<ul data-eligibleForWebStory="false"><li>Enhanced sampling techniques aim to observe rare events like state transitions in molecular dynamics simulations by using collective variables (CVs).</li><li>A new framework called TLC has been proposed to learn CVs directly from time-lagged conditions of a generative model, capturing slow dynamic behavior instead of just static information.</li><li>TLC was validated on the Alanine Dipeptide system for two CV-based enhanced sampling tasks, demonstrating superior performance compared to existing machine learning CV discovery methods.</li><li>The study introduces a novel approach to learning CVs that could improve accuracy in observing rare events in molecular dynamics simulations.</li></ul>

Learning Collective Variables from Time-lagged Generation

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