论文标题
分子动力学中采样过渡路径的生成方法
Generative methods for sampling transition paths in molecular dynamics
论文作者
论文摘要
在切换到另一种势能之前，分子系统通常会长时间被困在势能函数的某些局部最小值左右 - 一种称为亚稳定性的行为。通过直接数值方法，很难模拟将一个亚稳态与另一个状态联系起来的过渡路径。鉴于机器学习技术的承诺，我们在这项工作中探索了两种方法，以更有效地生成过渡路径：基于生成模型的采样方法，例如变异自动编码器，以及基于增强学习的重要性采样方法。
Molecular systems often remain trapped for long times around some local minimum of the potential energy function, before switching to another one -- a behavior known as metastability. Simulating transition paths linking one metastable state to another one is difficult by direct numerical methods. In view of the promises of machine learning techniques, we explore in this work two approaches to more efficiently generate transition paths: sampling methods based on generative models such as variational autoencoders, and importance sampling methods based on reinforcement learning.
