[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290810-105":59,"doc-detail-290810-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","advancing-perovskite-photovoltaic-technology-through-machine-learning-driven-automation","Advancing perovskite photovoltaic technology through machine learning-driven automation","","Perovskite photovoltaic technology has progressed rapidly since 2009, raising power conversion efficiencies from 3.8% to over 26%, yet long-term material and device stability and reproducible, user-independent workflows remain major obstacles. The review explains why traditional trial-and-error experiments and manual analysis are inefficient, and how automated acceleration platforms can improve efficiency, consistency, and error control. 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It also highlights remaining stability challenges despite this progress.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Why are traditional trial-and-error and manual analysis approaches inadequate?",{"text":117,"@type":113},"They are inefficient and labor-intensive, especially when addressing multi-objective optimization across large multidimensional composition and parameter spaces. The document emphasizes the need to process large datasets and optimize complex parameter spaces more effectively.",{"name":119,"@type":110,"acceptedAnswer":120},"How does machine learning-driven automation support perovskite solar research?",{"text":121,"@type":113},"It enables intelligent experimental preselection and accelerates workflows for new transport material discovery, composition screening, and device preparation optimization. The approach aims to streamline the pipeline from materials to improved device performance.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290810,1789642607,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":46,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":31,"preview_page_count":4},1099514067438,"https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542","DOI: 10.1002/inf2.70005  \nREVI EW AR TICLE  \nAdvancing perovskite photovoltaic technology through machine learning-driven automation  \nJiyun Zhang 1,2  | Jianchang Wu 1,2 | Vincent M. Le Corre 1,2,3 | Jens A. Hauch 1,2 | Yicheng Zhao 4  | Christoph J. Brabec 1,2  \n1High Throughput Methods in Photovoltaics, Forschungszentrum Jülich GmbH, Helmholtz-Institute Erlangen-Nürnberg (HI ERN), Erlangen, Germany  \n2Faculty of Engineering, Department of Material Science, Institute of Materials for Electronics and Energy Technology (i-MEET), FriedrichAlexander-University Erlangen-Nuremberg (FAU), Erlangen, Germany  \n3Center for Advanced Photovoltaics and Thin Film Energy Devices (SDU CAPE), University of Southern Denmark, Mads Clausen Institute, Sønderborg, Denmark  \n4State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Electronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, the People’s Republic of China  \nCorrespondence  \nJiyun Zhang and Christoph J. Brabec, Forschungszentrum Jülich GmbH, Helmholtz-Institute Erlangen-Nürnberg (HI ERN), High Throughput Methods in Photovoltaics, Immerwahrstraße 2, 91058 Erlangen, Germany.  \nEmail: [jiyun.zhang@fau.de](jiyun.zhang@fau.de) and christoph. [brabec@fau.de](brabec@fau.de)  \nYicheng Zhao, University of Electronic Science and Technology of China (UESTC), State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Electronic Science and Engineering, 611731 Chengdu, the People’s Republic of China.  \nEmail: [zhaoyicheng@uestc.edu.cn](zhaoyicheng@uestc.edu.cn)  \nFunding information  \nBavarian State Government, Grant/Award Numbers: 44-6521a/20/4, FKZ  \n20.2-3410.5-4-5; German Federal Ministry for Economic Affairs and Climate Action, Grant/Award Number: FKZ: 03EE1092A; Helmholtz Association; China Scholarship Council (CSC); Sino-German Postdoc Scholarship Program (CSC-DAAD); Villum Foundation, Grant/Award Number: 50440  \nAbstract  \nSince its emergence in 2009, perovskite photovoltaic technology has achieved remarkable progress, with efficiencies soaring from 3.8% to over 26% . Despite these advancements, challenges such as long-term material and device stability remain. Addressing these challenges requires reproducible, user-independent laboratory processes and intelligent experimental preselection. Traditional trial-and-error methods and manual analysis are inefficient and urgently need advanced strategies. Automated acceleration platforms have transformed this field by improving efficiency, minimizing errors, and ensuring consistency. This review summarizes recent developments in machine learning-driven automation for perovskite photovoltaics, with a focus on its application in new transport material discovery, composition screening, and device preparation optimization. Furthermore, the review introduces the concept of the self-driven Autonomous Material and Device Acceleration Platforms (AMADAP) laboratory and discusses potential challenges it may face. This approach streamlines the entire process, from material discovery to device performance improvement, ultimately accelerating the development of emerging photovoltaic technologies.  \nKEYWOR DS  \nautomation, device acceleration platforms, machine learning, materials acceleration platforms, perovskite solar cells, self-driving laboratory  \nJiyun Zhang and Jianchang Wu contributed equally to this study.  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2025 The Author(s). InfoMat published by UESTC and John Wiley & Sons Australia, Ltd.  \n1 | INTRODUCTION  \nPerovskite photovoltaics have experienced remarkable development since their first application in solar cells in 2009. The power conversion efficiency (PCE) of these materials has surged from 3 .8% to over 26% .1–3 Perovskite materials, typical","cbCaibeYQe3aizOE","https://ap.wps.com/l/cbCaibeYQe3aizOE","pdf",22331283,"English","# Abstract\n# Introduction\n# Automated acceleration platforms\n# Machine learning-driven automation in perovskite photovoltaics\n## Transport material discovery\n## Composition screening\n## Device preparation optimization\n# Autonomous Material and Device Acceleration Platforms (AMADAP)\n## Potential challenges","[{\"question\":\"What progress has perovskite photovoltaic technology achieved since 2009?\",\"answer\":\"Since 2009, perovskite photovoltaics have shown remarkable efficiency gains, increasing power conversion efficiency from 3.8% to above 26%. It also highlights remaining stability challenges despite this progress.\"},{\"question\":\"Why are traditional trial-and-error and manual analysis approaches inadequate?\",\"answer\":\"They are inefficient and labor-intensive, especially when addressing multi-objective optimization across large multidimensional composition and parameter spaces. The document emphasizes the need to process large datasets and optimize complex parameter spaces more effectively.\"},{\"question\":\"How does machine learning-driven automation support perovskite solar research?\",\"answer\":\"It enables intelligent experimental preselection and accelerates workflows for new transport material discovery, composition screening, and device preparation optimization. The approach aims to streamline the pipeline from materials to improved device performance.\"}]","Advancing perovskite photovoltaic technology through machine learning-driven automation | PDF"]