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 (Y2/7Yb5/7)(Ta1/2Nb1/2)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(Ta1/2Nb1/2)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.
[1] 赵云松, 张迈, 戴建伟, 等. 航空发动机涡轮叶片热障涂层研究进展[J]. 材料导报, 2023, 37(6): 77-83. ZHAO Yunsong, ZHANG Mai, DAI Jianwei, et al.Research progress of thermal barrier coatings for aeroengine turbine blades[J]. Materials Review, 2023, 37(6): 77-83. [2] 赵娟利, 杨岚, 张成冠, 等. 热障涂层材料研究进展[J]. 现代技术陶瓷, 2020, 41(3): 148-170. ZHAO Juanli, YANG Lan, ZHANG Chengguan, et al.Recent progress in thermal barrier coatings[J]. Advanced Ceramics, 2020, 41(3): 148-170. [3] RAHIMI S, SHARIFIANJAZI F, ESMAEILKHANIAN A, et al.Effect of SiO2 content on Y-TZP/Al2O3 ceramic-nanocomposite properties as potential dental applications[J]. Ceramics International, 2020, 46(8): 10910-10916. [4] BROIDO D A, MALORNY M, BIRNER G, et al.Intrinsic lattice thermal conductivity of semiconductors from first principles[J]. Applied Physics Letters, 2007, 91(23): 457-462. [5] FAN Z, PEREIRA L F C, WANG H Q, et al. Force and heat current formulas for many-body potentials in molecular dynamics simulations with applications to thermal conductivity calculations[J]. Physical Review B, 2015, 92(9): 094301. [6] LUO Y, LI M, YUAN H, et al.Predicting lattice thermal conductivity via machine learning: a mini review[J]. NPJ Computational Materials, 2023, 9(1): 4. [7] JAAFREH R, KANG Y S, HAMAD K.Lattice thermal conductivity: an accelerated discovery guided by machine learning[J]. ACS Applied Materials & Interfaces, 2021, 13(48): 57204-57213. [8] CHAKRABORTY P, LIU Y D, MA T F, et al.Quenching thermal transport in aperiodic superlattices: a molecular dynamics & machine learning study[J]. ACS Applied Materials & Interfaces, 2020, 12(7): 8795-8804. [9] RACCUGLIA P, ELBERT K C, ADLER P D F, et al. Machine-learning-assisted materials discovery using failed experiments[J]. Nature, 2016, 533(7601): 73-76. [10] QIN J C, LIU Z F, MA M S, et al.Machine learning-assisted materials design and discovery of low-melting-point inorganic oxides for low-temperature cofired ceramic applications[J]. ACS Sustainable Chemistry & Engineering, 2022, 10(4): 1554-1564. [11] CHEN L, FENG J.Thermal and mechanical properties optimization of ABO4 type EuNbO4 by the B-site substitution of Ta[J]. Engineering, 2020, 6(2): 178-185. [12] CHEN L, LI B H, FENG J.Rare-earth tantalates for next-generation thermal barrier coatings[J]. Progress in Materials Science, 2024, 144: 101265. [13] WU P, CHONG X Y, WU F S, et al.Investigation of the thermophysical properties of (Y1-xYbx)TaO4 ceramics[J]. Journal of the European Ceramic Society, 2020, 40(8): 3111-3121. [14] Royal Society of Chemistry. Periodic table[Z/OL]. (2024-09) [2024-10-29]. https://www.rsc.org/periodic-table. [15] Chemical Aid. Periodic table[Z/OL]. (2024-06-09)[2024- 10-29]. https://periodictable.chemicalaid.com. [16] The Materials Project. Crystal structure[DB/OL]. (2024-10- 25) [2024-10-29]. https://materialsproject.org. [17] SCHLICHTING K W, PADTURE N P, KLEMENS P G.Thermal conductivity of dense and porous yttria-stabilized zirconia[J]. Materal Science, 2001, 36(12): 3003-3010. [18] CHEN L, WANG J K, LI B H, et al.Simultaneous manipulations of thermal expansion and conductivity in symbiotic ScTaO4/SmTaO4 composites via multiscale effects[J]. Journal of Advanced Ceramics, 2023, 12(8): 1625-1640. [19] WANG J, CHONG X Y, ZHOU R, et al.Microstructure and thermal properties of RETaO4 (RE=Nd, Eu, Gd, Dy, Er, Yb, Lu) as promising thermal barrier coating materials[J]. Scripta Materialia, 2017, 126: 24-28. [20] WANG J, ZHENG Q, SHI X L, et al.Microstructural evolution and thermal-physical properties of YTaO4 coating after high-temperature exposure[J]. Surface and Coatings Technology, 2023, 456: 129222. [21] WU F S, WU P, ZHOU Y X, et al.The thermo‐mechanical properties and ferroelastic phase transition of RENbO4 (RE=Y, La, Nd, Sm, Gd, Dy, Yb) ceramics[J]. Journal of the American Ceramic Society, 2020, 103(4): 2727-2740. [22] CHEN L, WU P, SONG P, et al.Potential thermal barrier coating materials: RE3NbO7 (RE=La, Nd, Sm, Eu, Gd, Dy) ceramics[J]. Journal of the American Ceramic Society, 2018, 101(10): 4503-4508. [23] WANG J, WU F S, ZOU R A, et al.High‐entropy ferroelastic rare‐earth tantalite ceramic: (Y0.2Ce0.2Sm0.2Gd0.2Dy0.2)TaO4[J]. Journal of the American Ceramic Society, 2021, 104(11): 5873-5882. [24] CHEN L, LI B H, LUO K R, et al.Origins of high fracture toughness and glass‐like thermal conductivity in Zr-Ta-O composites[J]. Journal of the American Ceramic Society, 2022, 105(11): 6508-6516. [25] ZHU J T, XU J, ZHANG P, et al.Enhanced mechanical and thermal properties of ferroelastic high-entropy rare-earth- niobates[J]. Scripta Materialia, 2021, 200: 113912. [26] WU P, CHONG X Y, FENG J.Effect of Al3+ doping on mechanical and thermal properties of DyTaO4 as promising thermal barrier coating application[J]. Journal of the American Ceramic Society, 2018, 101(5): 1818-1823. [27] XUE T, ZHANG Y A, GAO Y, et al.Synthesis and thermophysical properties of (AlxY1-x)TaO4 ceramics as thermal barrier coating materials[J]. Ceramics International, 2023, 49(16): 27577-27588. [28] WU P, HU M Y, CHEN L, et al.Investigation on microstructures and thermo-physical properties of ferroelastic (Y1-xDyx)TaO4 ceramics[J]. Materialia, 2018, 4: 478-486. [29] ZHANG P, FENG Y J, LI Y, et al.Thermal and mechanical properties of ferroelastic RENbO4 (RE=Nd, Sm, Gd, Dy, Er, Yb) for thermal barrier coatings[J]. Scripta Materialia, 2020, 180: 51-56. [30] WANG P P, WANG J, CAI H Y, et al.Effect of multi-component at the A site on the thermophysical properties of high entropy niobates[J]. Journal of the European Ceramic Society, 2024, 44(5): 2954-2964. [31] WANG J, CHONG X Y, LV L, et al.High-entropy ferroelastic (10RE0.1)TaO4 ceramics with oxygen vacancies and improved thermophysical properties[J]. Journal of Materials Science & Technology, 2023, 157: 98-106. [32] DINSDALE A T.SGTE data for pure elements[J]. Calphad, 1991, 15(4): 317-425. [33] GAN M D, LAI L P, WANG J K, et al.Suppressing the oxygen-ionic conductivity and promoting the phase stability of the high-entropy rare earth niobates via Ta substitution[J]. Journal of Materials Science & Technology, 2025, 209: 79-94. [34] CHEN L, HU M Y, FENG J.Defect-dominated phonon scattering processes and thermal transports of ferroelastic (Sm1-xYbx)TaO4 solid solutions[J]. Materials Today Physics, 2023, 35: 101118. [35] LUO Y, CHEN L, WU P, et al.Synthesis and thermophysics properties of ferroelastic SmNb1-xTaxO4 ceramics[J]. Ceramics International, 2018, 44(12): 13999-14006. [36] 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016. ZHOU Zhihua.Machine Learning[M]. Beijing: Tsinghua University Press, 2016. [37] YANG J S, ZHAO C Y, YU H T, et al.Use GBDT to predict the stock market[J]. Procedia Computer Science, 2020, 174: 161-171. [38] NICHOLLS J R, LAWSON K J, JOHNSTONE A, et al. Methods to reduce the thermal conductivity of EB-PVD TBCs[J]. Surface and Coatings Technology, 2002, 151/152: 383-391. [39] 黄昆. 固体物理学[M]. 北京: 高等教育出版社, 1988. HUANG Kun.Solid State Physics[M]. Beijing: Higher Education Press, 1988. [40] LECUN Y, BENGIO Y, HINTON. Deep learning[J]. Nature, 2015, 521: 436-444. [41] ZHANG X, LI H.A robust kernel principal component analysis method and its applications[J]. Neurocomputing, 2020, 395: 191-201. [42] GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al.Generative adversarial networks[J]. Communications of the ACM, 2020, 63(11): 139-144. [43] TSAI M H, YEH J W.High-entropy alloys: a critical review[J]. Materials Research Letters, 2014, 2(3): 107-123. [44] PARKER W J, JENKINS R J, BUTLER C P, et al.Flash method of determining thermal diffusivity, heat capacity, and thermal conductivity[J]. Journal of Applied Physics, 1961, 32(9): 1679-1684. [45] LEITNER J, VOŇKA P, SEDMIDUBSKÝ D, et al. Application of Neumann-Kopp rule for the estimation of heat capacity of mixed oxides[J]. Thermochimica Acta, 2010, 497(1/2): 7-13.
2025, Vol. 30 
