用机器学习加速分子路径采样，自动发现反应坐标
Path Sampling for Rare Events Boosted by Machine Learning
- 结合机器学习实时估计反应概率，动态优化路径采样
- 可同时提取人类可理解的反应坐标，揭示复杂分子机制
- 适合研究化学反应、蛋白质折叠等复杂动力学过程
Jung 等人（Nat Comput Sci. 3:334-345 (2023)）提出了人工智能分子机制发现（AIMMD）算法，一种融合机器学习的新型路径采样方法，用于提升过渡路径采样（TPS）效率。该方法支持在线估计共价概率，并同时推导出人类可解释的反应坐标，为解析复杂分子过程的机理路径提供了稳健框架。本文评论了核心 AIMMD 框架，探讨其近期扩展，并评估该方法的潜力与局限。
原文摘要 · Abstract (English)
The study by Jung et al. (Jung H, Covino R, Arjun A, et al., Nat Comput Sci. 3:334-345 (2023)) introduced Artificial Intelligence for Molecular Mechanism Discovery (AIMMD), a novel sampling algorithm that integrates machine learning to enhance the efficiency of transition path sampling (TPS). By enabling on-the-fly estimation of the committor probability and simultaneously deriving a human-interpretable reaction coordinate, AIMMD offers a robust framework for elucidating the mechanistic pathways of complex molecular processes. This commentary provides a discussion and critical analysis of the core AIMMD framework, explores its recent extensions, and offers an assessment of the method's potential impact and limitations.
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