External Potential Ensembles to Improve the Learning of Transferable Coarse-Grained Potentials

Simulation of complex, heterogeneous molecular systems requires models that are transferable across environmental conditions. However, current bottom-up strategies for parametrizing accurate coarse-grained (CG) models often rely on explicitly targeting the selected thermodynamic quantities (measured from experiments or atomistic simulations), which can be difficult to obtain. We argue that this information limitation can be overcome by coarse graining using thermodynamically informative ensembles, where variables conjugate to the thermodynamic variables of interest are allowed to fluctuate. We demonstrate this approach by using external potential ensembles with the relative entropy optimization to parametrize highly coarse grained models of solvent mixtures from atomistic force fields. The CG models can reproduce the activity coefficient of the atomistic model to within 0.1kT accuracy across the entire composition range, without explicitly measuring and matching chemical potentials during the parameterization process. This approach allows for the efficient transfer of exciting improvements of atomistically detailed models to fast, coarse-grained simulations of macro-scale systems.