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@ARTICLE{Wang:1049006,
      author       = {Wang, Yanxue and Perea-Puente, Sinuhé and Le Corre,
                      Vincent M. and Wu, Zhenni and Sytnyk, Mykhailo and These,
                      Albert and Zhang, Jiyun and Li, Chaohui and Lüer, Larry and
                      Hauch, Jens and Brabec, Christoph and Peters, Ian Marius},
      title        = {{H}ybrid {L}earning {E}nables {R}eproducible $\>24\%$
                      {E}fficiency in {A}utonomously {F}abricated {P}erovskites
                      {S}olar {C}ells},
      journal      = {Advanced energy materials},
      volume       = {16},
      number       = {4},
      issn         = {1614-6832},
      address      = {Weinheim},
      publisher    = {Wiley-VCH},
      reportid     = {FZJ-2025-05101},
      pages        = {e04340},
      year         = {2026},
      abstract     = {Achieving 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.},
      cin          = {IET-2},
      ddc          = {050},
      cid          = {I:(DE-Juel1)IET-2-20140314},
      pnm          = {1213 - Cell Design and Development (POF4-121)},
      pid          = {G:(DE-HGF)POF4-1213},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001621234500001},
      doi          = {10.1002/aenm.202504340},
      url          = {https://juser.fz-juelich.de/record/1049006},
}