Augmenting Human Expertise in Weighted Ensemble Simulations through Deep Learning-Based Information Bottleneck.

ABSTRACT: The weighted ensemble (WE) method stands out as a widely used segment-based sampling technique renowned for its rigorous treatment of kinetics. The WE framework typically involves initially mapping the configuration space onto a low-dimensional collective variable (CV) space and then partitioning it into bins. The efficacy of WE simulations heavily depends on the selection of CVs and binning schemes. The recently proposed state predictive information bottleneck (SPIB) method has emerged as a promising tool for automatically constructing CVs from data and guiding enhanced sampling through an iterative manner. In this work, we advance this data-driven pipeline by incorporating prior expert knowledge. Our hybrid approach combines SPIB-learned CVs to enhance sampling in explored regions with e

SUBMITTER: Wang D 

PROVIDER: S-EPMC12085903 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature