Active Learning of Coarse Grained Force Fields with Gaussian Process Regression

Many physically relevant spatial and temporal scales remain inaccessible with leading MD techniques, especially in soft matter and composite materials. Consequently, it is common practice to replace the all-atom representation with effective beads using coarse graining approaches. However, development of coarse grained force fields is a laborious procedure, and available force fields tend to lose orientation information, making all-atom reconstruction, or fine-graining, difficult. We propose a novel machine learning method for automatically constructing coarse grained force fields by active learning. In addition these force fields contain predictive uncertainty and allow for fine-grain reconstruction. We demonstrate that Gaussian Process Regression can alleviate the need for large initial all-atom trajectories that are generally required for achieving thermodynamically consistent results in the latent space. Moreover, we will discuss the performance of such models in the context of a variety of molecular systems possessing different amounts of rotational symmetry.