Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials

Kyle Noordhoek, Christopher J. Bartel

Research output: Contribution to journalReview articlepeer-review

3 Scopus citations


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 material's 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.

Original languageEnglish (US)
Pages (from-to)6365-6382
Number of pages18
Issue number13
StatePublished - Mar 6 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024 The Royal Society of Chemistry.

PubMed: MeSH publication types

  • Journal Article
  • Review


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