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原文摘要

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: the probability that a trajectory initiated from a given configuration reaches one state before another. Because the committor encodes both the progress and the mechanism of a transition, it is an ideal collective variable (CV). Learning it, however, requires sampling the rare transitions it describes. We address this circular dependency with a ``WE-in-the-loop'' framework that couples committor learning with weighted ensemble (WE), an enhanced sampling method that uses a CV to drive transition dynamics. Our framework alternates between the two: WE simulation generates transition data for training a neural network committor with a self-consistent loss, and the learned committor then serves as an improved CV for subsequent WE simulation. Across a two-channel 2D system, alanine dipeptide, and chignolin, our framework consistently outperforms greedy baselines, highlighting the importance of principled exploration. We also curate a public benchmark of empirically determined committor values for alanine dipeptide. Together, the framework and benchmark provide a new method for studying rare transitions in high-dimensional molecular systems and a standard for evaluating committor learning methods.