Performance Prediction of High-Entropy Perovskites La<sub>0.8</sub>Sr<sub>0.2</sub>Mn<sub>x</sub>Co<sub>y</sub>Fe<sub>z</sub>O<sub>3</sub> with Automated High-Throughput Characterization of Combinatorial Libraries and Machine Learning.
basic_science · Level V
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- Record sourced from PubMed, PMID 39498698.
- Also identified by DOI 10.1002/adma.202407372 and PMC identifier 11636067.
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Abstract
Perovskite oxides form a large family of materials with applications across various fields, owing to their structural and chemical flexibility. Efficient exploration of this extensive compositional space is now achievable through automated high-throughput experimentation combined with machine learning. In this study, we investigate the composition-structure-performance relationships of high-entropy La<sub>0.8</sub>Sr<sub>0.2</sub>Mn<sub>x</sub>Co<sub>y</sub>Fe<sub>z</sub>O<sub>3±𝞭</sub> perovskite oxides (0 < x, y, z <1; x+y+z≈1) for application as oxygen electrodes in Solid Oxide Cells. Following the deposition of a continuous compositional map using thin-film combinatorial pulsed laser deposition, compositional, structural, and performance properties are characterized using six different techniques with mapping capabilities. Random forests effectively model electrochemical performance, consistently identifying Fe-rich oxides as optimal compounds with the lowest area-specific resistance values for oxygen electrodes at 700 °C. Additionally, the models identify a statistical correlation between oxygen sublattice distortion-derived from spectral analysis of Raman-active modes-and enhanced performance.