DeePMD-kit
A deep learning package for many-body potential energy representation and molecular dynamics, designed to minimize the effort required to build deep learning-based models of interatomic potential energy and force fields and to perform MD. Interfaced with TensorFlow, PyTorch, JAX, and Paddle backends, and with LAMMPS, i-PI, AMBER, CP2K, GROMACS, OpenMM, and ABACUS.
What it does
A deep learning package for many-body potential energy representation and molecular dynamics, designed to minimize the effort required to build deep learning-based models of interatomic potential energy and force fields and to perform MD. Interfaced with TensorFlow, PyTorch, JAX, and Paddle backends, and with LAMMPS, i-PI, AMBER, CP2K, GROMACS, OpenMM, and ABACUS.
- Building deep learning-based models of interatomic potential energy and force fields
- Performing molecular dynamics (MD)
- Simulating finite molecules, extended systems, metallic systems, and chemically bonded systems
- Category
- Scientific models
- Built for
- Energy and utilities
- Users
- Computational chemistry researchers, Materials science researchers, Molecular dynamics practitioners
- Public API
- Not offered
Not yet disclosed: deployment options, platforms, languages and support channels.
Pricing
Pricing isn't published
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Security and compliance
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