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

Authors:
; ORCiD logo; ;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1548574
Grant/Contract Number:  
SC0008626; SC0009248
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Computer Physics Communications
Additional Journal Information:
Journal Name: Computer Physics Communications Journal Volume: 228 Journal Issue: C; Journal ID: ISSN 0010-4655
Publisher:
Elsevier
Country of Publication:
Netherlands
Language:
English

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. Netherlands: 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. Netherlands. 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". Netherlands. doi:10.1016/j.cpc.2018.03.016.
@article{osti_1548574,
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 = {},
doi = {10.1016/j.cpc.2018.03.016},
journal = {Computer Physics Communications},
number = C,
volume = 228,
place = {Netherlands},
year = {2018},
month = {7}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1016/j.cpc.2018.03.016

Citation Metrics:
Cited by: 13 works
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