A flexible and performant framework for training machine learning potentials.
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Updated
Jun 10, 2024 - Python
A flexible and performant framework for training machine learning potentials.
Official PyTorch implementation of "Comprehensive Molecular Representation from Equivariant Transformer" paper https://arxiv.org/abs/2308.10752. Made in Cardiff University.
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
Build neural networks for machine learning force fields with JAX
The Open Forcefield Toolkit provides implementations of the SMIRNOFF format, parameterization engine, and other tools. Documentation available at http://open-forcefield-toolkit.readthedocs.io
Tracking citations of atomistic simulation engines
Tinker: Software Tools for Molecular Design
NequIP is a code for building E(3)-equivariant interatomic potentials
UF3: a python library for generating ultra-fast interatomic potentials
Internal tool for benchmarking force fields
KIM-based Learning-Integrated Fitting Framework for interatomic potentials.
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
PyStokes: phoresis and Stokesian hydrodynamics in Python
A repository to hold forcefields for molecular mechanics calculations with RASPA
MACE-MP models
A general cross-platform tool for preparing simulations of molecules and complex molecular assemblies
A dataset for benchmarking non-local capabilities of geometric machine learning models.
Tinker9: Next Generation of Tinker with GPU Support
Data and scripts relevant to an evaluation of force field methods for conformer scoring
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