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：分子系统往往长期被困在潜在能源功能的某个本地最低值周围,然后转而采用另一种行为 -- -- 一种被称为“可变性”的行为。模拟将一个可变状态与另一个可变状态连接起来的过渡路径很难直接用数字方法。鉴于机器学习技术的许诺,我们在此工作中探讨两种更有效地产生转轨路径的方法:基于变异自动转换器等基因模型的抽样方法,以及基于强化学习的重要抽样方法。