Computer simulations are increasingly acquiring the necessary speed and accuracy to tackle rational materials design. The screening of many compounds requires transferable force fields, so as to alleviate tedious parametrization efforts for every new compound. In this talk, I will describe efforts aimed at optimizing classical intermolecular potentials that do away with a manual parametrization of every new molecule. By a combination of a machine-learning-based prediction of atomic properties, coupled with specific physics-based interactions, we achieve accurate interaction-energy decompositions as compared to SAPT for a wide range of small organic molecules: static multipole electrostatics, polarization, many-body dispersion, and pairwise repulsion.
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