On committor functions in milestoning

Author:

Ji Xiaojun12ORCID,Wang Ru3ORCID,Wang Hao3ORCID,Liu Wenjian3ORCID

Affiliation:

1. Research Center for Mathematics and Interdisciplinary Sciences, Shandong University 1 , Qingdao, Shandong 266237, People’s Republic of China

2. Frontiers Science Center for Nonlinear Expectations (Ministry of Education), Shandong University 2 , Qingdao, Shandong 266237, People’s Republic of China

3. Qingdao Institute for Theoretical and Computational Sciences, Institute of Frontier and Interdisciplinary Science, Shandong University 3 , Qingdao, Shandong 266237, People’s Republic of China

Abstract

As an optimal one-dimensional reaction coordinate, the committor function not only describes the probability of a trajectory initiated at a phase space point first reaching the product state before reaching the reactant state but also preserves the kinetics when utilized to run a reduced dynamics model. However, calculating the committor function in high-dimensional systems poses significant challenges. In this paper, within the framework of milestoning, exact expressions for committor functions at two levels of coarse graining are given, including committor functions of phase space point to point (CFPP) and milestone to milestone (CFMM). When combined with transition kernels obtained from trajectory analysis, these expressions can be utilized to accurately and efficiently compute the committor functions. Furthermore, based on the calculated committor functions, an adaptive algorithm is developed to gradually refine the transition state region. Finally, two model examples are employed to assess the accuracy of these different formulations of committor functions.

Funder

Natural Science Foundation of Shandong Province

Shandong University

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Kinetic Network in Milestoning: Clustering, Reduction, and Transition Path Analysis;Journal of Chemical Theory and Computation;2024-06-17

2. Collective Variable-Based Enhanced Sampling: From Human Learning to Machine Learning;The Journal of Physical Chemistry Letters;2024-02-08

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