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Title: An atomistic fingerprint algorithm for learning ab initio molecular force fields

Authors:
ORCiD logo [1];  [1];  [1]
  1. Division of Applied Mathematics, Brown University, Providence, Rhode Island 02912, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1417101
Grant/Contract Number:
Collaboratory on Mathematics for Mesoscopic Modeling of Materials
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 148; Journal Issue: 3; Related Information: CHORUS Timestamp: 2018-02-14 18:32:18; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics
Country of Publication:
United States
Language:
English

Citation Formats

Tang, Yu-Hang, Zhang, Dongkun, and Karniadakis, George Em. An atomistic fingerprint algorithm for learning ab initio molecular force fields. United States: N. p., 2018. Web. doi:10.1063/1.5008630.
Tang, Yu-Hang, Zhang, Dongkun, & Karniadakis, George Em. An atomistic fingerprint algorithm for learning ab initio molecular force fields. United States. doi:10.1063/1.5008630.
Tang, Yu-Hang, Zhang, Dongkun, and Karniadakis, George Em. Sun . "An atomistic fingerprint algorithm for learning ab initio molecular force fields". United States. doi:10.1063/1.5008630.
@article{osti_1417101,
title = {An atomistic fingerprint algorithm for learning ab initio molecular force fields},
author = {Tang, Yu-Hang and Zhang, Dongkun and Karniadakis, George Em},
abstractNote = {},
doi = {10.1063/1.5008630},
journal = {Journal of Chemical Physics},
number = 3,
volume = 148,
place = {United States},
year = {Sun Jan 21 00:00:00 EST 2018},
month = {Sun Jan 21 00:00:00 EST 2018}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on January 16, 2019
Publisher's Accepted Manuscript

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