论文标题
使用标准化流量的液体系统平衡状态之间学习映射
Learning Mappings between Equilibrium States of Liquid Systems Using Normalizing Flows
论文作者
论文摘要
生成模型是解决统计力学框架中多体和凝结系统系统中采样问题的有前途的工具。在这项工作中，我们表明可以使用归一化的流程来学习转换，以相互映射不同的液体系统，同时允许通过重新升级过程获得无偏见的平衡分布。提出了两项原告计算，用于对具有不同深度的潜在井和Lennard-Jones之间的颗粒的Lennard-Jones系统之间的转换，以及Lennard-Jones与排斥颗粒系统之间的转化。在两个数值实验中，系统都处于液态状态。在将来的应用中，这种方法可能会导致有效的方法以Ab-Initio准确性模拟液体系统，并使用较少精确模型的计算成本（例如力场或粗粒模拟）的计算成本。
Generative models are a promising tool to address the sampling problem in multi-body and condensed-matter systems in the framework of statistical mechanics. In this work, we show that normalizing flows can be used to learn a transformation to map different liquid systems into each other allowing at the same time to obtain an unbiased equilibrium distribution through a reweighting process. Two proof-of-principles calculations are presented for the transformation between Lennard-Jones systems of particles with different depths of the potential well and for the transformation between a Lennard-Jones and a system of repulsive particles. In both numerical experiments, systems are in the liquid state. In future applications, this approach could lead to efficient methods to simulate liquid systems at ab-initio accuracy with the computational cost of less accurate models, such as force field or coarse-grained simulations.
