Active Learning of Coarse Grained Models for Free Energy Surfaces

Coarse graining procedures serve as a primary tool for alleviating limitations of all-atom molecular dynamics relating to long simulation times and large system sizes, which make simulating dynamic phenomena in polymers and proteins prohibitive. Many approaches exist for modeling the resultant interactions, but the recent progress in machine learned force fields has motivated their application to so called “bottom up” coarse grained approaches that preserve the thermodynamic properties of the all-atom system. In the present work, we explore how Gaussian processes can be used in determining the optimal complexity of coarse grained free energy surfaces. The Gaussian process framework further allows for the possibility of active learning, which has been successful in applications to ab initio molecular dynamics. We explore the extension of this framework to coarse graining problems and discuss the issue of “fine graining,” where returning to the all-atom representation is a key element of the active learning process.