基于机器学习筛选低热导率稀土钽/铌酸盐材料

doi:10.19976/j.cnki.43-1448/TF.2024109

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Abstract Rare earth tantalate/niobate (RE(Ta/Nb)O₄) materials have attracted significant attention as promising candidates for next-generation thermal barrier coatings due to their excellent properties. The machine learning was used to analyze the thermal conductivity of RE(Ta/Nb)O₄ (RE=Sc, Y, Yb, Dy, Gd, Sm, Ho, La, Lu, Tm, Er, Ce, Eu) materials, and combined a greedy algorithm to identify materials with lower thermal conductivity for thermal barrier coatings in this study. Using feature parameters such as element composition, atomic properties and parameters, crystal structure information, and thermodynamic data, the gradient boosting decision tree model was employed for material screening and validated through experiments. The results show that the experimental values of (Y_2/7Yb_5/7)(Ta_1/2Nb_1/2)O₄ align well with the predicted model, and gradient boosting decision tree proves to be an effective machine learning model for future thermal conductivity predictions. Several low thermal conductivity RE(Ta_1/2Nb_1/2)O₄ thermal barrier coating materials are successfully identified. Co-doping with multiple rare earth elements and high-entropy RE(Ta/Nb)O₄ exhibit superior thermal performance compared to certain single- component RE(Ta/Nb)O₄ materials, making them promising new materials for thermal barrier coatings.

Key words： machine learning thermal conductivity rare earth tantalate/niobate material design gradient boosting decision tree generative adversarial network

Received: 13 December 2024 Published: 28 July 2025

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	LIAO Mengting
	LI Junli
	PI Zhipeng
	ZHANG Fan

Cite this article:

LIAO Mengting,LI Junli,PI Zhipeng, et al. Screening of low thermal conductivity rare earth tantalate/niobate materials based on machine learning[J]. Materials Science and Engineering of Powder Metallurgy, 2025, 30(3): 179-192.

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http://pmbjb.csu.edu.cn/EN/10.19976/j.cnki.43-1448/TF.2024109 OR http://pmbjb.csu.edu.cn/EN/Y2025/V30/I3/179

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