A perspective on machine learning and data science for strongly correlated electron problems
Abstract
Numerical approaches to the correlated electron problem have achieved considerable success, yet are still constrained by several bottlenecks, including high order polynomial or exponential scaling in system size, long autocorrelation times, challenges in recognizing novel phases, and the Fermion sign problem. Methods in machine learning (ML), artificial intelligence, and data science promise to help address these limitations and open up a new frontier in strongly correlated quantum system simulations. In this paper, we review some of the progress in this area. We begin by examining these approaches in the context of classical models, where their underpinnings and application can be easily illustrated and benchmarked. We then discuss cases where ML methods have enabled scientific discovery. Finally, we will examine their applications in accelerating model solutions in state-of-the-art quantum many-body methods like quantum Monte Carlo and discuss potential future research directions.
- Authors:
- Publication Date:
- Research Org.:
- Univ. of Tennessee, Knoxville, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1897543
- Alternate Identifier(s):
- OSTI ID: 1907641
- Grant/Contract Number:
- SC0022311
- Resource Type:
- Published Article
- Journal Name:
- Carbon Trends
- Additional Journal Information:
- Journal Name: Carbon Trends Journal Volume: 9 Journal Issue: C; Journal ID: ISSN 2667-0569
- Publisher:
- Elsevier
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; Many-body physics; Quantum materials; Machine learning
Citation Formats
Johnston, Steven, Khatami, Ehsan, and Scalettar, Richard. A perspective on machine learning and data science for strongly correlated electron problems. United Kingdom: N. p., 2022.
Web. doi:10.1016/j.cartre.2022.100231.
Johnston, Steven, Khatami, Ehsan, & Scalettar, Richard. A perspective on machine learning and data science for strongly correlated electron problems. United Kingdom. https://doi.org/10.1016/j.cartre.2022.100231
Johnston, Steven, Khatami, Ehsan, and Scalettar, Richard. Sat .
"A perspective on machine learning and data science for strongly correlated electron problems". United Kingdom. https://doi.org/10.1016/j.cartre.2022.100231.
@article{osti_1897543,
title = {A perspective on machine learning and data science for strongly correlated electron problems},
author = {Johnston, Steven and Khatami, Ehsan and Scalettar, Richard},
abstractNote = {Numerical approaches to the correlated electron problem have achieved considerable success, yet are still constrained by several bottlenecks, including high order polynomial or exponential scaling in system size, long autocorrelation times, challenges in recognizing novel phases, and the Fermion sign problem. Methods in machine learning (ML), artificial intelligence, and data science promise to help address these limitations and open up a new frontier in strongly correlated quantum system simulations. In this paper, we review some of the progress in this area. We begin by examining these approaches in the context of classical models, where their underpinnings and application can be easily illustrated and benchmarked. We then discuss cases where ML methods have enabled scientific discovery. Finally, we will examine their applications in accelerating model solutions in state-of-the-art quantum many-body methods like quantum Monte Carlo and discuss potential future research directions.},
doi = {10.1016/j.cartre.2022.100231},
journal = {Carbon Trends},
number = C,
volume = 9,
place = {United Kingdom},
year = {Sat Oct 01 00:00:00 EDT 2022},
month = {Sat Oct 01 00:00:00 EDT 2022}
}
https://doi.org/10.1016/j.cartre.2022.100231
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