12 dépôts
Awesome GitHub RepositoriesGeneral Tools
Comprehensive toolkits and libraries for atomistic machine learning and materials informatics.
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Awesome General Tools GitHub Repositories
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- deepchem/deepchem
deepchem/deepchem
6,545Voir sur GitHubDeepChem is an open-source Python framework for applying deep learning to molecular, chemical, and biological data, serving as a comprehensive toolkit for drug discovery and materials science. At its core, it provides a featurizer-pipeline abstraction that converts raw molecular data into numerical representations, including graph-based molecular structures, SMILES tokenization vocabularies, and disk-sharded dataset persistence for handling large-scale data that exceeds RAM capacity. The framework distinguishes itself through integrated molecular docking workflows that automate pocket detecti
Deep learning toolkit for drug discovery and quantum chemistry.
Pythonbiologydeep-learningdrug-discovery - rdkit/rdkit
rdkit/rdkit
3,473Voir sur GitHubThe official sources for the RDKit library
Core cheminformatics and machine learning library for molecules.
HTMLc-plus-pluscheminformaticspython - divelab/airs
divelab/AIRS
license-image:https://img.shields.io/badge/license-GPL3.0-green.svg license-url:https://github.com/divelab/AIRS/blob/main/LICENSE contributing-image:https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat
Research-focused AI tools for scientific modeling and simulation.
Jupyter Notebook - materialyzeai/maml
materialyzeai/maml
maml (MAterials Machine Learning) is a Python package that aims to provide useful high-level interfaces that make ML for materials science as easy as possible.
Python toolkit for materials descriptors and force fields.
Jupyter Notebook - libatoms/quip
libAtoms/QUIP
The QUIP package is a collection of software tools to carry out molecular dynamics simulations. It implements a variety of interatomic potentials and tight binding quantum mechanics, and is also able to call external packages, and serve as plugins to other software such as LAMMPS, CP2K and also…
Molecular dynamics framework with machine-learned interatomic potentials.
Fortran - usnistgov/jarvis
usnistgov/jarvis
The JARVIS-Tools is an open-access software package for atomistic data-driven materials design. JARVIS-Tools can be used for a) setting up calculations, b) analysis and informatics, c) plotting, d) database development and e) web-page development.
Open-source package for data-driven atomistic materials design.
Python - pycroscopy/atomai
pycroscopy/atomai
AtomAI is a Pytorch-based package for deep and machine learning analysis of microscopy data that doesn't require any advanced knowledge of Python or machine learning. The intended audience is domain scientists with a basic understanding of how to use NumPy and Matplotlib. It was developed by…
Deep learning tools for microscopy and atomistic data.
Python - datamol-io/molfeat
datamol-io/molfeat
molfeat - the hub for all your molecular featurizers Docs | Homepage
Hub for molecular featurizers and pretrained representations.
Python - qmlcode/qml
qmlcode/qml
QML: Quantum Machine Learning
Toolkit for quantum machine learning applications.
Python - dralgroup/mlatom
dralgroup/mlatom
](https://pepy.tech/project/mlatom)
AI-enhanced computational chemistry and active learning framework.
Python - uw-cmg/mast-ml
uw-cmg/MAST-ML
MAST-ML is an open-source Python package designed to broaden and accelerate the use of machine learning in materials science research
Materials simulation toolkit for machine learning workflows.
Jupyter Notebook - scikit-learn-contrib/scikit-matter
scikit-learn-contrib/scikit-matter
scikit-matter
Scikit-learn compatible utilities for materials science methods.
Python
