Efficient Monte Carlo Sampling for Molecular Systems Using Continuous Normalizing Flow
Efficient Monte Carlo Sampling for Molecular Systems Using Continuous Normalizing Flow
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DOI：
10.1021/acs.jctc.1c01047
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发表时间：
2022-03-08
影响因子：
5.5
通讯作者：
Yasuoka, Kenji
中科院分区：
文献类型：
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作者：
Endo, Katsuhiro;Yuhara, Daisuke;Yasuoka, Kenji
蒙特卡罗分子模拟是模拟分子行为的强大计算方法。它生成分子系统可能状态的样本。为了有效地生成样本，避免提出永远不会成为可能状态的极高能量状态是有利的。在本研究中，我们提出了一种新的蒙特卡罗分子模拟采样方法，即连续归一化分子流（CNMF）方法，该方法可以从某些初始分布创建分子状态的各种概率分布。 CNMF 方法通过求解具有二体分子间相互作用项的一阶微分方程来生成样本。我们还使用称为反平方流的 CNMF 开发特定的概率分布，当分子对非常接近时，它会产生概率密度为零的分布，而在所有其他情况下，概率密度从初始分布均匀压缩。使用反平方流，我们证明蒙特卡罗分子模拟比标准模拟更有效。尽管CNMF方法增加的计算成本不可忽略，但该方法对于并行计算是可行的，并且具有扩展的潜力。
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.