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

Abstract

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.