{"ID":2868234,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.17208","arxiv_id":"2509.17208","title":"Active Learning for Machine Learning Driven Molecular Dynamics","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.","short_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 i...","url_abs":"https://arxiv.org/abs/2509.17208","url_pdf":"https://arxiv.org/pdf/2509.17208v3","authors":"[\"Kevin Bachelor\",\"Sanya Murdeshwar\",\"Daniel Sabo\",\"Razvan Marinescu\"]","published":"2025-09-21T19:26:32Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"physics.atm-clus\"]","methods":"[]","has_code":false}