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The Use of Cluster Expansions To Predict the Structures and Properties of Surfaces and Nanostructured Materials

The construction of cluster expansions parametrized by first-principles calculations is a powerful tool for calculating properties of materials. In this Perspective, we discuss the application of cluster expansions to surfaces and nanomaterials. We review the fundamentals of the cluster expansion formalism and how machine learning is used to improve the predictive accuracy of cluster expansions. We highlight several representative applications of cluster expansions to surfaces and nanomaterials, demonstrating how cluster expansions help researchers build structure-property relationships and enable rational design to accelerate the discovery of new materials. Potential applications and future challenges of cluster expansions are also discussed.