将过渡路径采样与数据驱动的集体变量相结合：一种基于反应性的射击算法
Combining transition path sampling with data-driven collective variables through a reactivity-biased shooting algorithm
AI总结：
本文提出一种基于反应性的射击算法，通过机器学习从过渡路径采样中迭代提取集体变量，无需先验知识即可提高稀有事件采样效率，并在多个体系中验证了其重建自由能剖面的能力。
摘要：
稀有事件采样是现代计算化学研究中的一个核心问题。在现有方法中，过渡路径采样（TPS）能够生成反应过程的无偏表示。然而，其效率取决于生成反应性试验路径的能力，而这又取决于所用射击算法的质量。我们提出了一种基于射击成功率（即反应性）的新算法，该反应性作为一组约化集体变量（CVs）的函数进行测量。这些变量通过机器学习方法直接从TPS模拟中提取，并使用多任务目标函数。通过迭代，该工作流程在无需对过程有任何先验知识的情况下显著提高了射击效率。此外，优化后的CVs可与有偏增强采样方法结合使用，以准确重建自由能剖面。我们在三个不同的体系上测试了该方法：一个二维玩具模型、丙氨酸二肽的构象转变，以及本体水中乙酰氯的水解。在最后一个体系中，我们将该工作流程与主动学习方案集成，以学习基于机器学习的反应性势能，这使我们能够以接近从头算的精度研究其机理和自由能剖面。
英文摘要：
Rare event sampling is a central problem in modern computational chemistry research. Among the existing methods, transition path sampling (TPS) can generate unbiased representations of reaction processes. However, its efficiency depends on the ability to generate reactive trial paths, which in turn depends on the quality of the shooting algorithm used. We propose a new algorithm based on the shooting success rate, i.e. reactivity, measured as a function of a reduced set of collective variables (CVs). These variables are extracted with a machine learning approach directly from TPS simulations, using a multi-task objective function. Iteratively, this workflow significantly improves shooting efficiency without any prior knowledge of the process. In addition, the optimized CVs can be used with biased enhanced sampling methodologies to accurately reconstruct the free energy profiles. We tested the method on three different systems: a two-dimensional toy model, conformational transitions of alanine dipeptide, and hydrolysis of acetyl chloride in bulk water. In the latter, we integrated our workflow with an active learning scheme to learn a reactive machine learning-based potential, which allowed us to study the mechanism and free energy profile with an ab initio-like accuracy.
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