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Machine learned force fields and potential energy surfaces

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If you have a question about this talk, please contact Joseph Nelson.

There is a long tradition in computational chemistry and materials science of representing the Born-Oppenheimer potential energy surface of molecules, clusters of molecules and extended materials 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 a computational expense in between that of simple force fields and quantum chemistry, and leading to diverse applications. Notable work in my group include potentials for amorphous carbon, silicon with defects, other difficult elemental systems such as boron and phosphorus, as well as regression of molecular properties. I will outline my vision for the bright future of force field modelling.

This talk will be held online using Zoom. Please register your email address here to receive Zoom links via email.

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This talk is part of the MSM-AIMR Joint Online Workshop 2020 series.

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