WELP : Weighted Ensemble in the LooP for committor learning

Understanding how complex molecular systems transition between metastable states is a central challenge in computational chemistry and biophysics. A key tool for characterizing these transitions is the committor function, defined as the probability that a trajectory initiated from a given configuration reaches one state before another, which encodes detailed mechanistic information about transition pathways. First, we develop a "WE in the loop" framework that combines a self consistent loss function with weighted ensemble (WE) simulation: WE is used as enhanced sampling to learn the committor, which in turn is used as a better collective variable for more WE. Second, we curate a public benchmark of empirically determined committor values for alanine dipeptide. Across a two-channel 2D system, alanine dipeptide and chignolin settings, our WE-based framework consistently outperforms greedy baselines, demonstrating the importance of principled exploration. Together, the benchmark and framework provide both a standard for evaluating committor learning methods and a new method for studying rare transitions in high-dimensional molecular systems.