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.

固态材料的表面性质对其功能起着重要作用，表面性质是由材料合成或操作条件决定的，计算表面科学方法和机器学习算法在研究复杂的纳米尺度的无机表面具有很大的潜力。