[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122048-en":3,"doc-seo-122048-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122048,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Performance Prediction of High-Entropy Perovskites - La0.8Sr0.2MnxCoyFezO3 with Automated High-Throughput Characterization of Combinatorial Libraries and Machine Learning","Performance Prediction of high-entropy perovskite oxides targets oxygen-electrode behavior for solid oxide cells by mapping composition, structure, and electrochemical response across La0.8Sr0.2MnxCoyFezO3±δ (0\u003Cx,y,z\u003C1; x+y+z≈1). A thin-film combinatorial pulsed laser deposition strategy generates a continuous compositional map, followed by multi-technique characterization with mapping capability. Random-forest models capture electrochemical performance and consistently select Fe-rich oxides as optimal at 700 °C, minimizing area-specific resistance. Raman-active mode spectral analysis shows a statistical link between oxygen sublattice distortion and enhanced performance.","RESEARCH ARTICLE  \n[www.advmat.de](www.advmat.de)  \nPerformance Prediction of High-Entropy Perovskites  \nLa0.8 Sr0.2 MnxCoy FezO3 with Automated High-Throughput Characterization of Combinatorial Libraries and Machine Learning  \nCarlota Bozal-Ginesta,* Juande Sirvent, Giulio Cordaro, Sarah Fearn, Sergio Pablo-García,  \nFrancesco Chiabrera, Changhyeok Choi, Lisa Laa, Marc Núñez, Andrea Cavallaro, Fjorelo Buzi, Ainara Aguadero, Guilhem Dezanneau, John Kilner, Alex Morata, Federico Baiutti, Alán Aspuru-Guzik,* and Albert Tarancón*  \nPerovskite 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 La0.8Sr0.2 MnxCoy FezO3±􀀂 perovskite oxides (0 \u003C x, y, z \u003C1; 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.  \n1. Introduction  \nMixed ionic electronic conductors (MIECs), such as certain perovskite oxides (ABO3 ), are essential for achieving high cathodic oxygen reduction in intermediatetemperature solid oxide fuel cells (ITSOFCs, T \u003C 700 °C) . [1–4] Due to their high ionic conductivity and the consequent increase in the active area, these MIEC materials have progressively replaced electrodes based on noble metals and electronconducting oxides like La1x Srx MnO3 (LSM),[5,6] which were originally employed at higher operating temperatures. Currently, lanthanum strontium perovskite oxide MIECs containing cobalt and iron in the B-site, La1-xSrx (Cox Fey )O3±􀀂 (0 \u003C x, y \u003C1; x+y ≈1), are the most established cathode material for IT-SOFC applications. [7–10]  \nC. Bozal-Ginesta, J. Sirvent, F. Chiabrera, L. Laa, M. Núñez, F. Buzi,  \nA. Morata, F. Baiutti, A. Tarancón Nanoionics and Fuel Cells group Catalonia Institute for Energy Research Jardins de Les Dones de Negre 1  \nSant Adrià de Besòs, Barcelona 08930, Spain  \nE-mail: [carlota.bozalginesta@empa.ch](carlota.bozalginesta@empa.ch); [atarancon@irec.cat](atarancon@irec.cat)  \nC. Bozal-Ginesta, S. Pablo-García, C. Choi, A. Aspuru-Guzik Departments of Chemistry and Computer Science University of Toronto  \nLash Miller Chemical Laboratories  \n80 St George Street, Toronto, Ontario M5S 3H6, Canada E-mail: [aspuru@utoronto.ca](aspuru@utoronto.ca)  \nThe ORCID identification number(s) for the author(s) of this article  \ncan be found under [https://doi.org/10.1002/adma.202407372](https://doi.org/10.1002/adma.202407372)[ ](https://doi.org/10.1002/adma.202407372)© 2024 The Author(s). Advanced Materials published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.  \nDOI: 10.1002/adma.202407372  \nG. Cordaro, G. Dezanneau Laboratoire Structures  \nPropriétés et Modélisation des Solides UMR8580  \nCentraleSupelec CNRS  \nUniversité Paris-Saclay  \n3 Rue Joliot-Curie, Gif Sur Yvette 91190, France  \nS. Fearn, A. Cavallaro, A. Aguadero, J. Kilner Department of Materials  \nImperial College London  \nLondon SW7 2BP, United Kingdom  \nA. Aguadero  \nI","cbCain3HmzuICaRg","https://ap.wps.com/l/cbCain3HmzuICaRg","pdf",2433015,1,12,"English","en",105,"# Introduction\n## Mixed ionic electronic conductors and cathode relevance\n## Need for quantitative composition–structure–performance correlations\n# Materials and combinatorial mapping approach\n# Characterization workflow and mapping techniques\n# Machine-learning modeling and performance prediction\n# Electrochemical descriptors and Raman-based distortion correlation","[{\"question\":\"How are high-entropy perovskite compositions mapped in the study?\",\"answer\":\"A continuous compositional map is created using thin-film combinatorial pulsed laser deposition, covering La0.8Sr0.2MnxCoyFezO3±δ compositions with 0\\u003cx,y,z\\u003c1 and x+y+z≈1.\"},{\"question\":\"Which modeling method is used to predict electrochemical performance?\",\"answer\":\"Random forests are used to model electrochemical performance from the characterized compositional and structural information.\"},{\"question\":\"What relationship is found between Raman analysis and oxygen-electrode performance?\",\"answer\":\"Spectral analysis of Raman-active modes yields oxygen sublattice distortion, and the machine-learning models show a statistical correlation between this distortion and improved oxygen-electrode performance.\"}]","Performance Prediction of High-Entropy Perovskites - La0.8Sr0.2MnxCoyFezO3 with Automated High-Throughput Characterization of Combinatorial Libraries and Machine Learning | PDF",1785808562,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"performance-prediction-of-high-entropy-perovskites-la08sr02mnxcoyfezo3-with-automated-high-throughput-characterization-of-combinatorial-libraries-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/performance-prediction-of-high-entropy-perovskites-la08sr02mnxcoyfezo3-with-automated-high-throughput-characterization-of-combinatorial-libraries-and-machine-learning/122048/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How are high-entropy perovskite compositions mapped in the study?","Question",{"text":75,"@type":76},"A continuous compositional map is created using thin-film combinatorial pulsed laser deposition, covering La0.8Sr0.2MnxCoyFezO3±δ compositions with 0\u003Cx,y,z\u003C1 and x+y+z≈1.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which modeling method is used to predict electrochemical performance?",{"text":80,"@type":76},"Random forests are used to model electrochemical performance from the characterized compositional and structural information.",{"name":82,"@type":73,"acceptedAnswer":83},"What relationship is found between Raman analysis and oxygen-electrode performance?",{"text":84,"@type":76},"Spectral analysis of Raman-active modes yields oxygen sublattice distortion, and the machine-learning models show a statistical correlation between this distortion and improved oxygen-electrode performance.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]