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Title: Raman spectrum and polarizability of liquid water from deep neural networks

Abstract

Using deep neural networks to model the polarizability and potential energy surfaces, we compute the Raman spectrum of liquid water at several temperatures with ab initio molecular dynamics accuracy.

Authors:
 [1]; ORCiD logo [2];  [3];  [4];  [5]
  1. Department of Physics, Princeton University, Princeton, USA
  2. Department of Chemistry, Princeton University, Princeton, USA
  3. Program in Applied and Computational Mathematics, Princeton University, Princeton, USA
  4. Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing 100088, P. R. China
  5. Department of Physics, Princeton University, Princeton, USA, Department of Chemistry
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1617522
Grant/Contract Number:  
AC02-05CH11231; SC0019394
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Physical Chemistry Chemical Physics. PCCP
Additional Journal Information:
Journal Name: Physical Chemistry Chemical Physics. PCCP Journal Volume: 22 Journal Issue: 19; Journal ID: ISSN 1463-9076
Publisher:
Royal Society of Chemistry (RSC)
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Sommers, Grace M., Calegari Andrade, Marcos F., Zhang, Linfeng, Wang, Han, and Car, Roberto. Raman spectrum and polarizability of liquid water from deep neural networks. United Kingdom: N. p., 2020. Web. doi:10.1039/D0CP01893G.
Sommers, Grace M., Calegari Andrade, Marcos F., Zhang, Linfeng, Wang, Han, & Car, Roberto. Raman spectrum and polarizability of liquid water from deep neural networks. United Kingdom. doi:https://doi.org/10.1039/D0CP01893G
Sommers, Grace M., Calegari Andrade, Marcos F., Zhang, Linfeng, Wang, Han, and Car, Roberto. Wed . "Raman spectrum and polarizability of liquid water from deep neural networks". United Kingdom. doi:https://doi.org/10.1039/D0CP01893G.
@article{osti_1617522,
title = {Raman spectrum and polarizability of liquid water from deep neural networks},
author = {Sommers, Grace M. and Calegari Andrade, Marcos F. and Zhang, Linfeng and Wang, Han and Car, Roberto},
abstractNote = {Using deep neural networks to model the polarizability and potential energy surfaces, we compute the Raman spectrum of liquid water at several temperatures with ab initio molecular dynamics accuracy.},
doi = {10.1039/D0CP01893G},
journal = {Physical Chemistry Chemical Physics. PCCP},
number = 19,
volume = 22,
place = {United Kingdom},
year = {2020},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: https://doi.org/10.1039/D0CP01893G

Citation Metrics:
Cited by: 3 works
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