Open AccessA Minimal, Adaptive Binning Scheme for Weighted Ensemble Simulations
Author(s) -
Paul A Torrillo,
Anthony Bogetti,
Lillian T. Chong
Publication year - 2021
Publication title -
the journal of physical chemistry. a/the journal of physical chemistry. a.
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.756
H-Index - 235
eISSN - 1520-5215
pISSN - 1089-5639
DOI - 10.1021/acs.jpca.0c10724
Subject(s) - sampling (signal processing) , scheme (mathematics) , adaptive sampling , computer science , algorithm , sampling scheme , division (mathematics) , path (computing) , energy (signal processing) , mathematics , statistics , monte carlo method , computer vision , mathematical analysis , arithmetic , filter (signal processing) , estimator , programming language
A promising approach for simulating rare events with rigorous kinetics is the weighted ensemble path sampling strategy. One challenge of this strategy is the division of configurational space into bins for sampling. Here we present a minimal adaptive binning (MAB) scheme for the automated, adaptive placement of bins along a progress coordinate within the framework of the weighted ensemble strategy. Results reveal that the MAB binning scheme, despite its simplicity, is more efficient than a manual, fixed binning scheme in generating transitions over large free energy barriers, generating a diversity of pathways, estimating rate constants, and sampling conformations. The scheme is general and extensible to any rare-events sampling strategy that employs progress coordinates.