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Learning quantum physics

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If you have a question about this talk, please contact Zoubin Ghahramani.

I will report on our efforts to apply machine learning techniques to the problem of describing the behaviour of atoms. For a number of atoms at given positions, quantum mechanics gives an expression for the total energy and hence the forces on every atom. The calculation of this energy is extremely expensive, and a minor industry exists that tries, with limited success, to model the energy function using oversimplified empirical interatomic potential functions (e.g. Lennard-Jones potential) which can be evaluated very quickly. We try to bridge the 8 orders of magnitude speed gap between the methods, while maintaining the accuracy of quantum mechanics.

This talk is part of the Machine Learning @ CUED series.

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