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Awesome Machine Learning Interatomic Potentials
License: GNU General Public License v3.0
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Awesome Machine Learning Interatomic Potentials
DeepChem : Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology.SchNetPack : Deep Neural Networks for Atomistic Systems.TorchANI : Accurate Neural Network Potential on PyTorch.AmpTorch : Atomistic Machine-learning Package.KLIFF : KIM-based Learning-Integrated Fitting Framework.NequIP : An open-source code for building E(3)-equivariant interatomic potentials.OpenChem : A deep learning toolkit for Computational Chemistry with PyTorch backend.PyXtal FF : A Python package for Machine learning of interatomic force field.MLatom : A Package for Atomistic Simulations with Machine Learning.REANN : A PyTorch-based end-to-end multi-functional Deep Neural Network Package for Molecular, Reactive and Periodic Systems.ML4Chem : Machine Learning for Chemistry and Materials Science.SpookyNet : Learning force fields with electronic degrees of freedom and nonlocal effects. Paper here .megnet : Graph Networks as a Universal Machine Learning Framework for Molecules and CrystalsTensorMol : Tensorflow + Molecules = TensorMol.n2p2 : A Neural Network Potential Package.TurboGAP
PYSEQM : PyTorch-based Semi-Empirical Quantum MechanicsDGL : Python package built to ease deep learning on graph, on top of existing DL frameworks.TorchDrug : A powerful and flexible machine learning platform for drug discovery.
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