机器学习分子动力学数据中的动力学
Machine learning kinetics from molecular dynamics data
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中文总结 AI 辅助
本文综述了从分子动力学数据中估计提交概率等动力学统计量的自监督方法，通过统一算子视角连接多种技术，并提供理论与实践指导，以推动其在反应速率计算等领域的应用。
中文摘要 AI 辅助
大多数分子转变发生在远超直接分子动力学模拟的时间尺度上。提交概率（committor），即一个构型在到达反应物状态之前到达产物状态的概率，是一个核心的动力学统计量，提供了与机制无关的反应坐标，并为转变路径理论和速率计算奠定了基础。本综述调查了从分子模拟中估计提交概率及相关动力学统计量的现代方法，重点强调自监督方法，这些方法学习其定义动力学方程的解，而非依赖标记的射击数据。我们发展了一个统一的算子视角，将基于生成器的偏微分方程、变分原理、马尔可夫状态模型、动力学伽辽金近似和神经网络联系起来。实证和理论证据表明这些方法的效率。我们提供了理论和实践指导，以在应用中实现其全部潜力，包括处理非马尔可夫效应和采样的策略。最后，我们指出了进一步研究的机会，包括与强化学习和生成建模的联系。
英文摘要
Most molecular transitions occur on timescales far beyond direct molecular dynamics simulations. The committor, the probability that a configuration reaches a product state before a reactant state, is a central kinetic statistic, providing a mechanism-independent reaction coordinate and a foundation for transition path theory and the calculation of rates. This review surveys modern approaches for estimating the committor and related kinetic statistics from molecular simulations, with an emphasis on self-supervised methods that learn solutions of their defining dynamical equations rather than relying on labeled shooting data. We develop a common operator viewpoint connecting generator-based partial differential equations, variational principles, Markov state models, dynamical Galerkin approximation, and neural networks. Empirical and theoretical evidence points to the efficiency of these methods. We provide theoretical and practical guidance for realizing their full potential in applications, including strategies for treating non-Markovian effects and for sampling. We conclude by identifying opportunities for further research, including connections to reinforcement learning and generative modeling.
发表机构
- New York University（纽约大学）
- University of Chicago（芝加哥大学）
机构由 AI 辅助整理，请以论文原文为准。
