Department of Materials Science and Engineering , Johns Hopkins University , Baltimore , Maryland 21218 , United States.
J Chem Inf Model. 2018 Dec 24;58(12):2401-2413. doi: 10.1021/acs.jcim.8b00413. Epub 2018 Oct 3.
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.
基于第一性原理计算的团簇展开的构建是计算材料性质的有力工具。在本观点中，我们讨论了团簇展开在表面和纳米材料中的应用。我们回顾了团簇展开形式主义的基本原理以及如何使用机器学习来提高团簇展开的预测准确性。我们强调了团簇展开在表面和纳米材料中的几个代表性应用，展示了团簇展开如何帮助研究人员建立结构-性质关系，并实现合理设计，以加速新材料的发现。还讨论了团簇展开的潜在应用和未来挑战。
