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
Carlota Bozal‐Ginesta, Juande Sirvent, Giulio Cordaro, Sarah Fearn, Sergio Pablo‐García, Francesco Chiabrera, Changhyeok Choi, Lisa Laa, Marc Núñez, Andrea Cavallaro, Fjorelo Buzi, Ainara Aguadero, Guilhem Dezanneau, John A. Kilner, Àlex Morata, Federico Baiutti, Alán Aspuru‐Guzik, Albert Tarancón
Abstract
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 0.8 Sr 0.2 Mn x Co y Fe z O 3±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.