首页|Discovering the ultralow thermal conductive A2B2O7-type high-entropy oxides through the hybrid knowledge-assisted data-driven machine learning

Discovering the ultralow thermal conductive A2B2O7-type high-entropy oxides through the hybrid knowledge-assisted data-driven machine learning

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Lattice engineering and distortion have been considered one kind of effective strategies for discovering advanced materials.The instinct chemical flexibility of high-entropy oxides(HEOs)motivates/accelerates to tailor the target properties through phase transformations and lattice distortion.Here,a hybrid knowledge-assisted data-driven machine learning(ML)strategy is utilized to discover the A2B2O7-type HEOs with low thermal conductivity(κ)through 17 rare-earth(RE=Sc,Y,La-Lu)solutes optimized A-site.A designing routine integrating the ML and high throughput first principles has been proposed to predict the key physical parameter(KPPs)correlated to the targeted K of advanced HEOs.Among the smart-designed 6188(5RE0.2)2Zr2O7 HEOs,the best candidates are addressed and validated by the princi-ples of severe lattice distortion and local phase transformation,which effectively reduce K by the strong multi-phonon scattering and weak interatomic interactions.Particularly,(Sc0.2Y0.2La0.2Ce0.2Pr0.2)2Zr2O7 with predicted κ below 1.59 Wm-1 K-1 is selected to be verified,which matches well with the ex-perimental κ=1.69 Wm1 K-1 at 300 K and could be further decreased to 0.14 Wm-1 K-1 at 1473 K.Moreover,the coupling effects of lattice vibrations and charges on heat transfer are revealed by the cross-validations of various models,indicating that the weak bonds with low electronegativity and few bond-ing charge density and the lattice distortion(r)identified by cation radius ratio(rA/rB)should be the KPPs to decrease K efficiently.This work supports an intelligent designing strategy with limited atomic and electronic KPPs to accelerate the development of advanced multi-component HEOs with proper-ties/performance at multi-scales.

High-entropy oxidesThermal conductivityPyrochloreKey physical parameterFirst-Principles

Ying Zhang、Ke Ren、William Yi Wang、Xingyu Gao、Ruihao Yuan、Jun Wang、Yiguang Wang、Haifeng Song、Xiubing Liang、Jinshan Li

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State Key Laboratory of Solidification Processing,Northwestern Polytechnical University,Xi'an 710072,China

Innovation Center,NPU Chongqing,Chongqing 401135,China

Institute of Advanced Structure Technology,Beijing Institute of Technology,Haidian District,Beijing 100081,China

Laboratory of Computational Physics,Institute of Applied Physics and Computational Mathematics,Beijing 100088,China

Defense Innovation Institute,Academy of Military Sciences of the PLA of China,Beijing 100071,China

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National defense basic scientific researchNational defense basic scientific research

2022-JCKY-JJ-1086211-CXCY-N103-03-04-00

2024

10.1016/j.jmst.2023.05.060

材料科学技术(英文版)
中国金属学会 中国材料研究学会 中国科学院金属研究所

材料科学技术(英文版)

CSTPCD
影响因子：0.657
ISSN：1005-0302
年,卷(期)：2024.168(1)
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