Find open-source science resources
A directory of tools, AI models, datasets, and research resources for biotech, bioinformatics, and other scientific fields. Aggregated from curated GitHub awesome-lists, HuggingFace, bio.tools, Bioconductor, and more.
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16 of 7,083 resources
Descriptor computation(chemistry) and (optional) storage for machine learning.
A collection of object-oriented software tools for problems involving chemical kinetics, thermodynamics, and transport processes.
Python Library for Automating Molecular Simulation: input preparation, job execution, file management, output processing and building data workflows.
PyTorch toolkit for deep neural networks in atomistic simulations, implementing SchNet, DimeNet++, PaiNN, and GemNet for molecular dynamics and quantum chemistry (900+ stars)
High-Throughput Molecular Dynamics: Programming Environment for Molecular Discovery.
A library for processing, analyzing and modeling spectroscopic data.
Universal pretrained neural network potential with charge and magnetic moment awareness, trained on 1.5M+ Materials Project inorganic structures for charge-informed molecular dynamics and phase diagram prediction (Berkeley, Nature Machine Intelligence 2023 Cover)
Library of descriptors to aid in the data-mining of materials properties, created by the Lawrence Berkeley National Laboratory.
Scientific Computing for Chemists with Python is a Jupyter book teaching basic python in chemistry skills, including relevant libraries, and applies them to solving chemical problems.
A package to 'build' collections of materials properties from the output of computational materials calculations.
Directed message passing neural networks for property prediction of molecules and reactions with uncertainty and interpretation.
A molecule manipulation library.
atomate2 is a library of computational materials science workflows.
This package provides a periodic table of the elements with support for mass, density and xray/neutron scattering information.
A [Jupyter](https://jupyter.org/) widget to interactively view molecular structures and trajectories.
NIST's open-source platform for data-driven atomistic materials design, integrating DFT datasets (JARVIS-DFT), machine learning property prediction (JARVIS-ML), and a comprehensive leaderboard for benchmarking materials AI methods across the periodic table (384+ stars)