Artificial-intelligence (AI) and, in particular, machine-learning (ML) modelling is substantially increasing the reach and predictive power of material-science simulations. Such strategies are adopted for two broad classes of applications: a) surrogate modelling of materials properties, e.g., learning energies and forces of given atomic configurations, where the Hamiltonian is known but computationally intensive to evaluate (Refs. 1 and 2 and references therein), and b) materials genomics, i.e., the identification of the features that can explain and be used to model certain materials' property (the genes for that material and property), together with fitting of a predictive model for the given property as function of the identified genes (Refs. 3 and references therein).