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 La0.8Sr0.2MnxCoyFezO3±𝞭 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.
References:
[1] Bozal-Ginesta C, Sirvent J, Cordaro G, Fearn S, Pablo-García S, Chiabrera F, Choi C, Laa L, Núñez M, Cavallaro A, Buzi F, Aguadero A, Dezanneau G, Kilner J, Morata A, Baiutti F, Aspuru-Guzik A & Tarancón A 2024, ‘Performance Prediction of High-Entropy Perovskites La0.8Sr0.2MnxCoyFezO3 with Automated High-Throughput Characterization of Combinatorial Libraries and Machine Learning‘, Advanced materials, 36 – 50 -2407372
[2] Database on high-entropy perovskites La0.8Sr0.2MnxCoyFezO3±𝞭, https://nanoionicshub.github.io/LSMCF_database/
