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• W4213157411 startingPage "1395" @default.
• W4213157411 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." @default.
• W4213157411 created "2022-02-24" @default.
• W4213157411 creator A5004379494 @default.
• W4213157411 creator A5049325122 @default.
• W4213157411 creator A5054794415 @default.
• W4213157411 date "2022-02-17" @default.
• W4213157411 modified "2023-10-17" @default.
• W4213157411 title "Efficient Monte Carlo Sampling for Molecular Systems Using Continuous Normalizing Flow" @default.
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• W4213157411 doi "https://doi.org/10.1021/acs.jctc.1c01047" @default.
• W4213157411 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35175774" @default.
• W4213157411 hasPublicationYear "2022" @default.
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