Augmenting Human Expertise in Weighted Ensemble Simulations through Deep Learning based Information Bottleneck

TL;DR

This paper introduces a hybrid approach combining deep learning-derived collective variables with expert knowledge to improve weighted ensemble simulations, enhancing sampling efficiency and interpretability.

Contribution

It advances the SPIB method by integrating expert knowledge, leading to better sampling guidance and analysis in weighted ensemble simulations.

Findings

Improved sampling of states of interest in benchmark systems.

Reduced run-to-run variance in simulations.

Enhanced identification of metastable states and pathways.

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 expert-based CVs to guide exploration in regions of interest, synergizing the strengths of both methods. Through benchmarking on alanine dipeptide and chignoin systems, we demonstrate that our hybrid approach effectively guides WE simulations to sample states of interest, and reduces run-to-run variances. Moreover, our integration of the SPIB model also enhances the analysis and interpretation of WE simulation data by effectively identifying metastable states and pathways, and offering direct visualization of dynamics.