Update: 2025.04.07

📈 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.

• pymatviz (🥇23 · ⭐ 200 · 📈) - A toolkit for visualizations in materials informatics. MIT general-tool probabilistic
• exmol (🥇22 · ⭐ 330 · 📈) - Explainer for black box models that predict molecule properties. MIT
• TorchSim (🥉13 · ⭐ 140 · 🐣) - Torch-native, batchable, atomistic simulation. MIT HTC UIP ML-IAP structure-optimization
• DeepErwin (🥉9 · ⭐ 54 · 📈) - DeepErwin is a python 3.8+ package that implements and optimizes JAX 2.x wave function models for numerical solutions.. Custom

📉 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.

• FLARE (🥇18 · ⭐ 310 · 📉) - An open-source Python package for creating fast and accurate interatomic potentials. MIT C++ ML-IAP
• SpheriCart (🥇18 · ⭐ 83 · 📉) - Multi-language library for the calculation of spherical harmonics in Cartesian coordinates. MIT
• gptchem (🥈12 · ⭐ 250 · 💀) - Use GPT-3 to solve chemistry problems. MIT
• PiNN (🥈12 · ⭐ 110 · 📉) - A Python library for building atomic neural networks. BSD-3
• BenchML (🥉11 · ⭐ 15 · 💀) - ML benchmarking and pipeling framework. Apache-2 benchmarking

➕ Added Projects

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

• SMACT (🥇27 · ⭐ 110 · ➕) - Python package to aid materials design and informatics. MIT HTC structure-prediction electrostatics
• MACE-FOUNDATION models (🥉19 · ⭐ 640 · ➕) - MACE-MP models. MIT ML-IAP pretrained rep-learn MD
• AtomAI (🥈19 · ⭐ 210 · 💀) - Deep and Machine Learning for Microscopy. MIT computer-vision USL experimental-data
• ElementEmbeddings (🥈16 · ⭐ 41 · ➕) - Python package to interact with high-dimensional representations of the chemical elements. MIT XAI USL viz
• pySIPFENN (🥈16 · ⭐ 22 · ➕) - Python python toolset for Structure-Informed Property and Feature Engineering with Neural Networks. It offers unique.. LGPL-3.0 material-defect Disordered Materials pretrained transfer-learning
• ChemML (🥈15 · ⭐ 160 · ➕) - ChemML is a machine learning and informatics program suite for the chemical and materials sciences. BSD-3 cheminformatics active-learning workflows
• SLICES and MatterGPT (🥇15 · ⭐ 100 · ➕) - SLICES: An Invertible, Invariant, and String-based Crystal Representation [2023, Nature Communications] MatterGPT,.. LGPL-2.1 rep-eng language-models transformer materials-discovery structure-prediction
• xtal2png (🥈14 · ⭐ 37 · 💀) - Encode/decode a crystal structure to/from a grayscale PNG image for direct use with image-based machine learning.. MIT computer-vision
• Bgolearn (🥈13 · ⭐ 91 · ➕) - [Materials & Design 2024] A Bayesian global optimization package for material design Adaptive Learning | Active.. MIT materials-discovery probabilistic
• mat_discover (🥈13 · ⭐ 41 · 💤) - A materials discovery algorithm geared towards exploring high-performance candidates in new chemical spaces. MIT materials-discovery rep-eng HTC
• Awesome-Scientific-Language-Models (🥈11 · ⭐ 560 · ➕) - A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery (EMNLP24). MIT language-models general-ml pretrained multimodal
• nablaDFT (🥈11 · ⭐ 200 · ➕) - nablaDFT: Large-Scale Conformational Energy and Hamiltonian Prediction benchmark and dataset. MIT ML-DFT ML-WFT drug-discovery ML-IAP benchmarking
• PDynA (🥉11 · ⭐ 41 · ➕) - Python package to analyse the structural dynamics of perovskites. MIT MD
• pumml (🥈11 · ⭐ 37 · 💀) - Positive and Unlabeled Materials Machine Learning (pumml) is a code that uses semi-supervised machine learning to.. MIT materials-discovery
• MPDS API (🥈11 · ⭐ 27 · ➕) - Tutorials, notebooks, issue tracker, and website on the MPDS API: the data retrieval interface for the Materials.. CC-BY-4.0 community-resource literature-data
• MLforMaterials (🥉7 · ⭐ 79 · ➕) - Online resource for a practical course in machine learning for materials research at Imperial College London.. MIT community-resource general-ml rep-eng materials-discovery
• molecular-vae (🥉7 · ⭐ 65 · 💀) - Pytorch implementation of the paper Automatic Chemical Design Using a Data-Driven Continuous Representation of.. MIT rep-learn cheminformatics single-paper
• PolyGNN (🥉7 · ⭐ 38 · ➕) - polyGNN is a Python library to automate ML model training for polymer informatics. MIT soft-matter multitask single-paper
• BPNET (🥉7 · ⭐ 2 · 🐣) - Behler-Parrinello type neural networks in Fortran2008. MIT rep-eng Fortran
• Geom3D (🥉6 · ⭐ 120 · 💀) - Geom3D: Geometric Modeling on 3D Structures, NeurIPS 2023. MIT benchmarking single-paper
• LLM4Chem (🥉6 · ⭐ 81 · 💤) - Official code repo for the paper LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale,.. MIT cheminformatics datasets
• Graph-Aware-Transformers (🥉6 · ⭐ 56 · 🐣) - Graph-Aware Attention for Adaptive Dynamics in Transformers. Apache-2 transformer graph-data pretrained single-paper
• ffonons (🥉6 · ⭐ 18 · ➕) - Phonons from ML force fields. MIT benchmarking density-of-states
• polyVERSE (🥉6 · ⭐ 17 · ➕) - polyVERSE is a comprehensive repository of informatics-ready datasets curated by the Ramprasad Group. Custom soft-matter
• Crystalformer (🥉5 · ⭐ 17 · ➕) - The official code respository for Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding (ICLR.. MIT transformer single-paper
• thermo (🥇5 · ⭐ 16 · 💤) - Data-driven risk-conscious thermoelectric materials discovery. MIT materials-discovery experimental-data active-learning transport-phenomena
• MUSE (🥉5 · ⭐ 4 · ➕) - A python package for fast building amorphous solids and liquid mixtures from @materialsproject computed structures and.. MIT ML-IAP Disordered Materials
• 3D-EMGP (🥉4 · ⭐ 34 · 💤) - [AAAI 2023] The implementation for the paper Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs. MIT pretrained rep-learn single-paper
• Crystalframer (🥉4 · ⭐ 5 · 🐣) - The official code respository for Rethinking the role of frames for SE(3)-invariant crystal structure modeling (ICLR.. MIT transformer single-paper