Accelerating the prediction of inorganic surfaces with machine learning
  interatomic potentials
Kyle Noordhoek Christopher J. Bartel
The surface properties of solid-state materials often dictate their
functionality, especially for applications where nanoscale effects become
important. The relevant surface(s) and their properties are determined, in
large part, by the materials synthesis or operating conditions. These
conditions dictate thermodynamic driving forces and kinetic rates responsible
for yielding the observed surface structure and morphology. Computational
surface science methods have long been applied to connect thermochemical
conditions to surface phase stability, particularly in the heterogeneous
catalysis and thin film growth communities. This review provides a brief
introduction to first-principles approaches to compute surface phase diagrams
before introducing emerging data-driven approaches. The remainder of the review
focuses on the application of machine learning, predominantly in the form of
learned interatomic potentials, to study complex surfaces. As machine learning
algorithms and large datasets on which to train them become more commonplace in
materials science, computational methods are poised to become even more
predictive and powerful for modeling the complexities of inorganic surfaces at
the nanoscale.