The development of enhanced sampling methods to investigate rare but important events has always been a focal point in the molecular simulation field. Such methods often rely on prior knowledge of the reaction coordinate. However, the search for this reaction coordinate is at the heart of the rare event problem. Transition path sampling (TPS) circumvents this problem by generating an ensemble of dynamical trajectories undergoing the activated event. The reaction coordinate is extracted from the resulting path ensemble using variants of machine learning, making it an output of the method instead of an input. Over the last 20 years, since its inception, many extensions of TPS have been developed. Perhaps surprisingly, large‐scale TPS simulations on complex molecular systems have become possible only recently. Other important developments include the transition interface sampling (TIS) methodology to compute rate constants, the application to multiple states, and adaptive path sampling. The development of OpenPathSampling and PyRETIS has enabled easy and flexible use and implementation of these and other novel path sampling algorithms. In this progress report, a brief overview of recent developments, novel algorithms, and software is given. In addition, several application areas are discussed, and a future outlook for the next decade is given.

中文翻译：

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过渡路径采样作为轨迹的马尔可夫链蒙特卡洛：最新算法，软件，应用程序和未来展望

增强采样方法以研究稀有但重要的事件一直是分子模拟领域的研究重点。这样的方法通常依赖于反应坐标的先验知识。但是，搜索此反应坐标是罕见事件问题的核心。过渡路径采样（TPS）通过生成经过激活事件的动态轨迹的集合来解决此问题。使用机器学习的变体从结果路径集合中提取反应坐标，使其成为方法的输出而不是输入。自其诞生以来，在过去的20年中，已经开发了许多TPS扩展。也许令人惊讶的是，直到最近，在复杂分子系统上进行大规模TPS模拟才成为可能。其他重要的发展包括用于计算速率常数的过渡接口采样（TIS）方法，应用于多种状态以及自适应路径采样。OpenPathSampling和PyRETIS的开发使这些以及其他新颖的路径采样算法可以轻松灵活地使用和实现。在此进度报告中，简要概述了最近的发展，新颖的算法和软件。此外，还讨论了几个应用领域，并给出了下一个十年的未来展望。简要概述了最近的发展，新颖的算法和软件。此外，还讨论了几个应用领域，并给出了下一个十年的未来展望。简要概述了最近的发展，新颖的算法和软件。此外，还讨论了几个应用领域，并给出了下一个十年的未来展望。