材料科学
氧化物
电极
电解
钙钛矿(结构)
质子
纳米技术
化学工程
冶金
物理化学
电解质
化学
工程类
物理
量子力学
作者
Ning Wang,Baoyin Yuan,Fangyuan Zheng,Shanyun Mo,Xiaohan Zhang,Lei Du,Lixin Xing,Ling Meng,Lei Zhao,Yoshitaka Aoki,Chunmei Tang,Siyu Ye
标识
DOI:10.1002/adfm.202309855
摘要
Abstract Proton‐conducting solid oxide cells (P‐SOCs) as energy conversion devices for power generation and hydrogen production have attracted increasing attention recently. The lack of efficient proton‐conducting air electrodes is a huge obstacle to developing high‐performance P‐SOCs. The currently widely used air electrode material is Co/Fe based perovskite oxide, however, there is still no systematic research on studying and comparing the roles of diversiform elements at the B site for Co/Fe based perovskite oxide. Here, a machine learning (ML) model with eXtreme Gradient Boosting (XGBoost) algorithm is built to quickly and accurately predict the proton absorption amount of Co/Fe based perovskite oxides with 27 elements dopant at B site. Hereafter, La(Co 0.9 Ni 0.1 )O 3 (LCN91) is screened by a combination of the ML model and the density functional theory calculation. Finally, LCN91 is applied to the air electrode of P‐SOC, and the cell exhibits excellent electrochemical performances in fuel cell and electrolysis modes. The current study not only provides a useful model for screening air electrodes of P‐SOC, but also extends the application of ML in exploring the key materials for P‐SOCs and other fuel cells/electrolyzers.
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