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Machine learning potentials always extrapolate, it does not matter

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If you have a question about this talk, please contact Dr Christoph Schran.

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Machine learning (ML) potentials for atomistic systems infer the mapping between configurations and a target objective function, e.g., the total energy of the system and/or the forces acting on each atom. We show that, contrary to popular assumptions, predictions from machine learning potentials built upon atom-density representations almost exclusively occur in an extrapolation regime – i.e., in regions of the representation space which lie outside the convex hull defined by the training set points. We then propose a perspective to rationalise the domain of robust extrapolation and accurate prediction of atomistic machine learning potentials in terms of the probability density induced by training points in the representation space.

This talk is part of the Lennard-Jones Centre series.

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