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Intra- and intermolecular potential energy surfaces derived from ab initio data by machine learning

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In recent years it has become possible to predict the properties of molecular materials using potential energy surfaces fitted only to ab initio calculations. With current methods and hardware it feasible to solve the Schroedinger equation at the CCSD level of accuracy for at most a few molecules at a time. Unfortunately this and other quantum chemistry methods scale steeply with system size [O(N7) for CCSD ] so in many cases cannot be applied. Much effort has been devoted to developing potential energy surfaces which map the accurate quantum mechanical results to functional forms which can be evaluated at vastly reduced expense. A number of methods exist for fitting these potentials, most of which require a great deal of expert knowledge, iterative improvement, or both. We use the recently developed method of Gaussian Approximation Potentials (GAP) to improve upon this process in several ways. GAP makes use of Gaussian Process regression, a principled, automatic, nonparametric, Bayesian approach to function fitting. We show how intra- and intermolecular potential energy surfaces for arbitrary molecules can be made automatically using GAP , and how these can be made systematically more accurate by including more training data.

This talk is part of the Electronic Structure Discussion Group series.

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