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Computational molecular sciences face three major challenges: model quality (i.e., force fields), data explosion, and nonetheless inadequate sampling of the most interesting events. Machine learning approaches hold much promise in all three areas. Our work concerns the latter two: how to extract useful information from large amounts of trajectory data, and how to steer the sampling to maximize the amount of useful information. We will report on our progress towards a fully autonomous, data-driven production and interpretation of molecular dynamics trajectories by combining machine learning and enhanced sampling methods. Host: Angel E. Garcia |