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journal article·2024-12Open access

Performance Prediction of High‐Entropy Perovskites La 0.8 Sr 0.2 Mn x Co y Fe z O 3 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 Kilner, Alex Morata, Federico Baiutti, Alán Aspuru‐Guzik, Albert Tarancón

Advanced Materials· Volume 36· Issue 50
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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.
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Research context

Electronic materialsMaterials processing

OpenAlex topics: Electronic and Structural Properties of Oxides · Advancements in Solid Oxide Fuel Cells · Magnetic and transport properties of perovskites and related materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Nanoionics and Fuel Cells group Catalonia Institute for Energy Research Jardins de Les Dones de Negre 1 Sant Adrià de Besòs Barcelona 08930 Spain

Departments of Chemistry and Computer Science University of Toronto Lash Miller Chemical Laboratories 80 St George Street Toronto Ontario M5S 3H6 Canada

Laboratoire Structures Propriétés et Modélisation des Solides UMR8580 CentraleSupelec CNRS Université Paris‐Saclay 3 Rue Joliot‐Curie Gif Sur Yvette 91190 France

Department of Materials Imperial College London London SW7 2BP United Kingdom

Instituto de Ciencia de Materiales de Madrid (ICMM‐CSIC) Sor Juana Ines de la Cruz 3 Madrid 28049 Spain

International Institute for Carbon‐Neutral Energy Research (I2CNER) Kyushu University Fukuoka 819‐0395 Japan

Departments of Chemical Engineering and Applied Chemistry, and Materials Science and Engineering University of Toronto Toronto M5S3E5 Canada

Acceleration Consortium University of Toronto Toronto M5G1X6 Canada

Canadian Institute for Advanced Research (CIFAR) and Vector Institute for Artificial Intelligence Toronto Ontario M5G1M1 Canada

ICREA Passeig Lluís Companys 23 Barcelona 08010 Spain

Funding

Agence Nationale de la Recherche · HORIZON EUROPE Framework Programme · HORIZON EUROPE Marie Sklodowska-Curie Actions · Generalitat de Catalunya

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