Exploring high-performance environmental barrier coatings for rare earth silicates: A combined approach of first principles calculations and machine learning

Abstract

RE2Si2O7 is promising materials for environmental barrier coating (EBC), but the vast phase space poses challenges for the screening of RE2Si2O7. It follows that a combined approach of first principles calculations and machine learning is proposed for this problem, with establishing a comprehensive database comprising β-, γ- and δ-RE2Si2O7 (RE=La–Lu, Y, Sc) and correlating their mechanical/thermal properties on structural characteristics. It is revealed the [O3Si–O–SiO3] structure and polyhedron distortion affect mechanical properties of RE2Si2O7, while criteria for selecting RE2Si2O7 with low thermal conductivity are identified, including complex crystal structures, chemical bond inhomogeneity, and strong non-harmonic lattice vibrations. Also, the machine learning model accurately predicts the coefficient of thermal expansion (CTE) and minimum thermal conductivity (λmin) of RE2Si2O7, with volume and mass variations identified as critical factors, respectively. This integrated approach efficiently screens RE2Si2O7 for EBC application and enables rapid assessments of their thermal properties.