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.

翻译：分子系统常常长时间被困在势能函数的某个局部极小值附近，之后才切换到另一个极小值——这种特性被称为亚稳态。通过直接数值方法模拟连接不同亚稳态之间的过渡路径十分困难。鉴于机器学习技术的前景，本文探索了两种更高效生成过渡路径的方法：基于变分自编码器等生成模型的采样方法，以及基于强化学习的重要采样方法。