python library for atomistic machine learning
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Updated
Jun 28, 2024 - Python
python library for atomistic machine learning
KIM-based Learning-Integrated Fitting Framework for interatomic potentials.
NequIP is a code for building E(3)-equivariant interatomic potentials
A Python library and command line interface for automated free energy calculations
UF3: a python library for generating ultra-fast interatomic potentials
A flexible and performant framework for training machine learning 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
A Python package for developing machine learning interatomic potentials, based on JAX.
A python package for fast building amorphous solids and liquid mixtures from https://materialsproject.org computed structures and machine learning interatomic potentials
A parallel molecular dynamics simulation algorithm for studying the thermal stability of nanoalloys.
[ICLR'23 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
An attempt to fit ab-initio materials database (e.g. Materials Project) results to FitSNAP potential.
Fitting interatomic potential for molecular dynamics
Сollection of ab initio calculated molecular potential energy surfaces
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