Efficient Monte Carlo Sampling for Molecular Systems Using Continuous Normalizing Flow

Katsuhiro Endo, Daisuke Yuhara, Kenji Yasuoka

Research output: Contribution to journalArticlepeer-review

Abstract

Monte Carlo molecular simulation is a powerful computational method for simulating molecular behavior. It generates samples of the possible states of molecular systems. To generate a sample efficiently, it is advantageous to avoid suggesting extremely high-energy states that would never become possible states. In this study, we propose a new sampling method for Monte Carlo molecular simulation, that is, a continuous normalizing molecular flow (CNMF) method, which can create various probabilistic distributions of molecular states from some initial distribution. The CNMF method generates samples by solving a first-order differential equation with two-body intermolecular interaction terms. We also develop specific probabilistic distributions using CNMF called inverse square flow, which yields distributions with zero probability density when molecule pairs are in close proximity, whereas probability densities are compressed uniformly from the initial distribution in all other cases. Using inverse square flow, we demonstrate that Monte Carlo molecular simulation is more efficient than the standard simulation. Although the increased computational costs of the CNMF method are non-negligible, this method is feasible for parallel computation and has the potential for expansion.

Original languageEnglish
Pages (from-to)1395-1405
Number of pages11
JournalJournal of chemical theory and computation
Volume18
Issue number3
DOIs
Publication statusPublished - 2022 Mar 8

ASJC Scopus subject areas

  • Computer Science Applications
  • Physical and Theoretical Chemistry

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