Description
GPUMD stands for Graphics Processing Units Molecular Dynamics. It is a general-purpose molecular dynamics (MD) code fully implemented on graphics processing units (GPUs).
Force evaluation for many-body potentials has been significantly accelerated by using GPUs [1], thanks to a set of simple expressions for force, virial stress, and heat current derived in Refs. [2, 3].
Apart from being highly efficient, another unique feature of GPUMD is that it has useful utilities to study heat transport [2, 3, 4, 5].
The title and description of this software correspond to the situation when the software metadata was imported to ACRIS. The most recent version of metadata is available in the original repository.
It can run MD simulations with the machine-learning based force constant potential (FCP) [6].
It can train the NEP machine-learning potential [7, 8, 9] and run MD simulations with it. See this nep-data Gitlab repo for some of the published NEP potentials and the related training/testing data.
Full description: https://github.com/brucefan1983/GPUMD/tree/v3.6
The title and description of this software/code correspond with the situation when the software metadata was imported to ACRIS. The most recent version of metadata is available in the original repository.
Force evaluation for many-body potentials has been significantly accelerated by using GPUs [1], thanks to a set of simple expressions for force, virial stress, and heat current derived in Refs. [2, 3].
Apart from being highly efficient, another unique feature of GPUMD is that it has useful utilities to study heat transport [2, 3, 4, 5].
The title and description of this software correspond to the situation when the software metadata was imported to ACRIS. The most recent version of metadata is available in the original repository.
It can run MD simulations with the machine-learning based force constant potential (FCP) [6].
It can train the NEP machine-learning potential [7, 8, 9] and run MD simulations with it. See this nep-data Gitlab repo for some of the published NEP potentials and the related training/testing data.
Full description: https://github.com/brucefan1983/GPUMD/tree/v3.6
The title and description of this software/code correspond with the situation when the software metadata was imported to ACRIS. The most recent version of metadata is available in the original repository.
| Date made available | 24 Sept 2017 |
|---|---|
| Publisher | Zenodo |
Dataset Licences
- Other
Datasets
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Supplementary Data: Kapitza thermal resistance across individual grain boundaries in graphene
Fan, Z. (Creator), Zenodo, 11 Oct 2017
Dataset