# Atomic-Scale Representation and Statistical Learning of Tensorial Properties

@article{Grisafi2019AtomicScaleRA, title={Atomic-Scale Representation and Statistical Learning of Tensorial Properties}, author={Andrea Grisafi and David M. Wilkins and Michael J. Willatt and M. Ceriotti}, journal={ACS Symposium Series}, year={2019} }

This chapter discusses the importance of incorporating three-dimensional symmetries in the context of statistical learning models geared towards the interpolation of the tensorial properties of atomic-scale structures. We focus on Gaussian process regression, and in particular on the construction of structural representations, and the associated kernel functions, that are endowed with the geometric covariance properties compatible with those of the learning targets. We summarize the general…

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