Active Learning for Machine Learning Driven Molecular Dynamics
Abstract
Machine learned coarse grained (CG) potentials are fast, but degrade over time when simulations reach undersampled biomolecular conformations, and generating widespread all atom (AA) data to combat this is computationally infeasible. We propose a novel active learning 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.