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Force Fields and Potential Energy Surfaces

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There is a long tradition in computational chemistry of representing the Born-Oppenheimer potential energy surface of molecules and clusters of molecules using empirical force fields on the one hand, and also, for small systems, using systematic expansions that have essentially arbitrary accuracy. The formalism of “machine learning” (non-parametric function fitting in high dimensions) unites these approaches. New kinds of parametrisations are the result, with diverse applications and computational expense in between that of simple force fields and quantum chemistry. Recent work in my group include potentials for methane, amorphous carbon, as well as regression of molecular properties.

This talk is part of the Theory - Chemistry Research Interest Group series.

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