NequIP is a code for building E(3)-equivariant interatomic potentials
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Updated
Oct 25, 2024 - Python
NequIP is a code for building E(3)-equivariant interatomic potentials
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
The Open Forcefield Toolkit provides implementations of the SMIRNOFF format, parameterization engine, and other tools. Documentation available at http://open-forcefield-toolkit.readthedocs.io
A general cross-platform tool for preparing simulations of molecules and complex molecular assemblies
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
Tinker: Software Tools for Molecular Design
List of molecules (small molecules, RNA, peptide, protein, enzymes, antibody, and PPIs) conformations and molecular dynamics (force fields) using generative artificial intelligence and deep learning
Build neural networks for machine learning force fields with JAX
UF3: a python library for generating ultra-fast interatomic potentials
Tinker9: Next Generation of Tinker with GPU Support
PyStokes: phoresis and Stokesian hydrodynamics in Python. github.com/rajeshrinet/pystokes
MACE-MP models
KIM-based Learning-Integrated Fitting Framework for interatomic potentials.
Quantum to Molecular Mechanics (Q2MM)
Tracking citations of atomistic simulation engines
[ArXiv 2024] Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields
A flexible and performant framework for training machine learning potentials.
Optimization tool for calibrating coarse-grained force fields of lipids, relying on the simultaneous usage of reference AA trajectories (bottom-up) and experimental data (top-down)
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