NequIP is a code for building E(3)-equivariant interatomic potentials
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Updated
Sep 17, 2024 - Python
NequIP is a code for building E(3)-equivariant interatomic potentials
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
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python library for atomistic machine learning
A Python library and command line interface for automated free energy calculations
UF3: a python library for generating ultra-fast interatomic potentials
KIM-based Learning-Integrated Fitting Framework for interatomic potentials.
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PotentialLearning.jl: Optimize your atomistic data and interatomic potential models in your molecular dynamic workflows.
A flexible and performant framework for training machine learning potentials.
Fair and transparent benchmark of machine learning interatomic potentials (MLIPs), beyond basic error metrics
Generator of polynomial machine learning potentials
Fitting interatomic potential for molecular dynamics
Provide easy-to-use CESMIX-aligned case studies. Integrate the latest developments of the Julia atomistic ecosystem and state-of-the-art tools.
A Python library for developing machine learning interatomic potentials, based on Google JAX.
Modified Embedded Atom Method with Bond Order (MEAM-BO) implementation in LAMMPS
Automatic Differentiation of Interatomic Potentials with Phonons in Julia: ADIP².jl
A python package for fast building amorphous solids and liquid mixtures from @materialsproject computed structures and machine learning interatomic potentials
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