Vervust Wouter, Zhang Daniel T, Ghysels An, Roet Sander, van Erp Titus S, Riccardi Enrico
IBiTech-BioMMedA Group, Ghent University, Ghent, Belgium.
Department of Chemistry, Norwegian University of Science and Technology, Trondheim, Norway.
J Comput Chem. 2024 Jun 5;45(15):1224-1234. doi: 10.1002/jcc.27319. Epub 2024 Feb 12.
We present and discuss the advancements made in PyRETIS 3, the third instalment of our Python library for an efficient and user-friendly rare event simulation, focused to execute molecular simulations with replica exchange transition interface sampling (RETIS) and its variations. Apart from a general rewiring of the internal code towards a more modular structure, several recently developed sampling strategies have been implemented. These include recently developed Monte Carlo moves to increase path decorrelation and convergence rate, and new ensemble definitions to handle the challenges of long-lived metastable states and transitions with unbounded reactant and product states. Additionally, the post-analysis software PyVisa is now embedded in the main code, allowing fast use of machine-learning algorithms for clustering and visualising collective variables in the simulation data.
我们展示并讨论了PyRETIS 3所取得的进展,这是我们用于高效且用户友好的罕见事件模拟的Python库的第三个版本,专注于使用副本交换过渡界面采样(RETIS)及其变体来执行分子模拟。除了将内部代码进行全面重新编写以实现更模块化的结构外,还实施了几种最近开发的采样策略。这些策略包括最近开发的蒙特卡罗移动,以提高路径去相关性和收敛速度,以及新的系综定义,以应对长寿命亚稳态以及反应物和产物状态无界的转变所带来的挑战。此外,后分析软件PyVisa现在已嵌入到主代码中,允许快速使用机器学习算法对模拟数据中的集体变量进行聚类和可视化。