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Awesome Machine Learning Interatomic Potentials

Table of contents

Software

  • 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 Crystals
  • TensorMol: Tensorflow + Molecules = TensorMol.
  • n2p2: A Neural Network Potential Package.
  • TurboGAP

Other

  • PYSEQM: PyTorch-based Semi-Empirical Quantum Mechanics
  • DGL: 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.

See Also

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