Computational screening of structurally and thermodynamically stable high‐entropy oxides with a perovskite structure

Abstract High‐entropy disordered solid solutions, including various carbides, nitrides, borides, sulfides, silicides, and oxides, have attracted considerable attention due to their unique properties. These materials exhibit significant property variations driven by increased entropy contributions to Gibbs free energy and lattice distortion. High‐entropy ceramics (HEC) are particularly promising for applications such as high‐temperature coatings, catalysts, and thermoelectric materials. This paper presents an algorithm designed to predict the structural and thermodynamic stability of high‐entropy oxides (HEOs) with a perovskite‐like structure. The algorithm calculates structural factors for individual metals and excludes elements that do not form perovskite structures. Specific calculations with defined tolerance and octahedral factors are employed to ensure stability. This method may be extended to predict the stability of various high‐entropy materials beyond oxides. We emphasize the importance of predicting highly entropic compounds, addressing the limitations of existing methods and the growing reliance on quantum chemical calculations and machine learning. By introducing a new algorithmic approach, this study aims to fill the data gap for high‐entropy compounds and minimize the need for empirical screening of metal combinations. The findings highlight the algorithm's potential to advance material science research and development.