001049006 001__ 1049006
001049006 005__ 20260223122509.0
001049006 0247_ $$2doi$$a10.1002/aenm.202504340
001049006 0247_ $$2ISSN$$a1614-6832
001049006 0247_ $$2ISSN$$a1614-6840
001049006 0247_ $$2WOS$$aWOS:001621234500001
001049006 037__ $$aFZJ-2025-05101
001049006 082__ $$a050
001049006 1001_ $$0P:(DE-Juel1)201816$$aWang, Yanxue$$b0$$eCorresponding author
001049006 245__ $$aHybrid Learning Enables Reproducible >24% Efficiency in Autonomously Fabricated Perovskites Solar Cells
001049006 260__ $$aWeinheim$$bWiley-VCH$$c2026
001049006 3367_ $$2DRIVER$$aarticle
001049006 3367_ $$2DataCite$$aOutput Types/Journal article
001049006 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1769777287_12857
001049006 3367_ $$2BibTeX$$aARTICLE
001049006 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001049006 3367_ $$00$$2EndNote$$aJournal Article
001049006 520__ $$aAchieving high-performance perovskite solar cells (PSCs) with satisfactory reproducibility remains a major challenge due to their intrinsic susceptibility to processing variations and environmental fluctuations. To address this challenge, this study introduces an autonomous optimization framework that integrates hybrid machine learning and high-throughput experimentation with modified gradient ascent methods to optimize fabrication processes and minimize experimental variances. The framework successfully maps the complex, non-linear interdependencies between fabrication parameters and reveals the critical decoupling of photovoltaic metrics. Optimization across seven rounds and 144 parameter sets results in pronounced power conversion efficiency (PCE) and reproducibility enhancement on the platform. The optimized procedure delivers champion devices achieving PCEs exceeding 24%, surpassing the experience manual operator performance (20.6% PCE, CV >25%) and reducing the coefficient of variation (CV) to below 4.7%, with improvements consistently validated across independent trials. This work offers a practical strategy for improving PSC performance and reproducibility, while laying a foundation for scalable manufacturing and accelerated materials development.
001049006 536__ $$0G:(DE-HGF)POF4-1213$$a1213 - Cell Design and Development (POF4-121)$$cPOF4-121$$fPOF IV$$x0
001049006 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001049006 7001_ $$aPerea-Puente, Sinuhé$$b1
001049006 7001_ $$aLe Corre, Vincent M.$$b2
001049006 7001_ $$0P:(DE-Juel1)200349$$aWu, Zhenni$$b3$$ufzj
001049006 7001_ $$0P:(DE-Juel1)188797$$aSytnyk, Mykhailo$$b4$$ufzj
001049006 7001_ $$0P:(DE-Juel1)204180$$aThese, Albert$$b5$$ufzj
001049006 7001_ $$0P:(DE-Juel1)194716$$aZhang, Jiyun$$b6$$ufzj
001049006 7001_ $$aLi, Chaohui$$b7
001049006 7001_ $$aLüer, Larry$$b8
001049006 7001_ $$0P:(DE-Juel1)177626$$aHauch, Jens$$b9$$ufzj
001049006 7001_ $$0P:(DE-Juel1)209819$$aBrabec, Christoph$$b10$$ufzj
001049006 7001_ $$0P:(DE-Juel1)179536$$aPeters, Ian Marius$$b11$$eCorresponding author$$ufzj
001049006 773__ $$0PERI:(DE-600)2594556-7$$a10.1002/aenm.202504340$$gp. e04340$$n4$$pe04340$$tAdvanced energy materials$$v16$$x1614-6832$$y2026
001049006 8564_ $$uhttps://juser.fz-juelich.de/record/1049006/files/Advanced%20Energy%20Materials%20-%202025%20-%20Wang%20-%20Hybrid%20Learning%20Enables%20Reproducible%2024%20Efficiency%20in%20Autonomously%20Fabricated.pdf$$yRestricted
001049006 8767_ $$d2025-12-09$$eHybrid-OA$$jDEAL
001049006 909CO $$ooai:juser.fz-juelich.de:1049006$$popenCost$$pVDB$$pOpenAPC_DEAL
001049006 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)201816$$aForschungszentrum Jülich$$b0$$kFZJ
001049006 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)200349$$aForschungszentrum Jülich$$b3$$kFZJ
001049006 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188797$$aForschungszentrum Jülich$$b4$$kFZJ
001049006 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)204180$$aForschungszentrum Jülich$$b5$$kFZJ
001049006 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)194716$$aForschungszentrum Jülich$$b6$$kFZJ
001049006 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)177626$$aForschungszentrum Jülich$$b9$$kFZJ
001049006 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)209819$$aForschungszentrum Jülich$$b10$$kFZJ
001049006 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)179536$$aForschungszentrum Jülich$$b11$$kFZJ
001049006 9131_ $$0G:(DE-HGF)POF4-121$$1G:(DE-HGF)POF4-120$$2G:(DE-HGF)POF4-100$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-1213$$aDE-HGF$$bForschungsbereich Energie$$lMaterialien und Technologien für die Energiewende (MTET)$$vPhotovoltaik und Windenergie$$x0
001049006 915pc $$0PC:(DE-HGF)0000$$2APC$$aAPC keys set
001049006 915pc $$0PC:(DE-HGF)0001$$2APC$$aLocal Funding
001049006 915pc $$0PC:(DE-HGF)0002$$2APC$$aDFG OA Publikationskosten
001049006 915pc $$0PC:(DE-HGF)0120$$2APC$$aDEAL: Wiley 2019
001049006 915__ $$0StatID:(DE-HGF)3001$$2StatID$$aDEAL Wiley$$d2024-12-12$$wger
001049006 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2024-12-12
001049006 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2024-12-12
001049006 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bADV ENERGY MATER : 2022$$d2025-11-05
001049006 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2025-11-05
001049006 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2025-11-05
001049006 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2025-11-05
001049006 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2025-11-05
001049006 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2025-11-05
001049006 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2025-11-05
001049006 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology$$d2025-11-05
001049006 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2025-11-05
001049006 915__ $$0StatID:(DE-HGF)9925$$2StatID$$aIF >= 25$$bADV ENERGY MATER : 2022$$d2025-11-05
001049006 920__ $$lyes
001049006 9201_ $$0I:(DE-Juel1)IET-2-20140314$$kIET-2$$lHelmholtz-Institut Erlangen-Nürnberg Erneuerbare Energien$$x0
001049006 980__ $$ajournal
001049006 980__ $$aVDB
001049006 980__ $$aI:(DE-Juel1)IET-2-20140314
001049006 980__ $$aAPC
001049006 980__ $$aUNRESTRICTED
001049006 9801_ $$aAPC