Interpretability In Atomic-Scale Machine Learning

I will provide a brief overview of some of the established frameworks used to apply machine-learning techniques to the atomistic modeling of matter, and in particular to the construction of surrogate models for quantum mechanical calculations. I will focus in particular on the construction of physics-aware descriptors of the atomic structure - based on symmetrized correlations of the atom density - and how they facilitate the interpretation of regression and classification models based on them.