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@ARTICLE{Zhang:1044681,
      author       = {Zhang, Jiyun and Le Corre, Vincent Marc and Wu, Jianchang
                      and DU, Tian and Osterrieder, Tobias and Zhang, Kaicheng and
                      Zhang, Handan and Lüer, Larry and Hauch, Jens and Brabec,
                      Christoph J.},
      title        = {{A}utonomous {O}ptimization of {A}ir‐{P}rocessed
                      {P}erovskite {S}olar {C}ell in a {M}ultidimensional
                      {P}arameter {S}pace},
      journal      = {Advanced energy materials},
      volume       = {15},
      number       = {19},
      issn         = {1614-6832},
      address      = {Weinheim},
      publisher    = {Wiley-VCH},
      reportid     = {FZJ-2025-03330},
      pages        = {2404957},
      year         = {2025},
      abstract     = {Traditional optimization methods often face challenges in
                      exploring complex process parameter spaces, which typically
                      result in suboptimal local maxima. Here an autonomous
                      framework driven by a machine learning (ML)-guided automated
                      platform is introduced to optimize the fabrication
                      conditions of additive- and passivation-free perovskite
                      solar cells (PSCs) under ambient conditions. By effectively
                      exploring a 6D parameter space, this method identifies five
                      parameter sets achieving efficiencies above $23\%,$ with a
                      peak efficiency of $23.7\%$ with limited experimental
                      budgets. Feature importance analysis indicates that the
                      rotation speeds during the first and second steps of
                      perovskite processing are the most influential factors
                      affecting device performance, thereby meriting
                      prioritization in the optimization efforts. These results
                      demonstrate the exceptional capability of the autonomous
                      framework in addressing complex process parameter
                      optimization challenges and its potential to advance
                      perovskite photovoltaic technology. Beyond PSCs, this work
                      provides a reliable and comprehensive strategy for
                      optimizing solution-processed semiconductors and highlights
                      the broader applications of autonomous methodologies in
                      materials science.},
      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:001386818000001},
      doi          = {10.1002/aenm.202404957},
      url          = {https://juser.fz-juelich.de/record/1044681},
}