TY - JOUR

T1 - Efficient Monte Carlo Sampling for Molecular Systems Using Continuous Normalizing Flow

AU - Endo, Katsuhiro

AU - Yuhara, Daisuke

AU - Yasuoka, Kenji

N1 - Publisher Copyright:
© 2022 American Chemical Society. All rights reserved.

PY - 2022/3/8

Y1 - 2022/3/8

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85125399713&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85125399713&partnerID=8YFLogxK

U2 - 10.1021/acs.jctc.1c01047

DO - 10.1021/acs.jctc.1c01047

M3 - Article

C2 - 35175774

AN - SCOPUS:85125399713

SN - 1549-9618

VL - 18

SP - 1395

EP - 1405

JO - Journal of chemical theory and computation

JF - Journal of chemical theory and computation

IS - 3

ER -