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Active Learning for Machine Learning Driven Molecular Dyn...
[Submitted on 21 Sep 2025 (v1), last revised 27 May 2026 (this v · 2026-05-29 · via cs.LG updates on arXiv.org

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Abstract:Machine-learned coarse-grained (CG) potentials are fast, but degrade over time when simulations reach under-sampled bio-molecular conformations, and generating widespread all-atom (AA) data to combat this is computationally infeasible. We propose a novel active learning (AL) framework for CG neural network potentials in molecular dynamics (MD). Building on the CGSchNet model, our method employs root mean squared deviation (RMSD)-based frame selection from MD simulations in order to generate data on-the-fly by querying an oracle during the training of a neural network potential. This framework preserves CG-level efficiency while correcting the model at precise, RMSD-identified coverage gaps. By training CGSchNet, a coarse-grained neural network potential, we empirically show that our framework explores previously unseen configurations and trains the model on unexplored regions of conformational space. Our active learning framework enables a CGSchNet model trained on the Chignolin protein to achieve a 33.05\% improvement in the Wasserstein-1 (W1) metric in Time-lagged Independent Component Analysis (TICA) space on an in-house benchmark suite.

Submission history

From: Kevin Bachelor [view email]
[v1] Sun, 21 Sep 2025 19:26:32 UTC (1,135 KB)
[v2] Mon, 17 Nov 2025 07:47:36 UTC (1,155 KB)
[v3] Wed, 27 May 2026 23:24:47 UTC (1,155 KB)