Yuelei Bai

Abstract As a rapid, scalable, and eco-friendly synthesis method, the self-propagating high-temperature synthesis (SHS) is widely applied for ceramics, however, whose development is always limited by high experimental costs. To address this challenge, a workflow for its feasibility is established by combining first-principles calculations and machine learning to quickly predict SHS reactions of MAX and MAB phases.

Supporting Information Filename Description jace70388-sup-0001-SupMat.docx1.6 MB Supporting Information REFERENCES 1, , , , , , et al. Phase formation capability and compositional design of β-phase multiple rare-earth principal component disilicates. Nat Commun. 2023; 14: 1275. https://doi.org/10.1038/s41467-023-36947-6 2, , , , . CMAS-phobic and infiltration-inhibiting protective layer material for thermal barrier coatings. J Adv Ceram. 2024; 13: 1254–1267.

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.