arXiv Machine Learning By Zichen Liu, Tiejun Li

FES-FM: Free Energy Surface Sampling via Reduced Flow Matching

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arXiv:2605. 00337v2 Announce Type: replace Abstract: Sampling the distribution of collective variables (CVs) and estimating the associated free energy surface are crucial problems in statistical physics, as they underpin a better understanding of chemical reactions and conformational transitions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Models

arXiv:2602. 16634v2 Announce Type: replace-cross Abstract: The rare-event sampling problem has long been the central limiting factor in molecular dynamics (MD), especially in biomolecular simulation.

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Reactive Flux Matching: Mechanism Discovery and Adaptive Sampling of Rare Events

Path sampling methods generate ensembles of reactive trajectories connecting metastable states, but extracting mechanistic insight from these data remains nontrivial. We introduce Flux Matching, a framework that learns two complementary objects directly from reactive trajectory data: a current velocity $u(z)$, whose streamlines trace the dominant reaction pathways, and a scalar potential $h(z)$, obtained from a weighted Helmholtz-Hodge decomposition of the reactive current, that serves as a data-driven reaction coordinate.