Performance Prediction of High‐Entropy Perovskites La0.8Sr0.2MnxCoyFezO3 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 learni...

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Veröffentlicht in:	Advanced materials (Weinheim) 2024-11, Vol.36 (50), p.e2407372-n/a
Hauptverfasser:	Bozal‐Ginesta, Carlota, Sirvent, Juande, Cordaro, Giulio, Fearn, Sarah, Pablo‐García, Sergio, Chiabrera, Francesco, Choi, Changhyeok, Laa, Lisa, Núñez, Marc, Cavallaro, Andrea, Buzi, Fjorelo, Aguadero, Ainara, Dezanneau, Guilhem, Kilner, John, Morata, Alex, Baiutti, Federico, Aspuru‐Guzik, Alán, Tarancón, Albert
Format:	Artikel
Sprache:	eng
Schlagworte:	Combinatorial analysis Electrochemical analysis Electrodes Entropy high entropy oxides high‐throughput experimentation Machine learning Oxides Oxygen Performance enhancement Performance prediction perovskite oxides Perovskites Physics Pulsed laser deposition Pulsed lasers solid oxide fuel cells Spectrum analysis Statistical correlation Structural analysis Thin films
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Zusammenfassung:	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
ISSN:	0935-9648 1521-4095 1521-4095
DOI:	10.1002/adma.202407372