Physical insights from atomic-scale machine learning

Michele Ceriotti
École Polytechnique Fédérale de Lausanne (EPFL)

Surrogate models of quantum mechanical calculations have transformed the simulation
of matter at the atomic scale, by dramatically reducing its computational cost.
Machine-learning models, however, offer more than acceleration. By using models
that reflect the physical priors of the problem, and critically analyzing their
performance as a function of the hyperparameters, one can learn much about the
key structural features, and molecular interactions that determine the properties
of a material. I will present some examples of the application of this
"model introspection", and discuss how, more broadly, machine-learning models
can be interpreted in terms of fundamental physical concepts.

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