Discovery of Descriptors for Stable Monolayer Oxide Coatings through Machine Learning

Monolayer metal oxides (MMOs) can provide tunable chemical properties dictated by choice of the support and coating metal oxide. Experimental discovery of (meta)stable coating/support combinations can be accelerated if stability could be predicted based on component physical properties. For such complex systems, machine learning approaches can help to discover underlying principles that dictate system properties. Herein, we use a supervised machine learning (ML) method, regressed against density functional theory-calculated monolayer stabilities, to predict physical properties that are predictive of metal oxide monolayer stability. Monolayer oxide coatings are considered in two classes: (1) "stoichiometric" coatings, in which the monolayer oxide has a stable phase at the same MO_x stoichiometry as the substrate, and (2) "nonstoichiometric" coatings. Our ML approach indicates that substrate surface energy, orbital radii, and ionization energies are important for stability of stoichiometric MMOs. The parent oxide stability of the coating material as well as oxidation state differences between coating and support are important descriptors of stability of nonstoichiometric MMOs.