Performance Prediction of High‐Entropy Perovskites La0.8Sr0.2MnxCoyFezO3 with Automated High‐Throughput Characterization of Combinatorial Libraries and Machine Learning

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

doi:10.1002/adma.202407372

Performance Prediction of High‐Entropy Perovskites La_0.8Sr_0.2Mn_xCo_yFe_zO₃ with Automated High‐Throughput Characterization of Combinatorial Libraries and Machine Learning

Journal Article · Tue Nov 05 00:00:00 EST 2024 · Advanced Materials

DOI:https://doi.org/10.1002/adma.202407372· OSTI ID:3010674

^[1]; Sirvent, Juande ^[2]; Cordaro, Giulio ^[3]; Fearn, Sarah ^[4]; Pablo‐García, Sergio ^[5]; Chiabrera, Francesco ^[2]; Choi, Changhyeok ^[5]; Laa, Lisa ^[2]; Núñez, Marc ^[2]; Cavallaro, Andrea ^[4]; Buzi, Fjorelo ^[2]; Aguadero, Ainara ^[6]; Dezanneau, Guilhem ^[3]; Kilner, John ^[7]; Morata, Alex ^[2]; Baiutti, Federico ^[2]; Aspuru‐Guzik, Alán ^[5]; Tarancón, Albert ^[2]

Catalonia Institute for Energy Research, Barcelona (Spain); University of Toronto, ON (Canada)
Catalonia Institute for Energy Research, Barcelona (Spain)
Université Paris-Saclay, Gif-sur-Yvette (France)
Imperial College London (United Kingdom)
University of Toronto, ON (Canada)
Imperial College London (United Kingdom); Instituto de Ciencia de Materiales de Madrid (ICMM-CSIC) (Spain)
Imperial College London (United Kingdom); Kyushu University, Fukuoka (Japan)

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_0.8Sr_0.2Mn_xCo_yFe_zO_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.

View Accepted Manuscript (DOE)

Research Organization:: University of Minnesota, Minneapolis, MN (United States); University of Toronto, ON (Canada)

Sponsoring Organization:: European Union's Horizon 2020 research and innovation program; Marie Skłodowska Curie; Marie Skłodowska Curie Actions Postdoctoral Fellowship; USDOE; USDOE Office of Science (SC)

Grant/Contract Number:: SC0023454

OSTI ID:: 3010674

Alternate ID(s):: OSTI ID: 3009695

Journal Information:: Advanced Materials, Journal Name: Advanced Materials Journal Issue: 50 Vol. 36; ISSN 1521-4095; ISSN 0935-9648

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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References (73)

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