Active Learning of Many-Body Transferable Coarse Grained Interactions in Polymers

Despite its widespread use in atomistic modeling, classical molecular dynamics becomes computationally intractable at length and time scales that are of great interest. Coarse graining methods allow fast degrees of freedom to be integrated out of the all-atom system, alleviating both the need to use a small time step and the cost of tracking all degrees of freedom. Bottom-up coarse graining techniques, wherein the thermodynamic properties of the underlying all-atom system are preserved, have seen increased attention with the recent progress in machine learned force fields for ab initio applications. In this work, we extend the idea of Gaussian process based on-the-fly active learning schemes applied to all-atom systems to coarse-grained applications of hydrocarbon liquids. We explore how the inherent interpretability of Gaussian process parameters give novel insight into the learning of coarse grained models, as well as how the active learning framework introduces the possibility of making coarse-grained models more transferable to chemically similar systems.