Strategy MMIC for force field parameter assignment
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Updated
May 7, 2021 - Python
Strategy MMIC for force field parameter assignment
Ridge-regression Atomistic Force Fields in PYthon
Learning neural network potentials using meta-learning. Final project for Stanford's CS330: Deep Multi-Task and Meta-Learning
Quantum to Molecular Mechanics (Q2MM)
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)
[ICLR'23 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
Data and scripts relevant to an evaluation of force field methods for conformer scoring
A general cross-platform tool for preparing simulations of molecules and complex molecular assemblies
Build neural networks for machine learning force fields with JAX
Official PyTorch implementation of "Comprehensive Molecular Representation from Equivariant Transformer" paper https://arxiv.org/abs/2308.10752. Made in Cardiff University.
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
A flexible and performant framework for training machine learning potentials.
KIM-based Learning-Integrated Fitting Framework for interatomic potentials.
Internal tool for benchmarking force fields
UF3: a python library for generating ultra-fast interatomic potentials
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
NequIP is a code for building E(3)-equivariant 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
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