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Gaussian process regression in molecular modelling

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The talk will summarise our recent work in using Gaussian process regression (a form of Bayesian non-parametric inference) to model moderate to high dimensional functions in chemistry. Applications range from constructing interatomic potentials (i.e. parametrisations of the Born-Oppenheimer potential energy surface) starting from total energy electronic structure calculations, to free energy surface reconstruction based on umbrella sampling trajectories. In every case studied so far, a careful treatment of data representation and hyperparameters leads to huge increases in computational efficiency and model fidelity.

This talk is part of the Isaac Newton Institute Seminar Series series.

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