Machine learning for molecular dynamics with strongly correlated electrons
Abstract
We use machine learning to enable large-scale molecular dynamics (MD) of a correlated electron model under the Gutzwiller approximation scheme. This model exhibits a Mott transition as a function of on-site Coulomb repulsion U. The repeated solution of the Gutzwiller self-consistency equations would be prohibitively expensive for large-scale MD simulations. We show that machine learning models of the Gutzwiller potential energy can be remarkably accurate. The models, which are trained with N = 33 atoms, enable highly accurate MD simulations at much larger scales (N ≳103). Here, we investigate the physics of the smooth Mott crossover in the fluid phase.
- Authors:
-
[2];
[2];
- The Univ. of Tennessee, Knoxville, TN (United States); The Univ. of Tokyo, Tokyo (Japan)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- The Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Univ. of Virginia, Charlottesville, VA (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1572324
- Alternate Identifier(s):
- OSTI ID: 1505839
- Report Number(s):
- LA-UR-18-31198
Journal ID: ISSN 2469-9950; PRBMDO; TRN: US2100060
- Grant/Contract Number:
- 89233218CNA000001
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Review B
- Additional Journal Information:
- Journal Volume: 99; Journal Issue: 16; 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; Material Science
Citation Formats
Suwa, Hidemaro, Smith, Justin Steven, Lubbers, Nicholas Edward, Batista, Cristian D., Chern, Gia-Wei, and Barros, Kipton. Machine learning for molecular dynamics with strongly correlated electrons. United States: N. p., 2019.
Web. doi:10.1103/PhysRevB.99.161107.
Suwa, Hidemaro, Smith, Justin Steven, Lubbers, Nicholas Edward, Batista, Cristian D., Chern, Gia-Wei, & Barros, Kipton. Machine learning for molecular dynamics with strongly correlated electrons. United States. https://doi.org/10.1103/PhysRevB.99.161107
Suwa, Hidemaro, Smith, Justin Steven, Lubbers, Nicholas Edward, Batista, Cristian D., Chern, Gia-Wei, and Barros, Kipton. Mon .
"Machine learning for molecular dynamics with strongly correlated electrons". United States. https://doi.org/10.1103/PhysRevB.99.161107. https://www.osti.gov/servlets/purl/1572324.
@article{osti_1572324,
title = {Machine learning for molecular dynamics with strongly correlated electrons},
author = {Suwa, Hidemaro and Smith, Justin Steven and Lubbers, Nicholas Edward and Batista, Cristian D. and Chern, Gia-Wei and Barros, Kipton},
abstractNote = {We use machine learning to enable large-scale molecular dynamics (MD) of a correlated electron model under the Gutzwiller approximation scheme. This model exhibits a Mott transition as a function of on-site Coulomb repulsion U. The repeated solution of the Gutzwiller self-consistency equations would be prohibitively expensive for large-scale MD simulations. We show that machine learning models of the Gutzwiller potential energy can be remarkably accurate. The models, which are trained with N = 33 atoms, enable highly accurate MD simulations at much larger scales (N ≳103). Here, we investigate the physics of the smooth Mott crossover in the fluid phase.},
doi = {10.1103/PhysRevB.99.161107},
journal = {Physical Review B},
number = 16,
volume = 99,
place = {United States},
year = {Mon Apr 08 00:00:00 EDT 2019},
month = {Mon Apr 08 00:00:00 EDT 2019}
}
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Works referencing / citing this record:
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