Data-driven parameterization of coarse-grained models of soft materials using machine learning tools

Many advances have been made in coarse-grained (CG) models of polymers to reduce computational effort yet capture the relaxation behavior imparted by hierarchal structure, resulting in two primary classes: “bottom-up” methods which preserve chemical-specificity and “top-down” methods which reproduce physical properties. Here, we combine a bottom-up coarse-grained model with a dissipative potential to obtain a chemically specific, thermodynamically consistent, and dynamically correct model. We parametrize the conservative forces using the iterative Boltzmann inversion (IBI) method to develop a CG force field from short all-atom (AA) simulations to recover AA structure, and thus, thermodynamics. We employ machine learning and filtering techniques to produce smooth distributions that enable automation and rapid convergence to smooth force profiles. We develop a similar approach for parameterization of the dissipative potential to correct the dynamics of the IBI-generated force field. In this method, we match AA diffusivity as a proxy for tuning monomeric friction. We demonstrate this method for oligomers in the melt state. Efforts to develop these methods into complementary automated packages will be discussed.