Perspective on optimal strategies of building cluster expansion models for configurationally disordered materials

Cluster expansion (CE) provides a general framework for first-principles-based theoretical modeling of multicomponent materials with configurational disorder, which has achieved remarkable success in the theoretical study of a variety of material properties and systems of different nature. On the other hand, there remains a lack of consensus regarding what is the optimal strategy to build CE models efficiently that can deliver accurate and robust prediction for both ground state energetic properties and statistical thermodynamic properties at finite temperature. There have been continuous efforts to develop more effective approaches to CE model building, which are further promoted by recent tremendous interest of applying machine learning techniques in materials research. In this Perspective, we present a critical review of recent methodological developments in building CE models for multicomponent materials, with particular focus on different approaches and strategies proposed to address cluster selection and training data generation. We comment on the pros and cons of different methods in a general formalism and present some personal views on the prospects of theoretical approaches to multicomponent materials.

Automated summary

Cluster expansion (CE) is a theoretical modeling framework based on first-principles that is used to study multicomponent materials with configurational disorder. However, there is still a lack of consensus on efficient strategies for building CE models that can accurately predict both ground state energetic properties and statistical thermodynamic properties at finite temperature. This article critically reviews recent methodological developments in building CE models for multicomponent materials, focusing on cluster selection and training data generation. Personal views on the prospects of theoretical approaches to multicomponent materials are also presented.