Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials

Abstract

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.

References in corpus (11)

• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• Machine learning for molecular simulation
• Nested sampling for physical scientists
• Anisotropic work function of elemental crystals
• Experimental and theoretical study of oxygen adsorption structures on Ag(111)
• CO oxidation on Pd(100) at technologically relevant pressure conditions: A first-principles kinetic Monte Carlo study
• Predicting interface structures: From SrTiO${}_3$ to graphene
• AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning
• Selective formation of metastable polymorphs in solid-state synthesis
• Grain Boundary Properties of Elemental Metals
• Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials

Cited by in corpus (3)

• $\Delta$-model correction of Foundation Model based on the models own understanding
• ViPErLEED package I: Calculation of $I(V)$ curves and structural optimization
• Confusion-driven machine learning of structural phases of a flexible, magnetic Stockmayer polymer