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arXiv Machine Learning By Alireza Omidi, Jiajun He, J\"org Gsponer, Saifuddin Syed

METALICA: METAdynamics and repLICA exchange for enhanced diffusion sampling

Read the original on arXiv Machine Learning →

METALICA is a new method that combines Metadynamics with Replica Exchange on pretrained diffusion models to improve sampling of rare conformational states in proteins. It builds a bias potential along a chosen Collective Variable, pushes new samples away from previously visited ones, and reweights them to recover the unbiased distribution. By running one replica per diffusion level and allowing inter‑replica communication, METALICA can generate long Markov chains that uncover rare events, as demonstrated on a bimodal test system and on protein unfolding where it reveals a second free‑energy minimum that other methods miss.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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