Difference between revisions of "DeepMD-LAMMPS"
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DeePMD-kit is a package written in Python/C++, designed to minimize the effort required to build deep learning-based model of interatomic potential energy and force field and to perform molecular dynamics (MD). This brings new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems. | DeePMD-kit is a package written in Python/C++, designed to minimize the effort required to build deep learning-based model of interatomic potential energy and force field and to perform molecular dynamics (MD). This brings new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems. | ||
| + | Highlighted features | ||
| + | Interfaced with TensorFlow, making the training process highly automatic and efficient. | ||
| − | + | Interfaced with high-performance classical MD and quantum (path-integral) MD packages, including LAMMPS, i-PI, AMBER, CP2K, GROMACS, OpenMM, and ABUCUS. | |
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| + | Implements the Deep Potential series models, which have been successfully applied to finite and extended systems, including organic molecules, metals, semiconductors, insulators, etc. | ||
| − | + | Implements MPI and GPU support, making it highly efficient for high-performance parallel and distributed computing. highly modularized, easy to adapt to different descriptors for deep learning-based potential energy models. | |
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Latest revision as of 20:08, 27 June 2024
Description
DeePMD-kit is a package written in Python/C++, designed to minimize the effort required to build deep learning-based model of interatomic potential energy and force field and to perform molecular dynamics (MD). This brings new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems. Highlighted features
Interfaced with TensorFlow, making the training process highly automatic and efficient.
Interfaced with high-performance classical MD and quantum (path-integral) MD packages, including LAMMPS, i-PI, AMBER, CP2K, GROMACS, OpenMM, and ABUCUS.
Implements the Deep Potential series models, which have been successfully applied to finite and extended systems, including organic molecules, metals, semiconductors, insulators, etc.
Implements MPI and GPU support, making it highly efficient for high-performance parallel and distributed computing. highly modularized, easy to adapt to different descriptors for deep learning-based potential energy models.
Environment Modules
Run module spider DeepMD-LAMMPS to find out what environment modules are available for this application.
System Variables
- HPC_DEEPMD-LAMMPS_DIR - installation directory