文献类型：期刊文献

英文题名：Rational Design of Perovskite Anode Materials in Solid Oxide Electrolysis Cells: Machine Learning-Assisted Prediction of Thermal Expansion Coefficients

作者：Wang, Ke[1];Lei, Ming[1];Zhou, Xing-Gui[1];Chen, De[2];Zhu, Yi-An[1]

机构：[1]East China Univ Sci & Technol, Sch Chem Engn, State Key Lab Green Chem Engn & Ind Catalysis, UNILAB, Shanghai 200237, Peoples R China;[2]Norwegian Univ Sci & Technol, Dept Chem Engn, N-7491 Trondheim, Norway

年份：2025

卷号：64

期号：21

起止页码：10508

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20252018436583);WOS:【SCI-EXPANDED(收录号:WOS:001490100200001)】；

基金：This work is supported by the National Key Research and Development Program of China (2024YFA1509901), the National Natural Science Foundation of China (22073027), and the research council of Norway through the innovation project for the industrial sector (340988, with Yara International).

语种：英文

外文关键词：Oxide semiconductors

摘要：Perovskite oxides have been recognized as promising electrode materials for solid oxide electrolysis cells. In this work, the phonon dispersion and thermal expansion coefficients (TECs) of LaBO3 (B = Sc-Cu), LaFe0.5B0.5O3 (B = Cr, Mn, Co, Ni), and LaCrO3-delta and LaFeO3-delta (delta = 0, 0.25, 0.5) have been studied by performing density functional perturbation theory calculations under the quasi-harmonic approximation. The calculated TECs agree well with experimental data, and LaFe0.5Co0.5O3 and LaFe0.5Ni0.5O3 have TECs close to those of the electrolyte yttria-stabilized zirconia (YSZ). A machine learning model is then developed to enable high-throughput screening of potential perovskite anode materials, where the data set is established based on our calculated TECs and experimentally reported values. The SHAP analysis indicates dominant factors governing the TEC include the cation radius and Mulliken electronegativity of the B-site element and the crystal gamma angle. Finally, 507 candidates compatible with YSZ are identified from 13,095 perovskite oxides.