Compositional design and phase formation capability of high-entropy rare-earth disilicates from machine learning and decision fusion

A key strategy for designing environmental barrier coatings is to incorporate multiple rare-earth (RE) components into β- and γ-RE₂Si₂O₇ to achieve multifunctional performance optimization. However, the polymorphic phase presents significant challenges for the design of multicomponent RE disilicates. Here, employing decision fusion, a machine learning (ML) method is crafted to identify multicomponent RE disilicates, showcasing notable accuracy in prediction. The well-trained ML models evaluated the phase formation capability of 117 (RE₁₀.₂₅RE₂₀.₂₅Yb₀.₂₅Lu₀.₂₅)₂Si₂O₇ and (RE₁₁/₆RE₂₁/₆RE₃₁/₆Gd₁/₆Yb₁/₆Lu₁/₆)₂Si₂O₇, which are unreported in experiments and validated by first-principles calculations. Utilizing model visualization, essential factors governing the formation of (RE₁₀.₂₅RE₂₀.₂₅Yb₀.₂₅Lu₀.₂₅)₂Si₂O₇ are pinpointed, including the average radius of RE³⁺ and variations in different RE³⁺ combinations. On the other hand, (RE₁₁/₆RE₂₁/₆RE₃₁/₆Gd₁/₆Yb₁/₆Lu₁/₆)₂Si₂O₇ must take into account the average mass and the electronegativity deviation of RE³⁺. This work combines material-oriented ML methods with formation mechanisms of multicomponent RE disilicates, enabling the efficient design of superior materials with exceptional properties for the application of environmental barrier coatings.

Yun Fan, Yuelei Bai, Qian Li, Zhiyao Lu, Dong Chen, Yuchen Liu, Wenxian Li, Bin Liu