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

Journal Article · Tue Jun 02 00:00:00 EDT 2020 · Journal of Physical Chemistry. B, Condensed Matter, Materials, Surfaces, Interfaces and Biophysical Chemistry

DOI:https://doi.org/10.1021/acs.jpcb.0c03738· OSTI ID:1699465

Jadrich, Ryan Bradley ^[1]; Leiding, Jeffery Allen ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

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.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC). Advanced Scientific Computing Research (ASCR)
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1699465
• Report Number(s): LA-UR--20-21151
• Journal Information: Journal of Physical Chemistry. B, Condensed Matter, Materials, Surfaces, Interfaces and Biophysical Chemistry, Journal Name: Journal of Physical Chemistry. B, Condensed Matter, Materials, Surfaces, Interfaces and Biophysical Chemistry Journal Issue: 26 Vol. 124; ISSN 1520-6106
• Publisher: American Chemical Society
• Country of Publication: United States
• Language: English

Related Subjects

74 ATOMIC AND MOLECULAR PHYSICS
Monte Carlo
ab initio
density functional theory
machine learning
molecular dynamics
simulation