  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.
