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Title: DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

Abstract

Not provided.

Authors:
; ORCiD logo; ;
Publication Date:
Research Org.:
Princeton Univ., NJ (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1538211
DOE Contract Number:  
SC0008626; SC0009248
Resource Type:
Journal Article
Journal Name:
Computer Physics Communications
Additional Journal Information:
Journal Volume: 228; Journal Issue: C; Journal ID: ISSN 0010-4655
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
Computer Science; Physics

Citation Formats

Wang, Han, Zhang, Linfeng, Han, Jiequn, and E, Weinan. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. United States: N. p., 2018. Web. doi:10.1016/j.cpc.2018.03.016.
Wang, Han, Zhang, Linfeng, Han, Jiequn, & E, Weinan. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. United States. doi:10.1016/j.cpc.2018.03.016.
Wang, Han, Zhang, Linfeng, Han, Jiequn, and E, Weinan. Sun . "DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics". United States. doi:10.1016/j.cpc.2018.03.016.
@article{osti_1538211,
title = {DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics},
author = {Wang, Han and Zhang, Linfeng and Han, Jiequn and E, Weinan},
abstractNote = {Not provided.},
doi = {10.1016/j.cpc.2018.03.016},
journal = {Computer Physics Communications},
issn = {0010-4655},
number = C,
volume = 228,
place = {United States},
year = {2018},
month = {7}
}