Update: 2024.03.17

📈 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.

• cdk (🥇24 · ⭐ 460 · 📈) - The Chemistry Development Kit. LGPL-2.1 cheminformatics Java
• AI for Science Resources (🥈14 · ⭐ 360 · 📈) - List of resources for AI4Science research, including learning resources. GPL-3.0 license
• QH9: A Quantum Hamiltonian Prediction Benchmark (🥈14 · ⭐ 360 · 📈) - Artificial Intelligence Research for Science (AIRS). CC-BY-NC-SA 4.0 ML-DFT
• Artificial Intelligence for Science (AIRS) (🥉14 · ⭐ 360 · 📈) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 license rep-learn generative ML-IAP MD ML-DFT ML-WFT biomolecules
• QHNet (🥈14 · ⭐ 360 · 📈) - Artificial Intelligence Research for Science (AIRS). GPL-3.0 rep-learn

📉 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.

• TorchMD-NET (🥇22 · ⭐ 270 · 📉) - Neural network potentials. MIT MD rep-learn transformer pre-trained
• DIG: Dive into Graphs (🥈21 · ⭐ 1.7K · 📉) - A library for graph deep learning research. GPL-3.0
• mlcolvar (🥈16 · ⭐ 74 · 📉) - A unified framework for machine learning collective variables for enhanced sampling simulations. MIT enhanced-sampling

➕ Added Projects

Projects that were recently added to this best-of list.

• pymatviz (🥉17 · ⭐ 78 · ➕) - A toolkit for visualizations in materials informatics. MIT general-tool probabilistic
• FAENet (🥈11 · ⭐ 21 · ➕) - MIT
• GNoME Explorer (🥉7 · ⭐ 500 · 🐣) - Graph Networks for Materials Exploration Database. Apache-2 datasets materials-discovery
• Materials Discovery: GNoME (🥈6 · ⭐ 500 · 🐣) - Apache-2 r e p - l e a r n , d a t a s e t s
• halex (🥉2 · ⭐ 1 · 🐣) - Hamiltonian Learning for Excited States https://doi.org/10.48550/arXiv.2311.00844. Unlicensed ML-WFT excited-states
• TorchMD-NET (🥈20 · ⭐ 220 · ➕) - Neural network potentials based on graph neural networks and equivariant transformers. MIT ML-IAP rep-learn tranformer pre-trained
• LLM-Prop (🥉8 · ⭐ 4 · ➕) - A repository for the LLM-Prop implementation. None found
• MLXDM (🥉7 · ⭐ 4 · 💤) - A Neural Network Potential with Rigorous Treatment of Long-Range Dispersion https://doi.org/10.1039/D2DD00150K. MIT long-range
• paper-data-redundancy (🥉7 · ⭐ 3 · 🐣) - Codes and data for the paper On the redundancy in large material datasets: efficient and robust learning with less data. BSD-3 small-data single-paper
• paper-ml-robustness-material-property (🥉4 · ⭐ 3 · 💤) - BSD-3 d a t a s e t s , s i n g l e - p a p e r
• Materials Data Facility (MDF) (🥈9 · ⭐ 10 · 💤) - A simple way to publish, discover, and access materials datasets. Publication of very large datasets supported (e.g.,.. Apache-2
• OPTIMADE Python tools (🥇25 · ⭐ 54 · ➕) - Tools for implementing and consuming OPTIMADE APIs in Python. MIT
• OPTIMADE Tutorial Exercises (🥈8 · ⭐ 11 · ➕) - Tutorial exercises for the OPTIMADE API. MIT datasets
• optimade.science (🥉8 · ⭐ 8 · ➕) - A sky-scanner Optimade browser-only GUI. MIT datasets
• Does this material exist? (🥉4 · ⭐ 2 · ➕) - Vote on whether you think predicted crystal structures could be synthesised. MIT for-fun materials-discovery
• OPTIMADE providers dashboard (🥉4 · ⭐ 1 · ➕) - A dashboard of known providers. Unlicensed
• GPUMD (🥇20 · ⭐ 300 · ➕) - GPUMD is a highly efficient general-purpose molecular dynamic (MD) package and enables machine-learned potentials.. GPL-3.0 MD C++ electrostatics
• nep-data (🥉1 · ⭐ 9 · 💀) - Data related to the NEP machine-learned potential of GPUMD. Unlicensed ML-IAP MD transport-phenomena

v2023.12.25

Test whether Zenodo latest release DOI 10.5281/zenodo.10430261 is working. That DOI is used in the README DOI badge.

v2023.12.21

Release for Zenodo DOI.

Update: 2023.12.03-21.16

➕ Added Projects

Projects that were recently added to this best-of list.

• Open Databases Integration for Materials Design (OPTIMADE) (🥈17 · ⭐ 59 · ➕) - Specification of a common REST API for access to materials databases. CC-BY-4.0
• MODNet (🥇17 · ⭐ 53 · ➕) - MODNet: a framework for machine learning materials properties. MIT pre-trained small-data transfer-learning
• Metatensor (🥉15 · ⭐ 25 · ➕) - Storage format for equivariant atomistic machine learning. BSD-3
• mlcolvar (🥈16 · ⭐ 59 · ➕) - A unified framework for machine learning collective variables for enhanced sampling simulations. MIT enhanced-sampling
• JAX-DFT (🥇25 · ⭐ 32K · ➕) - Google Research. Apache-2
• DIG: Dive into Graphs (🥈21 · ⭐ 1.7K · ➕) - A library for graph deep learning research. GPL-3.0
• ATOM3D (🥇18 · ⭐ 280 · 💤) - ATOM3D: tasks on molecules in three dimensions. MIT biomolecules benchmarking
• ChemCrow (🥇17 · ⭐ 320 · 🐣) - Chemcrow. MIT
• ChemDataExtractor (🥈16 · ⭐ 270 · 💀) - Automatically extract chemical information from scientific documents. MIT literature-data
• ChemNLP project (🥈16 · ⭐ 110 · ➕) - ChemNLP project. MIT datasets
• GT4SD - Generative Toolkit for Scientific Discovery (🥈15 · ⭐ 280 · ➕) - Gradio apps of generative models in GT4SD. MIT generative pre-trained drug-discovery
• dlpack (🥉14 · ⭐ 800 · 💤) - common in-memory tensor structure. Apache-2 C++
• Geometric GNN Dojo (🥇12 · ⭐ 350 · ➕) - New to geometric GNNs: try our practical notebook, prepared for MPhil students at the University of Cambridge. MIT rep-learn
• QH9: A Quantum Hamiltonian Prediction Benchmark (🥈12 · ⭐ 280 · ➕) - Artificial Intelligence for Science (AIRS). CC-BY-NC-SA 4.0 ML-DFT
• QHNet (🥈12 · ⭐ 280 · ➕) - Artificial Intelligence for Science (AIRS). GPL-3.0 rep-learn
• Grad DFT (🥈12 · ⭐ 43 · ➕) - Grad-DFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation.. Apache-2
• pretrained-gnns (🥇10 · ⭐ 870 · ➕) - Strategies for Pre-training Graph Neural Networks. MIT pre-trained
• DSECOP (🥈10 · ⭐ 31 · ➕) - This repository contains data science educational materials developed by DSECOP Fellows. CCO-1.0
• pair_nequip (🥉10 · ⭐ 29 · 💀) - LAMMPS pair style for NequIP. MIT ML-IAP rep-learn
• tinker-hp (🥉9 · ⭐ 69 · ➕) - Tinker-HP: High-Performance Massively Parallel Evolution of Tinker on CPUs & GPUs. Custom
• lie-nn (🥈9 · ⭐ 22 · ➕) - Tools for building equivariant polynomials on reductive Lie groups. MIT rep-learn
• TurboGAP (🥉9 · ⭐ 14 · ➕) - The TurboGAP code. Custom Fortran
• MoLFormers UI (🥉8 · ⭐ 140 · ➕) - Repository for MolFormer. Apache-2 transformer Language models pre-trained drug-discovery
• MoLFormer (🥉8 · ⭐ 140 · ➕) - Repository for MolFormer. Apache-2 transformer pre-trained drug-discovery
• pair_allegro (🥉8 · ⭐ 26 · ➕) - LAMMPS pair style for Allegro deep learning interatomic potentials with parallelization support. MIT ML-IAP rep-learn
• chemlift (🥉8 · ⭐ 10 · 🐣) - Language-interfaced fine-tuning for chemistry. MIT
• T-e3nn (🥉8 · ⭐ 6 · 💤) - Time-reversal Euclidean neural networks based on e3nn. MIT magnetism
• Awesome Neural Geometry (🥉7 · ⭐ 780 · ➕) - A curated collection of resources and research related to the geometry of representations in the brain, deep networks,.. Unlicensed educational rep-learn
• COATI (🥉6 · ⭐ 59 · 🐣) - COATI: multi-modal contrastive pre-training for representing and traversing chemical space. Apache-2 drug-discovery pre-trained rep-learn
• Mat2Spec (🥉6 · ⭐ 24 · 💀) - MIT spectroscopy
• NequIP-JAX (🥉5 · ⭐ 10 · ➕) - JAX implementation of the NequIP interatomic potential. Unlicensed
• MAPI_LLM (🥉5 · ⭐ 4 · ➕) - A LLM application developed during the LLM March MADNESS Hackathon https://doi.org/10.1039/D3DD00113J. MIT dataset
• soap_turbo (🥉5 · ⭐ 4 · 💤) - soap_turbo comprises a series of libraries to be used in combination with QUIP/GAP and TurboGAP. Custom Fortran
• MACE-tutorials (🥉5 · ⭐ 3 · 🐣) - Another set of tutorials for the MACE interatomic potential by one of the authors. MIT ML-IAP rep-learn MD
• Point Edge Transformer (PET) (🥉5 · ➕) -...

Update: 2023.08.25-14.45

➕ Added Projects

Projects that were recently added to this best-of list.

• MLDensity_tutorial (🥉1 · ⭐ 3 · 🐣) - Tutorial files to work with ML for the charge density in molecules and solids. ❗Unlicensed
• KFAC-JAX (🥇26 · ⭐ 10K · ➕) - Open source code for AlphaFold. Apache-2
• AlphaFold (🥇24 · ⭐ 10K · ➕) - Open source code for AlphaFold. Apache-2
• DM21 (🥇21 · ⭐ 12K · ➕) - This package provides a PySCF interface to the DM21 (DeepMind 21) family of exchange-correlation functionals described.. Apache-2
• DeepQMC (🥇20 · ⭐ 280 · ➕) - Deep learning quantum Monte Carlo for electrons in real space. MIT
• FermiNet (🥈15 · ⭐ 550 · ➕) - An implementation of the Fermionic Neural Network for ab-initio electronic structure calculations. Apache-2
• GElib (🥉9 · ⭐ 15 · ➕) - C++/CUDA library for SO(3) equivariant operations. MPL-2.0
• Cormorant (🥉6 · ⭐ 51 · 💀) - Codebase for Cormorant Neural Networks. Unlicensed
• Autobahn (🥉5 · ⭐ 26 · 💀) - Repository for Autobahn: Automorphism Based Graph Neural Networks. MIT
• SphericalNet ( ⭐ 2 · 💤) - Implementation of Clebsch-Gordan Networks (CGnet: https://arxiv.org/pdf/1806.09231.pdf) by GElib & cnine libraries in.. Unlicensed
• cnine (➕) - Unlicensed
• chemrev-gpr (🥉4 · ⭐ 5 · 💀) - Notebooks accompanying the paper on GPR in materials and molecules in Chemical Reviews 2020. Unlicensed
• paper-qa (🥇23 · ⭐ 2.6K · 🐣) - LLM Chain for answering questions from documents with citations. Apache-2
• Best-of Machine Learning with Python (🥇22 · ⭐ 14K · ➕) - A ranked list of awesome machine learning Python libraries. Updated weekly. CC-BY-4.0 general-ml Python
• Graph-based Deep Learning Literature (🥈18 · ⭐ 4.3K · ➕) - links to conference publications in graph-based deep learning. MIT general-ml rep-learn
• Awesome Materials Informatics (🥉11 · ⭐ 290 · ➕) - Curated list of known efforts in materials informatics = modern materials science. Custom t o p i c s / m a t e r i a l s - i n f o r m a t i c s
• The Collection of Database and Dataset Resources in Materials Science (🥉8 · ⭐ 160 · ➕) - A list of databases, datasets and books/handbooks where you can find materials properties for machine learning.. Unlicensed datasets
• A Highly Opinionated List of Open-Source Materials Informatics Resources (🥉7 · ⭐ 93 · 💀) - A Highly Opinionated List of Open Source Materials Informatics Resources. MIT
• GitHub topic materials-informatics (➕) - Unlicensed
• cdk (🥇25 · ⭐ 430 · ➕) - The Chemistry Development Kit. LGPL-2.1 cheminformatics Java
• MPContribs (🥇23 · ⭐ 32 · ➕) - Platform for materials scientists to contribute and disseminate their materials data through Materials Project. MIT
• Open Catalyst datasets (🥇18 · ⭐ 450 · ➕) - The datasets of the Open Catalyst project, OC20, OC22. CC-BY-4.0
• GT4SD (🥇18 · ⭐ 230 · ➕) - GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process. MIT pre-trained drug-discovery rep-learn
• CHGNet (🥈18 · ⭐ 79 · 🐣) - Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov. Custom MD pre-trained electrostatics magnetism structure-relaxation
• escnn (🥈17 · ⭐ 200 · ➕) - Equivariant Steerable CNNs Library for Pytorch https://quva-lab.github.io/escnn/. Custom
• MatBench (🥈16 · ⭐ 77 · ➕) - Matbench: Benchmarks for materials science property prediction. MIT datasets benchmarking
• NNPOps (🥈15 · ⭐ 61 · ➕) - High-performance operations for neural network potentials. MIT MD C++
• AI for Science Resources (🥉14 · ⭐ 220 · 🐣) - List of resources for AI4Science research, including learning resources. GPL-3.0 license
• Artificial Intelligence for Science (AIRS) (🥉14 · ⭐ 220 · 🐣) - Artificial Intelligence for Science (AIRS). GPL-3.0 license rep-learn generative MLIAP MD ML-DFT ML-WFT biomolecules
• openmm-torch (🥈14 · ⭐ 130 · ➕) - OpenMM plugin to define forces with neural networks. Custom MLIAP C++
• SPICE (🥈14 · ⭐ 89 · ➕) - A collection of QM data for training potential functions. MIT MLIAP MD
• mp-pyrho (🥉14 · ⭐ 27 · ➕) - Custom ML-DFT
• GlassPy (🥈13 · ⭐ 14 · ➕) - Python module for scientists working with glass materials. GPL-3.0
• mat2vec (🥈12 · ⭐ 590 · ➕) - Supplementary Materials for Tshitoyan et al. Unsupervised word embeddings capture latent knowledge from ...

Update: 2023.06.12-20.27

➕ Added Projects

Projects that were recently added to this best-of list.

• Deep Graph Library (DGL) (🥇37 · ⭐ 12K · ➕) - Python package built to ease deep learning on graph,.. Apache-2
• DeepChem (🥇36 · ⭐ 4.4K · ➕) - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry,.. MIT
• RDKit (🥇30 · ⭐ 2.1K · ➕) - BSD-3
• DeePMD-kit (🥇28 · ⭐ 1.1K · ➕) - A deep learning package for many-body potential.. ❗️LGPL-3.0
• Matminer (🥇27 · ⭐ 390 · ➕) - Data mining for materials science. ❗Unlicensed
• SchNetPack (🥇25 · ⭐ 600 · ➕) - SchNetPack - Deep Neural Networks for Atomistic Systems. ❗Unlicensed
• DScribe (🥇25 · ⭐ 320 · ➕) - DScribe is a python package for creating machine learning.. Apache-2
• QUIP (🥈25 · ⭐ 290 · ➕) - libAtoms/QUIP molecular dynamics framework:.. ❗Unlicensed
• paper-qa (🥇24 · ⭐ 2.6K · 🐣) - LLM Chain for answering questions from documents with citations. Apache-2
• e3nn (🥇23 · ⭐ 680 · ➕) - A modular framework for neural networks with Euclidean symmetry. ❗Unlicensed
• dgl-lifesci (🥇23 · ⭐ 580 · ➕) - Python package for graph neural networks in chemistry and.. Apache-2
• MEGNet (🥇22 · ⭐ 450 · ➕) - Graph Networks as a Universal Machine Learning Framework for.. BSD-3
• DP-GEN (🥇22 · ⭐ 220 · ➕) - The deep potential generator to generate a deep-learning.. ❗️LGPL-3.0
• dpdata (🥇22 · ⭐ 130 · ➕) - Manipulating multiple atomic simulation data formats, including.. ❗️LGPL-3.0
• kgcnn (🥈22 · ⭐ 75 · ➕) - Graph convolution with tf.keras. MIT
• NVIDIA Deep Learning Examples for Tensor Cores (🥇21 · ⭐ 11K · ➕) - State-of-the-Art Deep Learning scripts organized by.. ❗Unlicensed
• TorchANI (🥇21 · ⭐ 390 · ➕) - Accurate Neural Network Potential on PyTorch. MIT
• MAML (🥈21 · ⭐ 240 · ➕) - Python for Materials Machine Learning, Materials Descriptors, Machine.. BSD-3
• NequIP (🥇20 · ⭐ 390 · ➕) - NequIP is a code for building E(3)-equivariant interatomic potentials. MIT
• JARVIS-Tools (🥈20 · ⭐ 220 · ➕) - JARVIS-Tools: an open-source software package for.. ❗Unlicensed
• ocp (🥈19 · ⭐ 410 · ➕) - ocp is the Open Catalyst Projects library of state-of-the-art machine.. MIT
• exmol (🥇19 · ⭐ 240 · ➕) - Explainer for black box models that predict molecule properties. MIT
• FitSNAP (🥈19 · ⭐ 100 · ➕) - Software for generating SNAP machine-learning interatomic.. ❗️GPL-2.0
• FLARE (🥈18 · ⭐ 220 · ➕) - An open-source Python package for creating fast and accurate interatomic.. MIT
• e3nn-jax (🥈18 · ⭐ 110 · ➕) - jax library for E3 Equivariant Neural Networks. Apache-2
• MatGL (Materials Graph Library) (🥈18 · ⭐ 70 · ➕) - Graph deep learning library for materials. BSD-3
• Scikit-Matter (🥈18 · ⭐ 58 · ➕) - BSD-3 scikit-learn
• MALA (🥇18 · ⭐ 33 · ➕) - Materials Learning Algorithms. A framework for machine learning materials.. BSD-3
• M3GNet (🥈17 · ⭐ 160 · ➕) - Materials graph network with 3-body interactions featuring a DFT.. BSD-3
• XenonPy (🥈17 · ⭐ 110 · ➕) - XenonPy is a Python Software for Materials Informatics. BSD-3
• Chemiscope (🥇17 · ⭐ 86 · ➕) - BSD-3
• MAST-ML (🥈17 · ⭐ 82 · ➕) - MAterials Simulation Toolkit for Machine Learning (MAST-ML). MIT
• DADApy (🥇17 · ⭐ 63 · ➕) - Distance-based Analysis of DAta-manifolds in python. Apache-2
• Uni-Fold (🥇16 · ⭐ 260 · ➕) - An open-source platform for developing protein models beyond.. Apache-2
• QML (🥉16 · ⭐ 180 · 💀) - QML: Quantum Machine Learning. MIT
• ALIGNN (🥈16 · ⭐ 130 · ➕) - Atomistic Line Graph Neural Network. ❗Unlicensed
• sGDML (🥈16 · ⭐ 110 · ➕) - sGDML - Reference implementation of the Symmetric Gradient Domain.. MIT
• CatLearn (🥇16 · ⭐ 86 · ➕) - ❗️GPL-3.0
• benchmarking-gnns (🥈15 · ⭐ 2.2K · 💀) - Repository for benchmarking graph neural networks. MIT
• Uni-Mol (🥈15 · ⭐ 340 · ➕) - Official Repository for the Uni-Mol Series Methods. MIT
• MoLeR (🥇15 · ⭐ 180 · ➕) - Implementation of MoLeR: a generative model of molecular graphs which.. MIT
• Librascal (🥇15 · ⭐ 68 · ➕) - A scalable and versatile library to generate representations.. ❗️LGPL-2.1
• SpheriCart (🥇15 · ⭐ 34 · 🐣) - Multi-language library for the calculation of spherical.. Apache-2
• KLIFF (🥈15 · ⭐ 26 · ➕) - KIM-based Learning-Integrated Fitting Framework (KLIFF). ❗️LGPL-2.1
• CCS_fit (🥈15 · ⭐ 5 · ➕) - Curvature Constrained Splines. ❗️GPL-3.0
• n2p2 (🥈14 · ⭐ 180 · 💤) - n2p2 - A Neural Network Potential Package. ❗️GPL-3.0
• <a href="https://github.c...