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The surface properties of solid-state materials often dictate theirfunctionality, especially for applications where nanoscale effects becomeimportant. The relevant surface(s) and their properties are determined, inlarge part, by the materials synthesis or operating conditions. Theseconditions dictate thermodynamic driving forces and kinetic rates responsiblefor yielding the observed surface structure and morphology. Computationalsurface science methods have long been applied to connect thermochemicalconditions to surface phase stability, particularly in the heterogeneouscatalysis and thin film growth communities. This review provides a briefintroduction to first-principles approaches to compute surface phase diagramsbefore introducing emerging data-driven approaches. The remainder of the reviewfocuses on the application of machine learning, predominantly in the form oflearned interatomic potentials, to study complex surfaces. As machine learningalgorithms and large datasets on which to train them become more commonplace inmaterials science, computational methods are poised to become even morepredictive and powerful for modeling the complexities of inorganic surfaces atthe nanoscale.

Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials