发表机构
University of Vienna; University of Augsburg（维也纳大学; 奥格斯堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出GenAIMMD算法，结合committor学习与条件玻尔兹曼生成器，实现无需先验反应坐标的无相关、可并行过渡路径采样，并在二维模型和聚合物系统中验证了性能提升。
AI 中文摘要
研究系统的动力学行为通常取决于对其在长寿命状态之间如何转变的表征。由于这类转变是罕见的，观察它们通常需要专门的增强采样技术。过渡路径采样（TPS）是一种成熟的生成反应轨迹的方法，它易于实现且无需预先定义反应坐标。然而，其效率受到其顺序性质以及采样路径之间相关性的限制。先前的工作通过将TPS与基于条件玻尔兹曼生成器的采样方案相结合来解决这一局限性，条件玻尔兹曼生成器是一种能够对给定目标概率分布进行采样的生成式机器学习模型。这种方法产生不相关的过渡路径，但依赖于准确的反应坐标，而该坐标通常事先未知。基于committor学习的最新进展，特别是分子机制发现人工智能（AIMMD）方法，在本工作中我们引入了GenAIMMD，一种迭代算法，它主动且自洽地学习理想反应坐标（committor），并训练条件玻尔兹曼生成器以沿其任意偏置窗口进行采样。因此，GenAIMMD提供了一种无相关且完全可并行化的路径采样方案，无需预先了解系统的转变机制。我们将GenAIMMD应用于一个二维玩具模型和一个更高维的聚合物系统。在这两种情况下，GenAIMMD都成功训练了玻尔兹曼生成器并学习了committor。基准测试结果表明，与标准TPS相比，性能显著提升。
英文摘要
Studying the dynamical behavior of a system often depends on characterizing how it transitions between long-lived states. Because such transitions are rare, observing them usually requires specialized enhanced sampling techniques. Transition Path Sampling (TPS) is a well-established method for generating reactive trajectories, which is simple to implement and does not require the definition of a preconceived reaction coordinate. However, its efficiency is limited by its sequential nature and the resulting correlations between sampled paths. Previous work addressed this limitation by combining TPS with a sampling scheme based on conditioned Boltzmann Generators, a generative machine learning model capable of sampling a given target probability distribution. This approach produces uncorrelated transition paths but relies on an accurate reaction coordinate, which is rarely known in advance. Building on recent advances in committor learning, specifically on the Artificial Intelligence for Molecular Mechanism Discovery (AIMMD) method, in this work we introduce GenAIMMD, an iterative algorithm that actively and self-consistently learns the ideal reaction coordinate (the committor) and trains a conditioned Boltzmann Generator to sample from arbitrary bias windows along it. GenAIMMD thereby provides a correlation-free and fully parallelizable path sampling scheme that does not require prior knowledge of the system's transition mechanism. We apply GenAIMMD to a two-dimensional toy model and a higher-dimensional polymer system. In both cases, GenAIMMD succeeds in training the Boltzmann Generator and learning the committor. Benchmark results show a substantial increase in performance compared to standard TPS.
