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Co-expression of multi-genes for polynary perovskite electrocatalysts for reversible solid oxide cells

Xiaoxin Zhang, Hongyuan He, Yu Chen, Guangming Yang, Xiao Xiao, Haiping Lv, Yongkang Xiang, Shuxiong Wang, Chang Jiang, Jianhui Li, Zhou Chen, Subiao Liu, Ning Yan, Yong Xue, Abdullah N. Alodhayb, Yuanming Pan, Ning Chen, Jinru Lin, Xin Tu, Zongping Shao, Yifei Sun

2025Nature Communications25 citationsDOIOpen Access PDF

Abstract

High-entropy LnBaCo2O5+δ perovskites are explored as rSOC air electrodes, though high configuration entropy (Sconfig) alone poorly correlates with performance due to multifactorial interactions. We systematically engineer LnBaCo2O5+δ perovskites (Ln = lanthanides) with tunable Sconfig and 20 consistent parameters, employing Bayesian-optimized symbolic regression to decode activity descriptors. The model identifies synergistic contributions from Sconfig, ionic radius, and electronegativity, enabling screening of 177,100 compositions. Three validated oxides exhibit superior activity/durability, particularly (Pr0.05La0.4Nd0.2Sm0.1Y0.25)BaCo2O5+δ, showing enhanced oxygen vacancy concentration and disordered transport pathways. First-principles studies reveal optimized charge transfer kinetics via cobalt-oxygen bond modulation. Further, the interplay between first ionization energy, atomic mass, and ionic Lewis acidity dictates stability. This data-driven approach establishes a quantitative framework bridging entropy engineering and catalytic functionality in complex oxides. Polynary perovskite oxides are promising air electrodes for rSOCs, with configuration entropy (Sconfig) often linked to reactivity. However, high Sconfig alone does not strongly correlate with performance. This study develops a model identifying key descriptors to guide high-activity oxide discovery.

Topics & Concepts

Perovskite (structure)OxideGeneMaterials scienceCell biologyChemistryNanotechnologyBiologyBiochemistryCrystallographyMetallurgyElectrocatalysts for Energy ConversionAdvancements in Solid Oxide Fuel CellsMachine Learning in Materials Science