With the development of high-efficiency gas turbine engines and increasing inlet temperatures, the performance of thermal barrier coatings (TBCs) for hot-section components has been more severely challenged. The doping of multi-element rare earth elements significantly improves the thermodynamic properties and chemical compatibility of thermal barrier coatings so that the application performance of coatings in high-temperature environments is enhanced considerably. In this work, the doped coatings prepared by REYSZ (RE = La, Sm, Nd) were investigated and characterized in terms of crystal structure, elastic properties, and thermal–mechanical properties based on the first-principles approach, combined with various empirical and semi-empirical formulations, and a predictive model for resistance to CMAS corrosion based on machine learning approaches. The results showed that the tetragonal phase REYSZ material was mechanically stable, had a large strain damage tolerance, and was not easy to fracture under applied loads and thermal shocks. In terms of CMAS corrosion resistance, the NdYSZ interfacial model had a lower surface energy (3.130 J/m2) and Griffith fracture energy (6.934 J/m2) compared with the conventional YSZ model, and Nd2O3 had the potential to improve the CMAS corrosion resistance of zirconia-based material for thermal barrier coatings. By evaluating the machine learning prediction models, the regression coefficients of the two algorithms were 0.9627 and 0.9740, and both these two prediction models showed high prediction accuracy and strong robustness. Ultimately, this work presented a novel mechanism–data hybrid method, which would facilitate the efficient development of TBC new materials for anti-CMAS corrosion.
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