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Title: Accelerating Ab Initio Simulation via Nested Monte Carlo and Machine Learned Reference Potentials

Abstract

As a corollary of the rapid advances in computing, ab initio simulation is playing an increasingly important role in modeling materials at the atomic scale. Two strategies are possible, ab initio Monte Carlo (AIMC) and molecular dynamics (AIMD) simulation. The former benefits from exact sampling from the correct thermodynamic distribution, while the latter is typically more efficient with its collective all-atom coordinate updates. In this study, using a relatively simple test model comprised of helium and argon, we show that AIMC can be brought up to, and even above, the performance levels of AIMD via a hybrid nested sampling/machine learning (ML) strategy. Here, ML provides an accurate classical reference potential (up to three-body explicit interactions) that can pilot long collective Monte Carlo moves that are accepted or rejected in toto à la nested Monte Carlo (NMC); this is in contrast to the single move nature of a naive implementation. Our proposed method only requires a small up front expense from evaluating the ab initio energies and forces of (100) random configurations for training. Importantly, our method does not totally rely on the trained, assuredly imperfect, interaction. We show that high performance and exact sampling at the desired level of theorymore » can be realized even when the trained interaction has appreciable differences from the ab initio potential. Remarkably, at the highest levels of performance realized via our approach, a pair of statistically uncorrelated atomic configurations can be generated with (1) ab initio calculations.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]
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  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Office of Science (SC). Advanced Scientific Computing Research (ASCR); USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1699465
Report Number(s):
LA-UR-20-21151
Journal ID: ISSN 1520-6106; TRN: US2204532
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Physical Chemistry. B, Condensed Matter, Materials, Surfaces, Interfaces and Biophysical Chemistry
Additional Journal Information:
Journal Volume: 124; Journal Issue: 26; Journal ID: ISSN 1520-6106
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
74 ATOMIC AND MOLECULAR PHYSICS; machine learning; simulation; Monte Carlo; molecular dynamics; ab initio; density functional theory

Citation Formats

Jadrich, Ryan Bradley, and Leiding, Jeffery Allen. Accelerating Ab Initio Simulation via Nested Monte Carlo and Machine Learned Reference Potentials. United States: N. p., 2020. Web. doi:10.1021/acs.jpcb.0c03738.
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Jadrich, Ryan Bradley, & Leiding, Jeffery Allen. Accelerating Ab Initio Simulation via Nested Monte Carlo and Machine Learned Reference Potentials. United States. https://doi.org/10.1021/acs.jpcb.0c03738
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Jadrich, Ryan Bradley, and Leiding, Jeffery Allen. Tue . "Accelerating Ab Initio Simulation via Nested Monte Carlo and Machine Learned Reference Potentials". United States. https://doi.org/10.1021/acs.jpcb.0c03738. https://www.osti.gov/servlets/purl/1699465.
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@article{osti_1699465,
title = {Accelerating Ab Initio Simulation via Nested Monte Carlo and Machine Learned Reference Potentials},
author = {Jadrich, Ryan Bradley and Leiding, Jeffery Allen},
abstractNote = {As a corollary of the rapid advances in computing, ab initio simulation is playing an increasingly important role in modeling materials at the atomic scale. Two strategies are possible, ab initio Monte Carlo (AIMC) and molecular dynamics (AIMD) simulation. The former benefits from exact sampling from the correct thermodynamic distribution, while the latter is typically more efficient with its collective all-atom coordinate updates. In this study, using a relatively simple test model comprised of helium and argon, we show that AIMC can be brought up to, and even above, the performance levels of AIMD via a hybrid nested sampling/machine learning (ML) strategy. Here, ML provides an accurate classical reference potential (up to three-body explicit interactions) that can pilot long collective Monte Carlo moves that are accepted or rejected in toto à la nested Monte Carlo (NMC); this is in contrast to the single move nature of a naive implementation. Our proposed method only requires a small up front expense from evaluating the ab initio energies and forces of (100) random configurations for training. Importantly, our method does not totally rely on the trained, assuredly imperfect, interaction. We show that high performance and exact sampling at the desired level of theory can be realized even when the trained interaction has appreciable differences from the ab initio potential. Remarkably, at the highest levels of performance realized via our approach, a pair of statistically uncorrelated atomic configurations can be generated with (1) ab initio calculations.},
doi = {10.1021/acs.jpcb.0c03738},
journal = {Journal of Physical Chemistry. B, Condensed Matter, Materials, Surfaces, Interfaces and Biophysical Chemistry},
number = 26,
volume = 124,
place = {United States},
year = {Tue Jun 02 00:00:00 EDT 2020},
month = {Tue Jun 02 00:00:00 EDT 2020}
}
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https://doi.org/10.1021/acs.jpcb.0c03738
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