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
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.
Authors
- Giulio Cordaro (ORCID: https://orcid.org/0000-0003-3042-6160)
- John A. Kilner (ORCID: https://orcid.org/0000-0002-6520-2055)
- Fjorelo Buzi (ORCID: https://orcid.org/0000-0002-4366-9287)
- Francesco Chiabrera (ORCID: https://orcid.org/0000-0001-8940-2708)
- Albert Tarancón (ORCID: https://orcid.org/0000-0002-1933-2406)
- Sergio Pablo‐García (ORCID: https://orcid.org/0000-0002-3327-9285)
- Àlex Morata (ORCID: https://orcid.org/0000-0002-3300-4636)
- Sarah Fearn (ORCID: https://orcid.org/0000-0002-9798-8398)
- Marc Núñez (ORCID: https://orcid.org/0000-0002-6898-198X)
- Guilhem Dezanneau (ORCID: https://orcid.org/0000-0003-1937-0581)
- Federico Baiutti (ORCID: https://orcid.org/0000-0001-9664-2486)
- Andrea Cavallaro (ORCID: https://orcid.org/0000-0002-6688-1643)
- Alán Aspuru‐Guzik (ORCID: https://orcid.org/0000-0002-8277-4434)
- Ainara Aguadero (ORCID: https://orcid.org/0000-0001-7098-1033)
- Juan de Dios Sirvent (ORCID: https://orcid.org/0000-0001-6999-7505)
- Changhyeok Choi (ORCID: https://orcid.org/0000-0002-7181-3100)
- Carlota Bozal‐Ginesta (ORCID: https://orcid.org/0000-0001-7299-5869)
- Lisa Laa (ORCID: https://orcid.org/0000-0001-7959-6115)
Institutions
- Canadian Institute for Advanced Research (CA)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Centre National de la Recherche Scientifique (FR)
- Kyushu University (JP)
- University of Toronto (CA)
- Université Paris-Saclay (FR)
- Structural Genomics Consortium (CA)
- CentraleSupélec (FR)
- Instituto de Ciencia de Materiales de Madrid (ES)
- Vector Institute (CA)
- Institut de Recerca en Energia de Catalunya (ES)
- Imperial College London (GB)
Publication Details
- Journal
- Advanced Materials
- Published
- 2024-11-05
- DOI
- https://doi.org/10.1002/adma.202407372
- Citations
- 14
- Primary Topic
- Electronic and Structural Properties of Oxides
- Type
- article
- Field-Weighted Citation Impact
- 1.64
Funders
- U.S. Department of Energy
- University of Minnesota
- European Commission
- Agence Nationale de la Recherche
- Generalitat de Catalunya
- Office of Science
- HORIZON EUROPE Framework Programme
- Natural Resources Canada
- HORIZON EUROPE Marie Sklodowska-Curie Actions