Deep neural network for the dielectric response of insulators
Abstract
In this work, we introduce a deep neural network to model in a symmetry preserving way the environmental dependence of the centers of the electronic charge. The model learns from ab initio density functional theory, wherein the electronic centers are uniquely assigned by the maximally localized Wannier functions. When combined with the deep potential model of the atomic potential energy surface, the scheme predicts the dielectric response of insulators for trajectories inaccessible to direct ab initio simulation. The scheme is nonperturbative and can capture the response of a mutating chemical environment. We demonstrate the approach by calculating the infrared spectra of liquid water at standard conditions, and of ice under extreme pressure, when it transforms from a molecular to an ionic crystal.
- Authors:
-
[1];
[3];
[4];
[1]
- Princeton Univ., NJ (United States)
- Peking Univ., Beijing (China)
- Temple Univ., Philadelphia, PA (United States)
- Institute of Applied Physics and Computational Mathematics, Beijing (China)
- Princeton Univ., NJ (United States); Beijing Institute of Big Data Research (China)
- Publication Date:
- Research Org.:
- Princeton Univ., NJ (United States); Univ. of California, Oakland, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
- Sponsoring Org.:
- USDOE Office of Science (SC); US Department of the Navy, Office of Naval Research (ONR); National Natural Science Foundation of China (NSFC); National Key Research and Development Program of China
- OSTI Identifier:
- 1803785
- Grant/Contract Number:
- SC0019394; AC02-05CH11231; N00014-13-1-0338; 11871110; 2016YFB0201200; 2016YFB0201203
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Review. B
- Additional Journal Information:
- Journal Volume: 102; Journal Issue: 4; Journal ID: ISSN 2469-9950
- Publisher:
- American Physical Society (APS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY; Materials Science; Physics
Citation Formats
Zhang, Linfeng, Chen, Mohan, Wu, Xifan, Wang, Han, E, Weinan, and Car, Roberto. Deep neural network for the dielectric response of insulators. United States: N. p., 2020.
Web. doi:10.1103/physrevb.102.041121.
Zhang, Linfeng, Chen, Mohan, Wu, Xifan, Wang, Han, E, Weinan, & Car, Roberto. Deep neural network for the dielectric response of insulators. United States. https://doi.org/10.1103/physrevb.102.041121
Zhang, Linfeng, Chen, Mohan, Wu, Xifan, Wang, Han, E, Weinan, and Car, Roberto. Wed .
"Deep neural network for the dielectric response of insulators". United States. https://doi.org/10.1103/physrevb.102.041121. https://www.osti.gov/servlets/purl/1803785.
@article{osti_1803785,
title = {Deep neural network for the dielectric response of insulators},
author = {Zhang, Linfeng and Chen, Mohan and Wu, Xifan and Wang, Han and E, Weinan and Car, Roberto},
abstractNote = {In this work, we introduce a deep neural network to model in a symmetry preserving way the environmental dependence of the centers of the electronic charge. The model learns from ab initio density functional theory, wherein the electronic centers are uniquely assigned by the maximally localized Wannier functions. When combined with the deep potential model of the atomic potential energy surface, the scheme predicts the dielectric response of insulators for trajectories inaccessible to direct ab initio simulation. The scheme is nonperturbative and can capture the response of a mutating chemical environment. We demonstrate the approach by calculating the infrared spectra of liquid water at standard conditions, and of ice under extreme pressure, when it transforms from a molecular to an ionic crystal.},
doi = {10.1103/physrevb.102.041121},
journal = {Physical Review. B},
number = 4,
volume = 102,
place = {United States},
year = {Wed Jul 22 00:00:00 EDT 2020},
month = {Wed Jul 22 00:00:00 EDT 2020}
}
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