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

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−δ and LaFeO3−δ (δ = 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.