant Diffusion Model for Molecule Generation in 3D.,9,False,,ehoogeboom/e3_diffusion_for_molecules,https://github.com/ehoogeboom/e3_diffusion_for_molecules,2022-04-15 14:34:35,2022-07-10 17:56:18.000,2022-07-10 17:56:12,6.0,,113.0,9.0,,3.0,36.0,440.0,,,,1.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +208,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.",9,True,,tilde-lab/awesome-materials-informatics,https://github.com/tilde-lab/awesome-materials-informatics,2018-02-15 15:14:16,2024-09-18 13:34:19.000,2024-09-18 13:34:19,139.0,1.0,84.0,17.0,54.0,,8.0,378.0,2023-03-02 19:56:59.000,2023.03.02,1.0,19.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +209,DimeNet,,ml-iap,https://github.com/gasteigerjo/dimenet/blob/master/LICENSE.md,https://github.com/gasteigerjo/dimenet,"DimeNet and DimeNet++ models, as proposed in Directional Message Passing for Molecular Graphs (ICLR 2020) and Fast and..",9,True,,gasteigerjo/dimenet,https://github.com/gasteigerjo/dimenet,2020-02-14 12:40:15,2023-10-03 09:57:19.000,2023-10-03 09:57:19,103.0,,60.0,4.0,,1.0,30.0,292.0,,,,2.0,,,1.0,1.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +210,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 16:34:25.000,2023-10-16 16:33:13,7.0,,41.0,10.0,3.0,9.0,10.0,258.0,,,,5.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +211,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 16:34:25.000,2023-10-16 16:33:13,7.0,,41.0,10.0,3.0,9.0,10.0,258.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +212,SchNet,,ml-iap,MIT,https://github.com/atomistic-machine-learning/SchNet,SchNet - a deep learning architecture for quantum chemistry.,9,False,,atomistic-machine-learning/SchNet,https://github.com/atomistic-machine-learning/SchNet,2017-10-03 11:52:20,2018-09-04 08:42:35.000,2018-09-04 08:42:34,53.0,,65.0,16.0,,1.0,2.0,219.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +213,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 14:20:12.000,2023-04-26 14:20:12,36.0,,29.0,4.0,1.0,,14.0,179.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +214,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 17:03:36.000,2023-10-31 17:03:36,81.0,,9.0,6.0,8.0,2.0,4.0,103.0,2023-08-04 12:22:15.000,1.2b,5.0,4.0,,msr-ai4science/molskill,,,,,,,15.0,https://anaconda.org/msr-ai4science/molskill,2023-06-18 17:27:43.196,312.0,3.0,,,,,,,,,,,,,,,,,,,,, +215,GATGNN: Global Attention Graph Neural Network,,rep-learn,MIT,https://github.com/superlouis/GATGNN,Pytorch Repository for our work: Graph convolutional neural networks with global attention for improved materials..,9,False,,superlouis/GATGNN,https://github.com/superlouis/GATGNN,2020-06-21 03:27:36,2022-10-03 21:57:33.000,2022-10-03 21:57:33,99.0,,18.0,8.0,,3.0,3.0,71.0,2021-04-05 06:49:29.000,0.2,2.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +216,ACE.jl,,ml-iap,https://github.com/ACEsuit/ACE.jl/blob/main/license/mit.md,https://github.com/ACEsuit/ACE.jl,Parameterisation of Equivariant Properties of Particle Systems.,9,True,['lang-julia'],ACEsuit/ACE.jl,https://github.com/ACEsuit/ACE.jl,2019-11-30 16:22:51,2024-08-31 03:43:18.000,2024-08-31 03:41:21,914.0,2.0,15.0,8.0,65.0,24.0,58.0,65.0,,,,12.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +217,PROPhet,,ml-dft,GPL-3.0,https://github.com/biklooost/PROPhet,PROPhet is a code to integrate machine learning techniques with first-principles quantum chemistry approaches.,9,False,"['ml-iap', 'md', 'single-paper', 'lang-cpp']",biklooost/PROPhet,https://github.com/biklooost/PROPhet,2016-09-16 16:21:06,2018-04-19 02:09:46.000,2018-04-19 02:00:46,120.0,,26.0,14.0,6.0,9.0,7.0,63.0,2018-04-15 16:55:15.000,1.2,3.0,4.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +218,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..,9,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 08:15:34,2024-10-14 03:45:00.000,2024-10-14 03:44:53,375.0,6.0,8.0,1.0,30.0,8.0,17.0,63.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +219,DeepErwin,,ml-wft,https://github.com/mdsunivie/deeperwin/blob/master/LICENSE,https://github.com/mdsunivie/deeperwin,DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions..,9,True,,mdsunivie/deeperwin,https://github.com/mdsunivie/deeperwin,2021-06-14 15:18:32,2024-06-07 15:52:47.000,2024-06-07 15:52:33,66.0,,7.0,2.0,5.0,,11.0,49.0,2024-03-25 13:47:47.000,transferable_atomic_orbitals,6.0,7.0,deeperwin,,,,https://pypi.org/project/deeperwin,2021-12-14 11:03:19.657,,482.0,482.0,,,,3.0,,,,,,,12.0,,,,,,,,,,,,,, +220,Sketchmap,,unsupervised,GPL-3.0,https://github.com/lab-cosmo/sketchmap,Suite of programs to perform non-linear dimensionality reduction -- sketch-map in particular.,9,False,['lang-cpp'],lab-cosmo/sketchmap,https://github.com/lab-cosmo/sketchmap,2014-05-20 09:33:32,2024-09-30 15:56:54.000,2023-05-24 22:47:50,64.0,,10.0,31.0,1.0,4.0,5.0,46.0,,,,8.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +221,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.,9,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 14:49:22.000,2024-06-26 14:49:19,555.0,,25.0,10.0,25.0,1.0,7.0,43.0,,,,14.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +222,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 21:27:41,2024-10-09 14:20:36.000,2024-10-09 14:20:22,243.0,1.0,5.0,4.0,7.0,4.0,,26.0,,,,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +223,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 19:38:34.000,2023-06-20 22:30:53,249.0,,1.0,8.0,3.0,1.0,,26.0,2023-06-20 22:31:12.000,0.0.0,1.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +224,SkipAtom,,rep-eng,MIT,https://github.com/lantunes/skipatom,"Distributed representations of atoms, inspired by the Skip-gram model.",9,False,,lantunes/skipatom,https://github.com/lantunes/skipatom,2021-06-19 13:09:13,2023-07-16 19:28:39.000,2022-05-04 13:18:30,46.0,,3.0,2.0,7.0,3.0,1.0,24.0,2022-05-04 13:20:18.000,1.2.5,12.0,1.0,skipatom,conda-forge/skipatom,2.0,2.0,https://pypi.org/project/skipatom,2022-05-04 13:20:18.000,,442.0,502.0,https://anaconda.org/conda-forge/skipatom,2023-06-18 08:42:05.505,1622.0,3.0,,,,,,,,,,,,,,,,,,,,, +225,ACE1.jl,,ml-iap,https://github.com/ACEsuit/ACE1.jl/blob/main/ASL.md,https://acesuit.github.io/,Atomic Cluster Expansion for Modelling Invariant Atomic Properties.,9,True,['lang-julia'],ACEsuit/ACE1.jl,https://github.com/ACEsuit/ACE1.jl,2022-01-14 19:52:49,2024-09-11 23:05:47.000,2024-09-11 23:05:45,561.0,3.0,7.0,5.0,31.0,22.0,24.0,20.0,,,,9.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +226,Point Edge Transformer (PET),,ml-iap,MIT,https://github.com/spozdn/pet,Point Edge Transformer.,9,True,"['rep-learn', 'transformer']",spozdn/pet,https://github.com/spozdn/pet,2023-02-08 18:36:10,2024-10-17 13:23:23.000,2024-07-02 10:29:58,201.0,,5.0,4.0,12.0,5.0,,18.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +227,OPTIMADE Tutorial Exercises,,educational,MIT,https://github.com/Materials-Consortia/optimade-tutorial-exercises,Tutorial exercises for the OPTIMADE API.,9,False,['datasets'],Materials-Consortia/optimade-tutorial-exercises,https://github.com/Materials-Consortia/optimade-tutorial-exercises,2021-08-25 17:33:15,2023-09-27 08:32:31.000,2023-09-27 08:32:30,49.0,,7.0,12.0,15.0,,3.0,15.0,2023-06-12 07:47:14.000,2.0.1,5.0,6.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +228,SiMGen,,generative,MIT,https://github.com/RokasEl/simgen,Zero Shot Molecular Generation via Similarity Kernels.,9,True,['visualization'],RokasEl/simgen,https://github.com/RokasEl/simgen,2023-01-25 16:41:18,2024-06-13 15:43:18.000,2024-02-15 10:31:41,257.0,,2.0,2.0,23.0,1.0,3.0,13.0,2024-02-14 10:35:02.000,0.1.0,2.0,4.0,simgen,,2.0,2.0,https://pypi.org/project/simgen,2024-02-14 11:08:25.000,,89.0,89.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +229,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 03:11:45.000,2024-02-05 22:48:40,158.0,,1.0,4.0,35.0,1.0,6.0,10.0,2024-02-05 22:49:58.000,0.5.0,23.0,7.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +230,2DMD 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 11:30:42.000,2023-11-21 11:30:33,1118.0,,3.0,8.0,28.0,,12.0,6.0,,,,11.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +231,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 11:30:42.000,2023-11-21 11:30:33,1118.0,,3.0,8.0,28.0,,12.0,6.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +232,Asparagus,,ml-iap,MIT,https://github.com/MMunibas/Asparagus,"Program Package for Sampling, Training and Applying ML-based Potential models https://doi.org/10.48550/arXiv.2407.15175.",9,False,"['workflows', 'sampling', 'md']",MMunibas/Asparagus,https://github.com/MMunibas/Asparagus,2024-07-08 13:44:56,2024-10-20 22:21:26.000,2024-10-20 22:21:21,50.0,46.0,3.0,1.0,4.0,,,4.0,,,2.0,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +233,Meta Open Materials 2024 (OMat24) Dataset,,datasets,CC-BY-4.0,https://huggingface.co/datasets/fairchem/OMAT24,Contains over 100 million Density Functional Theory calculations focused on structural and compositional diversity.,9,True,,https://github.com/FAIR-Chem/fairchem,,,2024-09-14 01:28:48.000,,,,,,,,,,2024-09-14 01:28:48.000,1.2.0,5.0,,fairchem-core,,1.0,,https://pypi.org/project/fairchem-core,2024-09-14 01:28:48.000,1.0,2172.0,2172.0,,,,2.0,,,,,,,,,,,,,,,,,,,,, +234,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 01:19:57,2024-09-25 13:12:36.000,2024-09-25 13:12:36,126.0,2.0,57.0,29.0,13.0,,1.0,916.0,,,,12.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +235,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..",8,False,,masashitsubaki/molecularGNN_smiles,https://github.com/masashitsubaki/molecularGNN_smiles,2018-11-06 00:25:26,2020-11-28 02:04:45.000,2020-11-28 02:04:45,79.0,,76.0,6.0,,6.0,1.0,296.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +236,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 13:36:55.000,2023-03-19 13:36:55,68.0,,73.0,16.0,7.0,5.0,1.0,262.0,,,,5.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +237,EquiformerV2,,rep-learn,MIT,https://github.com/atomicarchitects/equiformer_v2,[ICLR 2024] 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 23:38:48.000,2024-07-16 05:51:23,16.0,,26.0,5.0,1.0,14.0,4.0,210.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +238,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..",8,False,,masashitsubaki/QuantumDeepField_molecule,https://github.com/masashitsubaki/QuantumDeepField_molecule,2020-11-11 01:06:09,2021-02-20 03:46:18.000,2021-02-20 03:46:09,20.0,,42.0,4.0,,1.0,3.0,203.0,,,,1.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,, +239,Equiformer,,rep-learn,MIT,https://github.com/atomicarchitects/equiformer,[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs.,8,True,['transformer'],atomicarchitects/equiformer,https://github.com/atomicarchitects/equiformer,2023-02-28 00:21:30,2024-07-27 08:42:53.000,2024-07-18 10:32:17,6.0,,37.0,5.0,2.0,6.0,9.0,202.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +240,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 02:58:25.000,2023-11-17 02:58:25,17.0,,70.0,8.0,8.0,5.0,2.0,178.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +241,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 12:05:41.000,2023-03-24 12:05:41,64.0,,25.0,7.0,,,10.0,129.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +242,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 23:49:01.000,2019-11-21 23:49:00,7.0,,25.0,10.0,2.0,4.0,,97.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +243,ANI-1 Dataset,,datasets,MIT,https://github.com/isayev/ANI1_dataset,A data set of 20 million calculated off-equilibrium conformations for organic molecules.,8,False,,isayev/ANI1_dataset,https://github.com/isayev/ANI1_dataset,2017-08-07 20:08:46,2022-08-08 15:56:17.000,2022-08-08 15:56:17,25.0,,18.0,12.0,2.0,8.0,3.0,96.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +244,MoleculeNet Leaderboard,,datasets,MIT,https://github.com/deepchem/moleculenet,,8,False,['benchmarking'],deepchem/moleculenet,https://github.com/deepchem/moleculenet,2020-02-24 18:14:05,2021-04-29 19:51:06.000,2021-04-29 19:51:06,78.0,,20.0,5.0,15.0,24.0,5.0,89.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +245,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.,8,True,,sherrylixuecheng/AI_for_Science_paper_collection,https://github.com/sherrylixuecheng/AI_for_Science_paper_collection,2024-06-28 16:20:57,2024-09-14 16:58:10.000,2024-09-14 16:58:10,79.0,15.0,6.0,2.0,10.0,,,61.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +246,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 04:10:45.000,2024-08-08 04:10:44,30.0,2.0,9.0,5.0,4.0,3.0,1.0,61.0,,,,2.0,,,12.0,12.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +247,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 08:07:35.000,2023-10-04 08:07:35,207.0,,5.0,11.0,1.0,4.0,4.0,60.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +248,HamGNN,,ml-dft,GPL-3.0,https://github.com/QuantumLab-ZY/HamGNN,An E(3) equivariant Graph Neural Network for predicting electronic Hamiltonian matrix.,8,True,"['rep-learn', 'magnetism', 'lang-c']",QuantumLab-ZY/HamGNN,https://github.com/QuantumLab-ZY/HamGNN,2023-07-14 12:20:27,2024-09-26 08:22:48.000,2024-09-26 08:22:48,70.0,10.0,15.0,5.0,1.0,25.0,6.0,58.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +249,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 18:11:52.000,2024-01-19 18:11:38,25.0,,8.0,3.0,1.0,4.0,8.0,48.0,2022-03-08 02:14:28.000,1.0,1.0,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +250,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 14:48:56,2024-08-17 08:35:27.000,2024-08-17 08:35:27,206.0,1.0,20.0,10.0,69.0,,,40.0,,,,13.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +251,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 02:08:47.000,2023-12-29 02:08:47,504.0,,17.0,5.0,88.0,4.0,9.0,40.0,,,,13.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +252,SNAP,,ml-iap,BSD-3-Clause,https://github.com/materialsvirtuallab/snap,Repository for spectral neighbor analysis potential (SNAP) model development.,8,False,,materialsvirtuallab/snap,https://github.com/materialsvirtuallab/snap,2017-06-26 21:56:00,2020-06-30 05:20:37.000,2020-06-30 05:20:37,38.0,,17.0,11.0,1.0,1.0,3.0,37.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +253,pair_allegro,,md,MIT,https://github.com/mir-group/pair_allegro,LAMMPS pair style for Allegro deep learning interatomic potentials with parallelization support.,8,True,"['ml-iap', 'rep-learn']",mir-group/pair_allegro,https://github.com/mir-group/pair_allegro,2021-08-09 17:26:51,2024-10-21 23:11:23.000,2024-06-05 17:00:50,101.0,,8.0,9.0,3.0,12.0,18.0,34.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +254,Atomistic Adversarial Attacks,,ml-iap,MIT,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,Code for performing adversarial attacks on atomistic systems using NN potentials.,8,False,['probabilistic'],learningmatter-mit/Atomistic-Adversarial-Attacks,https://github.com/learningmatter-mit/Atomistic-Adversarial-Attacks,2021-03-28 17:39:52,2022-10-03 16:19:31.000,2022-10-03 16:19:29,33.0,,7.0,5.0,1.0,,1.0,31.0,2021-07-19 18:09:36.000,1.0.1,1.0,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +255,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.,8,True,['active-learning'],lanl/alf,https://github.com/lanl/ALF,2023-01-04 23:13:24,2024-10-11 06:03:08.000,2024-08-08 16:59:10,149.0,1.0,12.0,8.0,27.0,,,29.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +256,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 12:04:35.000,2023-01-10 22:31:07,153.0,,9.0,3.0,1.0,,1.0,25.0,2023-07-18 12:04:35.000,0.1,1.0,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +257,UVVisML,,rep-learn,MIT,https://github.com/learningmatter-mit/uvvisml,Predict optical properties of molecules with machine learning.,8,False,"['optical-properties', 'single-paper', 'probabilistic']",learningmatter-mit/uvvisml,https://github.com/learningmatter-mit/uvvisml,2021-10-13 05:58:48,2023-05-26 22:35:14.000,2023-05-26 22:35:14,17.0,,8.0,4.0,1.0,,1.0,23.0,2022-02-06 18:14:14.000,0.0.2,2.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +258,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 20:56:40,2024-09-25 17:51:19.000,2024-07-27 21:36:58,602.0,1.0,3.0,4.0,4.0,4.0,4.0,19.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +259,COSMO Software Cookbook,,educational,BSD-3-Clause,https://github.com/lab-cosmo/atomistic-cookbook,A cookbook wtih recipes for atomic-scale modeling of materials and molecules.,8,True,,lab-cosmo/software-cookbook,https://github.com/lab-cosmo/atomistic-cookbook,2023-05-23 10:33:47,2024-10-16 09:32:07.000,2024-10-14 13:34:41,102.0,36.0,1.0,16.0,79.0,1.0,11.0,16.0,,,,11.0,,,,,,,,,,,,,2.0,,,,,,,,lab-cosmo/atomistic-cookbook,,,,,,,,,,,,, +260,TurboGAP,,ml-iap,https://github.com/mcaroba/turbogap/blob/master/LICENSE.md,https://github.com/mcaroba/turbogap,The TurboGAP code.,8,True,['lang-fortran'],mcaroba/turbogap,https://github.com/mcaroba/turbogap,2021-05-02 09:19:05,2024-10-17 16:14:11.000,2024-10-17 16:13:48,316.0,7.0,9.0,8.0,7.0,8.0,3.0,16.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +261,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 04:39:24.000,2023-07-11 04:39:24,39.0,,3.0,8.0,,,,11.0,2023-03-01 14:26:13.000,1.0.0,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +262,T-e3nn,,rep-learn,MIT,https://github.com/Hongyu-yu/T-e3nn,Time-reversal Euclidean neural networks based on e3nn.,8,True,['magnetism'],Hongyu-yu/T-e3nn,https://github.com/Hongyu-yu/T-e3nn,2022-11-21 14:49:45,2024-09-29 08:13:51.000,2024-09-29 08:13:51,2146.0,1.0,,2.0,,,,9.0,,,,26.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +263,optimade.science,,community,MIT,https://optimade.science,A sky-scanner Optimade browser-only GUI.,8,True,['datasets'],tilde-lab/optimade.science,https://github.com/tilde-lab/optimade.science,2019-06-08 14:10:54,2024-06-10 12:03:39.000,2024-06-10 12:03:39,247.0,,2.0,4.0,32.0,7.0,19.0,8.0,2023-03-02 20:13:25.000,2.0.0,1.0,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +264,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.,..",8,False,['material-defect'],HSE-LAMBDA/MEGNetSparse,https://github.com/HSE-LAMBDA/MEGNetSparse,2023-07-19 08:17:42,2024-10-17 14:05:03.000,2024-10-17 14:04:59,24.0,5.0,1.0,2.0,,,,1.0,2023-08-21 17:11:01.000,0.0.10,9.0,2.0,MEGNetSparse,,2.0,2.0,https://pypi.org/project/MEGNetSparse,2023-08-21 17:11:01.000,,769.0,769.0,,,,3.0,,,,,,,,,,,,,,,,,,,,, +265,Awesome-Graph-Generation,,community,,https://github.com/yuanqidu/awesome-graph-generation,A curated list of up-to-date graph generation papers and resources.,7,True,['rep-learn'],yuanqidu/awesome-graph-generation,https://github.com/yuanqidu/awesome-graph-generation,2021-08-07 05:43:46,2024-10-14 12:01:41.000,2024-10-14 12:01:35,85.0,1.0,18.0,8.0,2.0,,,283.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +266,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 18:57:39.000,2022-04-24 18:57:39,95.0,,26.0,10.0,,2.0,11.0,201.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +267,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 17:22:16.000,2020-01-07 17:22:15,10.0,,32.0,9.0,2.0,1.0,2.0,153.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +268,A Highly Opinionated List of Open-Source Materials Informatics Resources,,community,MIT,https://github.com/ncfrey/resources,A Highly Opinionated List of Open Source Materials Informatics Resources.,7,False,,ncfrey/resources,https://github.com/ncfrey/resources,2020-11-17 23:47:07,2022-02-18 13:37:51.000,2022-02-18 13:37:51,8.0,,23.0,9.0,,,,121.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +269,Awesome Neural SBI,,community,MIT,https://github.com/smsharma/awesome-neural-sbi,Community-sourced list of papers and resources on neural simulation-based inference.,7,True,['active-learning'],smsharma/awesome-neural-sbi,https://github.com/smsharma/awesome-neural-sbi,2023-01-20 19:48:13,2024-06-17 04:24:32.000,2024-06-17 04:24:27,56.0,,6.0,6.0,2.0,1.0,1.0,91.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +270,PhysNet,,ml-iap,MIT,https://github.com/MMunibas/PhysNet,Code for training PhysNet models.,7,False,['electrostatics'],MMunibas/PhysNet,https://github.com/MMunibas/PhysNet,2019-03-28 09:05:22,2022-10-16 17:45:42.000,2020-12-07 11:09:20,4.0,,26.0,9.0,1.0,5.0,,89.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +271,JAXChem,,general-tool,MIT,https://github.com/deepchem/jaxchem,JAXChem is a JAX-based deep learning library for complex and versatile chemical modeling.,7,False,,deepchem/jaxchem,https://github.com/deepchem/jaxchem,2020-05-11 18:54:41,2020-07-15 05:02:21.000,2020-07-15 04:55:41,96.0,,10.0,7.0,13.0,1.0,1.0,79.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +272,DTNN,,rep-learn,MIT,https://github.com/atomistic-machine-learning/dtnn,Deep Tensor Neural Network.,7,False,,atomistic-machine-learning/dtnn,https://github.com/atomistic-machine-learning/dtnn,2017-03-10 14:40:05,2017-07-11 08:26:15.000,2017-07-11 08:25:39,9.0,,31.0,14.0,,,3.0,76.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +273,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 11:12:18,2024-10-19 04:57:48.000,2024-10-19 04:57:33,35.0,1.0,8.0,4.0,,,,65.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +274,Cormorant,,rep-learn,https://github.com/risilab/cormorant/blob/master/LICENSE,https://github.com/risilab/cormorant,Codebase for Cormorant Neural Networks.,7,False,,risilab/cormorant,https://github.com/risilab/cormorant,2019-10-27 18:22:07,2022-05-11 12:49:05.000,2020-03-11 15:25:51,160.0,,10.0,6.0,1.0,3.0,,59.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +275,cG-SchNet,,generative,MIT,https://github.com/atomistic-machine-learning/cG-SchNet,cG-SchNet - a conditional generative neural network for 3d molecular structures.,7,False,,atomistic-machine-learning/cG-SchNet,https://github.com/atomistic-machine-learning/cG-SchNet,2021-12-02 15:35:18,2023-03-24 12:09:56.000,2023-03-24 12:09:56,28.0,,15.0,3.0,,,3.0,52.0,2022-02-21 13:36:41.000,1.0,1.0,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +276,uncertainty_benchmarking,,general-tool,,https://github.com/ulissigroup/uncertainty_benchmarking,Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions.,7,False,"['benchmarking', 'probabilistic']",ulissigroup/uncertainty_benchmarking,https://github.com/ulissigroup/uncertainty_benchmarking,2019-08-28 19:39:28,2021-06-07 23:29:39.000,2021-06-07 23:27:19,265.0,,7.0,6.0,1.0,,,40.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +277,MACE-tutorials,,educational,MIT,https://github.com/ilyes319/mace-tutorials,Another set of tutorials for the MACE interatomic potential by one of the authors.,7,True,"['ml-iap', 'rep-learn', 'md']",ilyes319/mace-tutorials,https://github.com/ilyes319/mace-tutorials,2023-09-11 18:09:18,2024-09-14 17:54:11.000,2024-07-16 12:45:42,7.0,,11.0,3.0,,1.0,,38.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +278,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 21:54:19.000,2023-07-31 16:28:09,56.0,,5.0,7.0,11.0,3.0,1.0,36.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +279,torchchem,,general-tool,MIT,https://github.com/deepchem/torchchem,An experimental repo for experimenting with PyTorch models.,7,False,,deepchem/torchchem,https://github.com/deepchem/torchchem,2020-03-07 17:06:44,2023-03-24 23:13:19.000,2020-05-01 20:12:23,49.0,,14.0,8.0,27.0,5.0,1.0,35.0,,,,5.0,,,2.0,2.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +280,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 10:47:50.000,2023-10-14 16:50:14,36.0,,3.0,1.0,1.0,11.0,7.0,31.0,2023-11-30 19:42:07.000,0.0.1,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +281,ChargE3Net,,ml-dft,MIT,https://github.com/AIforGreatGood/charge3net,Higher-order equivariant neural networks for charge density prediction in materials.,7,True,['rep-learn'],AIforGreatGood/charge3net,https://github.com/AIforGreatGood/charge3net,2023-12-16 13:54:56,2024-08-15 14:35:44.000,2024-08-15 14:35:27,12.0,4.0,9.0,5.0,1.0,3.0,3.0,31.0,,,,2.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +282,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 14:20:54.000,2024-04-26 14:20:54,175.0,,6.0,2.0,,1.0,1.0,28.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +283,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 14:40:32.000,2023-06-28 14:39:56,203.0,,2.0,,,,,27.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +284,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 17:12:29.000,2022-04-17 17:12:29,8.0,,11.0,,,,,27.0,,,,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +285,Libnxc,,ml-dft,MPL-2.0,https://github.com/semodi/libnxc,A library for using machine-learned exchange-correlation functionals for density-functional theory.,7,False,"['lang-cpp', 'lang-fortran']",semodi/libnxc,https://github.com/semodi/libnxc,2020-07-01 18:21:50,2021-09-18 14:53:52.000,2021-08-14 16:26:32,100.0,,4.0,2.0,3.0,13.0,3.0,16.0,,,2.0,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +286,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).,7,True,"['excited-states', 'general-tool']",lcmd-epfl/Q-stack,https://github.com/lcmd-epfl/Q-stack,2021-10-20 15:33:26,2024-10-23 14:28:53.000,2024-09-26 08:13:30,422.0,1.0,5.0,2.0,46.0,9.0,20.0,14.0,,,1.0,7.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +287,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 09:52:30,2024-10-22 09:18:51.000,2023-12-06 03:06:55,469.0,,8.0,8.0,210.0,5.0,1.0,12.0,,,,8.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +288,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 14:39:34.000,2024-04-15 14:39:33,233.0,,3.0,17.0,7.0,2.0,1.0,12.0,,,1.0,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +289,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.,7,True,['long-range'],RowleyGroup/MLXDM,https://github.com/RowleyGroup/MLXDM,2022-05-03 17:47:26,2024-08-15 21:32:50.000,2024-08-15 21:32:12,53.0,7.0,2.0,5.0,,,,6.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +290,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..,7,False,['datasets'],MADICES/MADICES.github.io,https://github.com/MADICES/MADICES.github.io,2021-12-26 13:27:32,2024-07-10 07:36:51.000,2024-07-10 07:35:07,217.0,,5.0,4.0,17.0,1.0,14.0,1.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +291,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 14:20:12.000,2023-09-29 10:20:31,1026.0,,,,7.0,9.0,23.0,1.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +292,AMP,,rep-eng,,https://bitbucket.org/andrewpeterson/amp/,Amp is an open-source package designed to easily bring machine-learning to atomistic calculations.,7,False,,,,,2023-01-25 17:30:41.112,,,,25.0,,,,,,2023-01-25 17:30:41.112,1.0.1,3.0,,amp-atomistics,,,,https://pypi.org/project/amp-atomistics,2023-01-25 17:30:41.112,,569.0,569.0,,,,3.0,,,,,,,,,,,,,,,,,,,,https://amp.readthedocs.io/, +293,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 08:17:06.000,2023-07-14 08:17:04,1320.0,,,3.0,,,,,2023-03-22 11:04:31.000,1.0,1.0,,,,,,,,,,0.0,,,,3.0,,,,,,,4.0,,,,,,,,,,,,,, +294,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 15:51:11.000,2024-06-07 15:51:11,30.0,,43.0,12.0,2.0,1.0,1.0,262.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +295,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 13:27:01.000,2023-04-04 13:26:27,16.0,,19.0,6.0,,9.0,7.0,74.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +296,Applied AI for Materials,,educational,,https://github.com/WardLT/applied-ai-for-materials,Course materials for Applied AI for Materials Science and Engineering.,6,False,,WardLT/applied-ai-for-materials,https://github.com/WardLT/applied-ai-for-materials,2020-10-12 19:39:06,2022-03-12 02:26:58.000,2022-03-12 02:26:41,107.0,,32.0,4.0,13.0,5.0,,59.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +297,ANI-1x Datasets,,datasets,MIT,https://github.com/aiqm/ANI1x_datasets,"The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for organic molecules.",6,False,,aiqm/ANI1x_datasets,https://github.com/aiqm/ANI1x_datasets,2019-09-17 18:19:28,2022-04-11 17:25:55.000,2022-04-11 17:25:55,12.0,,5.0,5.0,,4.0,3.0,58.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +298,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 15:37:49.000,2023-02-28 15:37:37,128.0,,8.0,1.0,,,5.0,58.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +299,COMP6 Benchmark dataset,,datasets,MIT,https://github.com/isayev/COMP6,COMP6 Benchmark dataset for ML potentials.,6,False,,isayev/COMP6,https://github.com/isayev/COMP6,2017-12-29 16:58:35,2018-07-09 23:56:35.000,2018-07-09 23:56:34,27.0,,4.0,5.0,,2.0,1.0,39.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +300,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 15:32:49.000,2023-06-06 10:09:58,19.0,,8.0,5.0,2.0,1.0,,32.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +301,charge_transfer_nnp,,rep-learn,MIT,https://github.com/pfnet-research/charge_transfer_nnp,Graph neural network potential with charge transfer.,6,False,['electrostatics'],pfnet-research/charge_transfer_nnp,https://github.com/pfnet-research/charge_transfer_nnp,2022-04-06 01:48:18,2022-04-06 01:53:35.000,2022-04-06 01:53:22,1.0,,8.0,11.0,,,,32.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +302,milad,,rep-eng,GPL-3.0,https://github.com/muhrin/milad,Moment Invariants Local Atomic Descriptor.,6,True,['generative'],muhrin/milad,https://github.com/muhrin/milad,2020-04-23 09:14:24,2024-08-20 12:50:12.000,2024-08-20 12:50:10,111.0,1.0,1.0,4.0,,,,30.0,,,,1.0,,,3.0,3.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +303,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 14:05:53.000,,,,8.0,,,24.0,6.0,26.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,ashapeev/mlip-3,https://gitlab.com/ashapeev/mlip-3,, +304,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 17:56:21.000,2022-12-31 17:56:21,14.0,,7.0,3.0,1.0,1.0,8.0,21.0,2021-08-23 18:58:52.000,0.1,1.0,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +305,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 13:03:45.000,2024-07-23 13:03:44,26.0,,13.0,3.0,10.0,2.0,5.0,19.0,2020-12-17 16:51:47.000,2020.0,1.0,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +306,EquivariantOperators.jl,,math,MIT,https://github.com/aced-differentiate/EquivariantOperators.jl,This package is deprecated. Functionalities are migrating to Porcupine.jl.,6,False,['lang-julia'],aced-differentiate/EquivariantOperators.jl,https://github.com/aced-differentiate/EquivariantOperators.jl,2021-11-29 03:36:21,2023-09-27 18:34:44.000,2023-09-27 18:34:44,62.0,,,4.0,,,,19.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +307,rxngenerator,,generative,MIT,https://github.com/tsudalab/rxngenerator,A generative model for molecular generation via multi-step chemical reactions.,6,False,,tsudalab/rxngenerator,https://github.com/tsudalab/rxngenerator,2021-06-18 07:44:53,2024-07-24 05:27:21.000,2022-08-09 07:21:05,16.0,,3.0,9.0,2.0,1.0,,12.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +308,CatGym,,reinforcement-learning,GPL,https://github.com/ulissigroup/catgym,Surface segregation using Deep Reinforcement Learning.,6,False,,ulissigroup/catgym,https://github.com/ulissigroup/catgym,2019-08-06 19:25:27,2021-08-30 17:05:36.000,2021-08-30 17:05:32,162.0,,2.0,4.0,,2.0,,11.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +309,testing-framework,,ml-iap,,https://github.com/libAtoms/testing-framework,The purpose of this repository is to aid the testing of a large number of interatomic potentials for a variety of..,6,False,['benchmarking'],libAtoms/testing-framework,https://github.com/libAtoms/testing-framework,2020-03-04 11:43:15,2022-02-10 17:23:46.000,2022-02-10 17:23:46,225.0,,7.0,16.0,10.0,5.0,3.0,11.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +310,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..",6,True,,ICAMS/TensorPotential,https://github.com/ICAMS/TensorPotential,2021-12-08 12:10:04,2024-09-12 10:19:56.000,2024-09-12 10:19:56,22.0,4.0,4.0,2.0,2.0,,,10.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +311,PANNA,,ml-iap,MIT,https://gitlab.com/PANNAdevs/panna,A package to train and validate all-to-all connected network models for BP[1] and modified-BP[2] type local atomic..,6,False,['benchmarking'],,,2018-11-09 10:47:48,2018-11-09 10:47:48.000,,,,10.0,,,,,10.0,,,2.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,PANNAdevs/panna,https://gitlab.com/PANNAdevs/panna,, +312,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).,6,True,['rep-learn'],ulissigroup/charge-density-models,https://github.com/ulissigroup/charge-density-models,2022-06-22 13:47:53,2023-11-29 15:07:42.000,2023-11-29 15:07:42,96.0,,3.0,2.0,16.0,1.0,3.0,10.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +313,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 18:06:07.000,2022-10-31 17:49:25,40.0,,1.0,4.0,,,,8.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +314,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 13:27:28.000,2024-03-19 13:27:02,107.0,,6.0,27.0,1.0,,,7.0,,,,9.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +315,SOAPxx,,rep-eng,GPL-2.0,https://github.com/capoe/soapxx,A SOAP implementation.,6,False,['lang-cpp'],capoe/soapxx,https://github.com/capoe/soapxx,2016-03-29 10:00:00,2020-03-27 13:47:44.000,2020-03-27 13:47:36,289.0,,3.0,3.0,1.0,,2.0,7.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +316,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-..,6,False,,ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python,https://github.com/ORNL/Analysis-of-Large-Scale-Molecular-Datasets-with-Python,2023-01-06 18:09:54,2024-10-24 17:52:29.000,2023-08-11 16:49:35,47.0,,6.0,6.0,14.0,2.0,,6.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +317,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 10:03:59.000,2023-10-27 09:55:17,55.0,,1.0,17.0,43.0,19.0,4.0,5.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +318,MEGAN: Multi Explanation Graph Attention Student,,xai,MIT,https://github.com/aimat-lab/graph_attention_student,Minimal implementation of graph attention student model architecture.,6,True,['rep-learn'],aimat-lab/graph_attention_student,https://github.com/aimat-lab/graph_attention_student,2022-07-28 06:22:50,2024-10-07 12:57:30.000,2024-10-07 12:57:25,104.0,17.0,1.0,3.0,1.0,1.0,2.0,5.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +319,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 15:20:25,2024-09-04 11:20:17.000,2023-05-24 09:42:00,36.0,,8.0,8.0,1.0,5.0,3.0,5.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +320,COSMO tools,,others,,https://github.com/lab-cosmo/cosmo-tools,"Scripts, jupyter nbs, and general helpful stuff from COSMO by COSMO.",6,False,,lab-cosmo/cosmo-tools,https://github.com/lab-cosmo/cosmo-tools,2018-11-06 09:40:00,2024-05-24 05:53:06.000,2024-05-24 05:53:06,63.0,,4.0,23.0,,,,4.0,,,,4.0,,,,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,True +321,KSR-DFT,,ml-dft,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 07:20:22.000,2023-03-04 07:20:18,466.0,,,1.0,,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +322,pyLODE,,rep-eng,Apache-2.0,https://github.com/ceriottm/lode,Pythonic implementation of LOng Distance Equivariants.,6,False,['electrostatics'],ceriottm/lode,https://github.com/ceriottm/lode,2022-01-19 17:01:38,2023-07-05 09:57:29.000,2023-07-05 09:57:14,241.0,,1.0,2.0,,1.0,,3.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +323,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 06:18:19.000,2023-10-05 21:21:35,162.0,,,4.0,16.0,5.0,4.0,2.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +324,Computational Autonomy for Materials Discovery (CAMD),,materials-discovery,Apache-2.0,https://github.com/ulissigroup/CAMD,Agent-based sequential learning software for materials discovery.,6,False,,ulissigroup/CAMD,https://github.com/ulissigroup/CAMD,2023-01-10 19:42:57,2023-01-10 19:49:35.000,2023-01-10 19:49:13,1336.0,,,1.0,,,,1.0,,,,17.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +325,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 18:06:26.000,2024-03-23 18:06:26,16.0,,6.0,2.0,7.0,1.0,2.0,100.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +326,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 02:15:55.000,2022-09-04 02:06:18,139.0,,13.0,9.0,29.0,2.0,1.0,84.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +327,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 17:10:52.000,2024-06-18 17:10:52,13.0,,13.0,4.0,3.0,9.0,2.0,76.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +328,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 11:09:21.000,2024-03-07 11:09:17,44.0,,20.0,3.0,7.0,1.0,,60.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +329,SchNOrb,,ml-wft,MIT,https://github.com/atomistic-machine-learning/SchNOrb,Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions.,5,False,,atomistic-machine-learning/SchNOrb,https://github.com/atomistic-machine-learning/SchNOrb,2019-09-17 12:41:48,2019-09-17 14:31:47.000,2019-09-17 14:31:19,2.0,,19.0,5.0,,1.0,,60.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +330,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 23:10:10,2024-10-22 22:29:40.000,2024-10-22 22:29:36,3.0,1.0,6.0,3.0,1.0,2.0,1.0,38.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +331,Machine Learning for Materials Hard and Soft,,educational,,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,ESI-DCAFM-TACO-VDSP Summer School on Machine Learning for Materials Hard and Soft.,5,False,,CompPhysVienna/MLSummerSchoolVienna2022,https://github.com/CompPhysVienna/MLSummerSchoolVienna2022,2022-07-01 08:42:41,2022-07-22 08:10:24.000,2022-07-22 08:10:24,49.0,,20.0,1.0,14.0,,,34.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +332,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 11:44:53.000,2023-06-14 11:44:46,4.0,,4.0,3.0,,2.0,,33.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +333,Autobahn,,rep-learn,MIT,https://github.com/risilab/Autobahn,Repository for Autobahn: Automorphism Based Graph Neural Networks.,5,False,,risilab/Autobahn,https://github.com/risilab/Autobahn,2021-03-02 01:14:40,2022-03-01 21:04:09.000,2022-03-01 21:04:04,11.0,,2.0,5.0,,,,30.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +334,SciBot,,language-models,,https://github.com/CFN-softbio/SciBot,SciBot is a simple demo of building a domain-specific chatbot for science.,5,True,['ai-agent'],CFN-softbio/SciBot,https://github.com/CFN-softbio/SciBot,2023-06-12 12:41:44,2024-09-03 15:21:15.000,2024-09-03 15:20:54,23.0,1.0,8.0,6.0,,,,28.0,,,,1.0,,,2.0,2.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +335,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 16:36:30.000,2020-09-18 16:36:30,9.0,,7.0,2.0,,1.0,1.0,23.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +336,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 21:39:34.000,2019-08-16 21:39:33,14.0,,7.0,6.0,,,,21.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +337,CSPML (crystal structure prediction with machine learning-based element substitution),,materials-discovery,MIT,https://github.com/Minoru938/CSPML,Original implementation of CSPML.,5,True,['structure-prediction'],minoru938/cspml,https://github.com/Minoru938/CSPML,2022-01-15 10:59:27,2024-09-25 07:36:12.000,2024-09-25 07:36:11,24.0,1.0,9.0,2.0,,2.0,1.0,20.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +338,NequIP-JAX,,ml-iap,,https://github.com/mariogeiger/nequip-jax,JAX implementation of the NequIP interatomic potential.,5,True,,mariogeiger/nequip-jax,https://github.com/mariogeiger/nequip-jax,2023-03-08 04:18:28,2023-11-01 20:35:48.000,2023-11-01 20:35:44,39.0,,3.0,1.0,2.0,2.0,,18.0,2023-06-22 22:36:36.000,1.1.0,3.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +339,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 09:28:38.000,2022-05-19 09:28:38,26.0,,4.0,2.0,1.0,1.0,,17.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +340,3DSC 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 09:21:11.000,2024-01-08 09:21:11,53.0,,4.0,2.0,,2.0,,15.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +341,SCFNN,,rep-learn,MIT,https://github.com/andy90/SCFNN,Self-consistent determination of long-range electrostatics in neural network potentials.,5,False,"['lang-cpp', 'electrostatics', 'single-paper']",andy90/SCFNN,https://github.com/andy90/SCFNN,2021-09-22 12:02:00,2022-01-30 02:29:03.000,2022-01-24 09:40:40,10.0,,8.0,2.0,,,,15.0,2022-01-30 02:29:04.000,1.0.0,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +342,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 01:13:22.000,2023-04-05 01:13:11,24.0,,1.0,1.0,,1.0,,13.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +343,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,False,['magnetism'],StefanoSanvitoGroup/BERT-PSIE-TC,https://github.com/StefanoSanvitoGroup/BERT-PSIE-TC,2023-01-25 10:27:26,2023-08-18 11:47:45.000,2023-08-18 12:48:31,36.0,,3.0,1.0,,,,12.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +344,SOMD,,md,AGPL-3.0,https://github.com/initqp/somd,Molecular dynamics package designed for the SIESTA DFT code.,5,True,"['ml-iap', 'active-learning']",initqp/somd,https://github.com/initqp/somd,2023-03-09 19:00:41,2024-10-22 10:57:33.000,2024-10-22 10:57:06,309.0,8.0,2.0,1.0,11.0,,1.0,12.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +345,ACEHAL,,active-learning,,https://github.com/ACEsuit/ACEHAL,Hyperactive Learning (HAL) Python interface for building Atomic Cluster Expansion potentials.,5,False,['lang-julia'],ACEsuit/ACEHAL,https://github.com/ACEsuit/ACEHAL,2023-02-24 17:33:47,2023-10-01 12:19:41.000,2023-09-21 21:50:43,121.0,,7.0,5.0,15.0,4.0,6.0,11.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +346,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 13:42:13.000,,,,3.0,,,,,11.0,,,0.0,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,pavanello-research-group/qmlearn,https://gitlab.com/pavanello-research-group/qmlearn,, +347,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', 'neural-operator']",ccr-cheng/infgcn-pytorch,https://github.com/ccr-cheng/InfGCN-pytorch,2023-10-01 21:21:40,2023-12-05 01:31:19.000,2023-12-05 01:31:14,3.0,,3.0,1.0,,,3.0,11.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +348,SciGlass,,datasets,MIT,https://github.com/drcassar/SciGlass,The database contains a vast set of data on the properties of glass materials.,5,False,,drcassar/SciGlass,https://github.com/drcassar/SciGlass,2019-06-19 19:36:32,2023-08-27 13:46:44.000,2023-08-27 13:46:44,28.0,,3.0,1.0,,,,10.0,2023-08-27 13:48:09.000,2.0.1,1.0,2.0,,,,,,,,,1.0,,,,3.0,,,,,,,22.0,,,,,,,,,,,,,, +349,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 15:56:31.000,,,,4.0,,,,,10.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,zaverkin_v/gmnn,https://gitlab.com/zaverkin_v/gmnn,, +350,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 03:16:17.000,2024-04-11 22:22:28,31.0,,2.0,1.0,7.0,,,9.0,2023-06-29 18:48:44.000,0.0.1,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +351,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 05:16:12.000,2023-11-19 05:14:44,161.0,,,,,4.0,,8.0,2023-07-16 05:46:38.000,0.1.0,1.0,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +352,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 18:08:05.000,2023-02-27 18:08:05,67.0,,3.0,1.0,1.0,,,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +353,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 12:48:49.000,2023-04-13 12:48:49,11.0,,1.0,8.0,2.0,,,5.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +354,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 20:11:27.000,,,,4.0,,,2.0,,4.0,,,0.0,,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,jmargraf/qpac,https://gitlab.com/jmargraf/qpac,, +355,cnine,,math,,https://github.com/risi-kondor/cnine,Cnine tensor library.,5,False,['lang-cpp'],risi-kondor/cnine,https://github.com/risi-kondor/cnine,2022-10-07 20:54:54,2024-10-19 06:25:51.000,2024-08-09 03:21:10,381.0,9.0,4.0,2.0,8.0,1.0,1.0,4.0,,,,6.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,https://risi-kondor.github.io/cnine/, +356,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 01:18:02.000,2023-04-13 01:18:02,3.0,,3.0,1.0,,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +357,rho_learn,,ml-dft,MIT,https://github.com/m-stack-org/rho_learn,A proof-of-concept workflow for torch-based electron density learning.,5,False,,jwa7/rho_learn,https://github.com/m-stack-org/rho_learn,2023-02-14 15:46:26,2023-04-03 07:03:02.000,2023-03-27 16:58:46,98.0,,2.0,1.0,1.0,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,m-stack-org/rho_learn,,,,,,,,,,,,, +358,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 14:20:59.000,,,,3.0,,,3.0,19.0,3.0,,,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,sissopp_developers/sissopp,https://gitlab.com/sissopp_developers/sissopp,, +359,halex,,ml-esm,,https://github.com/ecignoni/halex,Hamiltonian Learning for Excited States https://doi.org/10.48550/arXiv.2311.00844.,5,False,['excited-states'],ecignoni/halex,https://github.com/ecignoni/halex,2023-09-04 06:54:15,2024-02-08 10:20:53.000,2024-02-08 10:20:49,169.0,,,3.0,,1.0,,3.0,,,,3.0,,,,,,,,,,,,,2.0,,,,,,,,,,,,,,,,,,,,, +360,Alchemical learning,,ml-iap,BSD-3-Clause,https://github.com/Luthaf/alchemical-learning,Code for the Modeling high-entropy transition metal alloys with alchemical compression article.,5,False,,Luthaf/alchemical-learning,https://github.com/Luthaf/alchemical-learning,2021-12-02 17:02:00,2023-04-24 18:35:45.000,2023-04-07 10:19:10,120.0,,1.0,7.0,1.0,,4.0,2.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +361,linear-regression-benchmarks,,datasets,MIT,https://github.com/BingqingCheng/linear-regression-benchmarks,Data sets used for linear regression benchmarks.,5,False,"['benchmarking', 'single-paper']",BingqingCheng/linear-regression-benchmarks,https://github.com/BingqingCheng/linear-regression-benchmarks,2020-04-16 20:48:28,2022-01-26 08:29:46.000,2022-01-26 08:29:46,24.0,,,3.0,2.0,,,1.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +362,"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 12:48:17.000,2023-06-26 12:48:15,157.0,,15.0,2.0,1.0,,,1.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +363,ACE1Pack.jl,,ml-iap,MIT,https://github.com/ACEsuit/ACE1pack.jl,"Provides convenience functionality for the usage of ACE1.jl, ACEfit.jl, JuLIP.jl for fitting interatomic potentials..",5,False,['lang-julia'],ACEsuit/ACE1pack.jl,https://github.com/ACEsuit/ACE1pack.jl,2023-08-21 16:25:00,2023-08-21 16:30:19.000,2023-08-21 15:48:54,547.0,,,1.0,,,,1.0,,,,11.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,https://acesuit.github.io/ACE1pack.jl, +364,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 17:38:46.000,2023-06-05 17:38:41,28.0,,,,,,,1.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +365,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 17:35:19.000,2023-06-05 17:30:34,123.0,,1.0,,,,,1.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +366,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 16:25:54.000,2024-02-29 16:25:53,37.0,,7.0,1.0,3.0,,1.0,93.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +367,ML-in-chemistry-101,,educational,,https://github.com/BingqingCheng/ML-in-chemistry-101,The course materials for Machine Learning in Chemistry 101.,4,False,,BingqingCheng/ML-in-chemistry-101,https://github.com/BingqingCheng/ML-in-chemistry-101,2020-02-09 17:47:07,2020-10-19 08:10:31.000,2020-10-19 08:10:30,13.0,,17.0,2.0,,,,68.0,,,,1.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +368,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 09:00:23.000,,,,15.0,,,,,61.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,bigd4/magus,https://gitlab.com/bigd4/magus,, +369,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 22:25:11.000,2023-08-03 22:24:35,82.0,,1.0,1.0,,,,19.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +370,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 12:06:56.000,2024-03-20 09:00:27,11.0,,1.0,2.0,3.0,,,17.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +371,Graph Transport Network,,rep-learn,https://github.com/gasteigerjo/gtn/blob/main/LICENSE.md,https://github.com/gasteigerjo/gtn,"Graph transport network (GTN), as proposed in Scalable Optimal Transport in High Dimensions for Graph Distances,..",4,False,['transport-phenomena'],gasteigerjo/gtn,https://github.com/gasteigerjo/gtn,2021-07-11 23:36:22,2023-04-26 14:22:00.000,2023-04-26 14:22:00,9.0,,3.0,2.0,,,,16.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +372,Does this material exist?,,community,MIT,https://thismaterialdoesnotexist.com/,Vote on whether you think predicted crystal structures could be synthesised.,4,False,"['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 09:50:18.000,2024-04-10 12:32:06,16.0,,3.0,2.0,2.0,2.0,,15.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +373,ChemDataWriter,,language-models,MIT,https://github.com/ShuHuang/chemdatawriter,ChemDataWriter is a transformer-based library for automatically generating research books in the chemistry area.,4,False,['literature-data'],ShuHuang/chemdatawriter,https://github.com/ShuHuang/chemdatawriter,2023-09-22 10:05:25,2023-10-07 04:23:47.000,2023-10-07 04:07:59,9.0,,1.0,2.0,,1.0,,14.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +374,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 15:00:28,2024-09-23 13:37:50.000,2024-09-23 13:37:49,18.0,1.0,,1.0,,,,8.0,2023-10-11 14:09:07.000,1.0,1.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +375,chemrev-gpr,,educational,,https://github.com/gabor1/chemrev-gpr,Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020.,4,False,,gabor1/chemrev-gpr,https://github.com/gabor1/chemrev-gpr,2020-12-18 23:48:06,2021-05-04 19:21:34.000,2021-05-04 19:21:30,10.0,,6.0,4.0,,,,7.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +376,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 08:12:29.000,2023-06-27 08:12:29,39.0,,2.0,1.0,,,,7.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +377,Cephalo,,language-models,Apache-2.0,https://github.com/lamm-mit/Cephalo,Multimodal Vision-Language Models for Bio-Inspired Materials Analysis and Design.,4,False,"['generative', 'multimodal', 'pretrained']",lamm-mit/Cephalo,https://github.com/lamm-mit/Cephalo,2024-05-28 12:29:13,2024-07-23 09:27:58.000,2024-07-23 09:27:57,24.0,,1.0,1.0,,,,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +378,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 15:22:54.000,2022-11-15 15:22:45,6.0,,1.0,2.0,,,,5.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +379,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 09:05:20.000,2024-03-20 09:05:14,3.0,,,1.0,,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +380,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 09:54:23.000,2023-11-17 09:51:02,6.0,,1.0,1.0,,,,4.0,2023-09-29 03:52:46.000,stam_m_2023_fix,2.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +381,KmdPlus,,unsupervised,MIT,https://github.com/Minoru938/KmdPlus,"This module contains a class for treating kernel mean descriptor (KMD), and a function for generating descriptors with..",4,False,,Minoru938/KmdPlus,https://github.com/Minoru938/KmdPlus,2023-03-26 10:06:34,2024-09-25 07:36:49.000,2024-09-25 07:36:48,8.0,1.0,1.0,1.0,,,,3.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +382,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..,4,False,"['xai', 'rep-learn']",aimat-lab/visual_graph_datasets,https://github.com/aimat-lab/visual_graph_datasets,2023-06-01 11:33:18,2024-10-18 11:36:15.000,2024-10-18 11:36:09,54.0,1.0,2.0,3.0,,1.0,,2.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +383,ACEatoms,,general-tool,https://github.com/ACEsuit/ACEatoms.jl/blob/main/ASL.md,https://github.com/ACEsuit/ACEatoms.jl,Generic code for modelling atomic properties using ACE.,4,False,['lang-julia'],ACEsuit/ACEatoms.jl,https://github.com/ACEsuit/ACEatoms.jl,2021-03-23 23:50:03,2023-01-13 21:35:06.000,2023-01-13 21:28:08,134.0,,1.0,3.0,14.0,4.0,3.0,2.0,,,,10.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +384,OPTIMADE providers dashboard,,datasets,,https://www.optimade.org/providers-dashboard/,A dashboard of known providers.,4,False,,Materials-Consortia/providers-dashboard,https://github.com/Materials-Consortia/providers-dashboard,2020-06-17 16:15:07,2024-10-24 06:37:43.000,2024-10-01 19:09:06,140.0,2.0,3.0,19.0,142.0,10.0,18.0,1.0,,,,7.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +385,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 16:32:18.000,2024-04-24 16:32:18,40.0,,,2.0,,,,1.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +386,MALADA,,ml-dft,BSD-3-Clause,https://github.com/mala-project/malada,MALA Data Acquisition: Helpful tools to build data for MALA.,4,False,,mala-project/malada,https://github.com/mala-project/malada,2021-07-26 05:46:08,2024-10-24 17:46:44.000,2024-10-24 17:46:39,125.0,11.0,1.0,2.0,5.0,17.0,2.0,1.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +387,magnetism-prediction,,rep-eng,Apache-2.0,https://github.com/dppant/magnetism-prediction,DFT-aided Machine Learning Search for Magnetism in Fe-based Bimetallic Chalcogenides.,4,False,"['magnetism', 'single-paper']",dppant/magnetism-prediction,https://github.com/dppant/magnetism-prediction,2022-09-13 03:58:10,2023-07-19 13:25:49.000,2023-07-19 13:25:49,46.0,,1.0,3.0,,,,1.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +388,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 18:01:00.000,2023-10-12 18:00:39,45.0,,1.0,3.0,7.0,1.0,,,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +389,gprep,,ml-dft,MIT,https://gitlab.com/jmargraf/gprep,Fitting DFTB repulsive potentials with GPR.,4,False,['single-paper'],,,2019-09-30 09:15:04,2019-09-30 09:15:04.000,,,,0.0,,,,,,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,jmargraf/gprep,https://gitlab.com/jmargraf/gprep,, +390,closed-loop-acceleration-benchmarks,,materials-discovery,MIT,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,Data and scripts in support of the publication By how much can closed-loop frameworks accelerate computational..,4,False,"['materials-discovery', 'active-learning', 'single-paper']",aced-differentiate/closed-loop-acceleration-benchmarks,https://github.com/aced-differentiate/closed-loop-acceleration-benchmarks,2022-11-10 20:22:30,2023-07-25 21:25:42.000,2023-05-02 17:07:48,17.0,,1.0,4.0,3.0,,,,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +391,PeriodicPotentials,,ml-iap,MIT,https://github.com/AaltoRSE/PeriodicPotentials,A Periodic table app that displays potentials based on the selected elements.,4,False,"['community', 'visualization', 'lang-js']",AaltoRSE/PeriodicPotentials,https://github.com/AaltoRSE/PeriodicPotentials,2022-10-14 09:03:59,2022-10-18 17:10:22.000,2022-10-18 17:10:22,17.0,,1.0,3.0,3.0,,,,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +392,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 02:59:22.000,2024-03-08 02:59:22,93.0,,2.0,1.0,2.0,,,20.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +393,SPINNER,,materials-discovery,GPL-3.0,https://github.com/MDIL-SNU/SPINNER,SPINNER (Structure Prediction of Inorganic crystals using Neural Network potentials with Evolutionary and Random..,3,False,"['lang-cpp', 'structure-prediction']",MDIL-SNU/SPINNER,https://github.com/MDIL-SNU/SPINNER,2021-07-15 02:10:58,2024-07-20 05:12:50.000,2021-11-25 07:58:15,102.0,,2.0,1.0,,1.0,,12.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +394,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 13:51:57.000,2024-03-17 13:51:52,2.0,,3.0,1.0,,1.0,,9.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +395,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 15:59:08.000,2023-10-19 15:35:49,74.0,,,2.0,,,,8.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +396,DeepCDP,,ml-dft,,https://github.com/siddarthachar/deepcdp,DeepCDP: Deep learning Charge Density Prediction.,3,False,,siddarthachar/deepcdp,https://github.com/siddarthachar/deepcdp,2021-12-18 14:26:56,2023-06-16 20:38:23.000,2023-06-16 20:38:23,96.0,,2.0,2.0,27.0,,,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +397,Element encoder,,rep-learn,GPL-3.0,https://github.com/jeherr/element-encoder,Autoencoder neural network to compress properties of atomic species into a vector representation.,3,False,['single-paper'],jeherr/element-encoder,https://github.com/jeherr/element-encoder,2019-03-27 17:11:30,2020-01-09 15:54:27.000,2020-01-09 15:54:26,8.0,,2.0,4.0,,,1.0,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +398,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 12:48:51.000,,,,6.0,,,4.0,1.0,5.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,ivannovikov/interface-lammps-mlip-3,https://gitlab.com/ivannovikov/interface-lammps-mlip-3,, +399,sl_discovery,,materials-discovery,Apache-2.0,https://github.com/CitrineInformatics-ERD-public/sl_discovery,Data processing and models related to Quantifying the performance of machine learning models in materials discovery.,3,False,"['materials-discovery', 'single-paper']",CitrineInformatics-ERD-public/sl_discovery,https://github.com/CitrineInformatics-ERD-public/sl_discovery,2022-10-24 18:10:14,2022-12-20 23:46:05.000,2022-12-20 23:45:57,5.0,,2.0,2.0,,,,5.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +400,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 03:43:39.000,2023-09-28 03:16:11,11.0,,,2.0,,,,4.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +401,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 12:59:56.000,2024-01-05 12:59:09,19.0,,,2.0,,,,3.0,,,,,,,2.0,2.0,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +402,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 05:33:01.000,2024-03-28 05:33:01,77.0,,2.0,18.0,35.0,7.0,2.0,3.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +403,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 15:59:56.000,2023-05-01 15:59:22,9.0,,1.0,3.0,1.0,,,2.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +404,CSNN,,ml-dft,BSD-3-Clause,https://github.com/foxjas/CSNN,Primary codebase of CSNN - Concentric Spherical Neural Network for 3D Representation Learning.,3,False,,foxjas/CSNN,https://github.com/foxjas/CSNN,2022-05-19 15:40:49,2022-10-11 04:27:40.000,2022-10-11 04:27:40,6.0,,,1.0,,,,2.0,,,,3.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +405,Linear vs blackbox,,xai,MIT,https://github.com/CitrineInformatics-ERD-public/linear-vs-blackbox,Code and data related to the publication: Interpretable models for extrapolation in scientific machine learning.,3,False,"['xai', 'single-paper', 'rep-eng']",CitrineInformatics-ERD-public/linear-vs-blackbox,https://github.com/CitrineInformatics-ERD-public/linear-vs-blackbox,2022-12-02 20:32:53,2022-12-16 18:48:12.000,2022-12-16 18:48:12,4.0,,,1.0,,,,2.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +406,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 19:05:50.000,2023-06-14 19:05:47,25.0,,,1.0,,,,1.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +407,Magpie,,general-tool,MIT,https://bitbucket.org/wolverton/magpie/,Materials Agnostic Platform for Informatics and Exploration (Magpie).,3,False,['lang-java'],,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +408,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 09:16:07.000,,,,4.0,,,,,,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,flame-code/PyFLAME,https://gitlab.com/flame-code/PyFLAME,, +409,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 19:43:01.000,,,,4.0,,,1.0,1.0,13.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,brucefan1983/nep-data,https://gitlab.com/brucefan1983/nep-data,, +410,MLDensity_tutorial,,educational,,https://github.com/bfocassio/MLDensity_tutorial,Tutorial files to work with ML for the charge density in molecules and solids.,2,False,,bfocassio/MLDensity_tutorial,https://github.com/bfocassio/MLDensity_tutorial,2023-01-31 10:33:23,2023-02-22 19:20:32.000,2023-02-22 19:20:32,8.0,,1.0,1.0,,,,9.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +411,SingleNN,,ml-iap,,https://github.com/lmj1029123/SingleNN,An efficient package for training and executing neural-network interatomic potentials.,2,False,['lang-cpp'],lmj1029123/SingleNN,https://github.com/lmj1029123/SingleNN,2020-03-11 18:36:16,2021-11-09 00:40:18.000,2021-11-09 00:40:10,17.0,,1.0,1.0,,1.0,,8.0,,,,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +412,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 17:10:39.000,2021-12-02 17:10:34,4.0,,1.0,2.0,,,,8.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +413,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 22:01:26.000,2024-04-09 22:01:26,17.0,,1.0,3.0,,,2.0,6.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +414,LAMMPS-style pair potentials with GAP,,educational,,https://github.com/victorprincipe/pair_potentials,A tutorial on how to create LAMMPS-style pair potentials and use them in combination with GAP potentials to run MD..,2,False,"['ml-iap', 'md', 'rep-eng']",victorprincipe/pair_potentials,https://github.com/victorprincipe/pair_potentials,2022-09-21 09:45:03,2022-10-03 08:06:22.000,2022-10-03 08:05:53,36.0,,,1.0,1.0,,,4.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +415,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 06:02:47.000,2022-10-11 05:58:06,2.0,,,1.0,,,,3.0,,,,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +416,MALA Tutorial,,educational,,https://github.com/mala-project/mala_tutorial,A full MALA hands-on tutorial.,2,False,,mala-project/mala_tutorial,https://github.com/mala-project/mala_tutorial,2023-03-09 14:01:54,2023-11-28 11:20:39.000,2023-11-28 11:17:01,24.0,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +417,quantum-structure-ml,,general-tool,,https://github.com/hgheiberger/quantum-structure-ml,Multi-class classification model for predicting the magnetic order of magnetic structures and a binary classification..,2,False,"['magnetism', 'benchmarking']",hgheiberger/quantum-structure-ml,https://github.com/hgheiberger/quantum-structure-ml,2020-10-05 01:11:01,2022-12-22 21:45:40.000,2022-12-22 21:45:40,19.0,,,2.0,,,,2.0,2022-08-18 05:25:24.000,1.0.0,1.0,4.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +418,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/, +419,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,,,,,,,,,,,,,,,,,,,,, +420,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 12:08:14.000,2023-12-19 12:08:14,11.0,,,1.0,,,,6.0,2023-12-19 12:02:35.000,1.0,1.0,2.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +421,SphericalNet,,rep-learn,,https://github.com/risilab/SphericalNet,Implementation of Clebsch-Gordan Networks (CGnet: https://arxiv.org/pdf/1806.09231.pdf) by GElib & cnine libraries in..,1,False,,risilab/SphericalNet,https://github.com/risilab/SphericalNet,2022-05-31 14:39:05,2022-06-07 03:57:10.000,2022-06-07 03:53:49,1.0,,,2.0,,,,3.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +422,Wigner Kernels,,math,,https://github.com/lab-cosmo/wigner_kernels,Collection of programs to benchmark Wigner kernels.,1,False,['benchmarking'],lab-cosmo/wigner_kernels,https://github.com/lab-cosmo/wigner_kernels,2022-12-08 12:28:26,2023-07-08 15:48:41.000,2023-07-08 15:48:37,109.0,,,1.0,,,1.0,2.0,,,,5.0,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +423,kdft,,ml-dft,,https://gitlab.com/jmargraf/kdf,The Kernel Density Functional (KDF) code allows generating ML based DFT functionals.,1,False,,,,2020-11-07 21:50:22,2020-11-07 21:50:22.000,,,,0.0,,,,,2.0,,,0.0,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,jmargraf/kdf,https://gitlab.com/jmargraf/kdf,, +424,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 22:34:27.000,2023-12-26 22:34:27,7.0,,,1.0,,,,2.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +425,mlp,,ml-iap,,https://github.com/cesmix-mit/MLP,Proper orthogonal descriptors for efficient and accurate interatomic potentials...,1,False,['lang-julia'],cesmix-mit/mlp,https://github.com/cesmix-mit/MLP,2022-02-25 23:03:09,2022-10-22 19:01:45.000,2022-10-22 19:01:42,12.0,,1.0,2.0,,,,1.0,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +426,GitHub topic materials-informatics,,community,,https://github.com/topics/materials-informatics,GitHub topic materials-informatics.,1,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +427,MateriApps,,community,,https://ma.issp.u-tokyo.ac.jp/en/,A Portal Site of Materials Science Simulation.,1,False,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3.0,,,,,,,,,,,,,,,,,,,,, +428,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 18:44:30.000,2024-04-09 18:44:30,7.0,,2.0,2.0,1.0,1.0,1.0,18.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +429,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,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +430,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 12:28:57.000,2023-02-22 19:25:51,14.0,,,2.0,,,,2.0,,,,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, diff --git a/latest-changes.md b/latest-changes.md index 1b737a9..c0a7ed2 100644 --- a/latest-changes.md +++ b/latest-changes.md @@ -2,24 +2,19 @@ _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._ -- DeepChem (🥇36 · ⭐ 5.5K · 📈) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. MIT -- paper-qa (🥇31 · ⭐ 6.2K · 📈) - High accuracy RAG for answering questions from scientific documents with citations. Apache-2 ai-agent -- FAIR Chemistry datasets (🥇25 · ⭐ 810 · 📈) - Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project. MIT catalysis -- fairchem (🥇25 · ⭐ 810 · 📈) - FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project. MIT pretrained UIP rep-learn catalysis -- PyNEP (🥉10 · ⭐ 48 · 📈) - A python interface of the machine learning potential NEP used in GPUMD. MIT +- OPTIMADE Python tools (🥇27 · ⭐ 69 · 📈) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT +- Crystal Toolkit (🥇25 · ⭐ 150 · 📈) - Crystal Toolkit is a framework for building web apps for materials science and is currently powering the new Materials.. MIT +- GPUMD (🥇22 · ⭐ 460 · 📈) - GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials.. GPL-3.0 MD C++ electrostatics +- DeepQMC (🥇22 · ⭐ 350 · 📈) - Deep learning quantum Monte Carlo for electrons in real space. MIT +- SevenNet (🥉16 · ⭐ 120 · 📈) - SevenNet (Scalable EquiVariance Enabled Neural Network) is a graph neural network interatomic potential package that.. GPL-3.0 ML-IAP MD pretrained ## 📉 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._ -- Deep Graph Library (DGL) (🥇38 · ⭐ 13K · 📉) - Python package built to ease deep learning on graph, on top of existing DL frameworks. Apache-2 -- OPTIMADE Python tools (🥇26 · ⭐ 68 · 📉) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT -- NequIP-JAX (🥉4 · ⭐ 18 · 💤) - JAX implementation of the NequIP interatomic potential. Unlicensed - -## ➕ Added Projects - -_Projects that were recently added to this best-of list._ - -- MLIP Arena Leaderboard (🥉10 · ⭐ 28 · ➕) - Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics. Apache-2 ML-IAP community-resource -- Meta Open Materials 2024 (OMat24) Dataset (🥈9 · ➕) - Contains over 100 million Density Functional Theory calculations focused on structural and compositional diversity. CC-BY-4.0 +- DeepChem (🥇35 · ⭐ 5.5K · 📉) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology. MIT +- FAIR Chemistry datasets (🥇24 · ⭐ 830 · 📉) - Datasets OC20, OC22, etc. Formerly known as Open Catalyst Project. MIT catalysis +- fairchem (🥇24 · ⭐ 830 · 📉) - FAIR Chemistrys library of machine learning methods for chemistry. Formerly known as Open Catalyst Project. MIT pretrained UIP rep-learn catalysis +- NequIP (🥇22 · ⭐ 620 · 📉) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT +- ALIGNN (🥈21 · ⭐ 230 · 📉) - Atomistic Line Graph Neural Network https://scholar.google.com/citations?user=9Q-tNnwAAAAJ&hl=en. Custom