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Abstract
Designing environmental barrier coatings (EBCs) often involves incorporating multiple rare-earth (RE) components into β- and γ-RE₂Si₂O₇ to optimize performance. This study uses decision fusion, a machine learning (ML) method, to accurately predict multicomponent RE disilicates. The ML models successfully evaluated the phase formation capability of 117 unreported (RE₁₀.₂₅RE₂₀.₂₅Yb₀.₂₅Lu₀.₂₅)₂Si₂O₇ and (RE₁₁/₆RE₂₁/₆RE₃₁/₆Gd₁/₆Yb₁/₆Lu₁/₆)₂Si₂O₇ compositions, validated by first-principles calculations. Model visualization identified key factors governing formation, including average RE³⁺ radius and variations in RE³⁺ combinations for (RE₁₀.₂₅RE₂₀.₂₅Yb₀.₂₅Lu₀.₂₅)₂Si₂O₇, and average mass and electronegativity deviation for (RE₁₁/₆RE₂₁/₆RE₃₁/₆Gd₁/₆Yb₁/₆Lu₁/₆)₂Si₂O₇. This work integrates ML and multicomponent RE disilicate formation mechanisms for efficient superior material design.
Publisher
npj Computational Materials
Published On
May 07, 2024
Authors
Yun Fan, Yuelei Bai, Qian Li, Zhiyao Lu, Dong Chen, Yuchen Liu, Wenxian Li, Bin Liu
Tags
environmental barrier coatings
machine learning
rare-earth disilicates
phase formation
material design

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