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@ARTICLE{Zhang:1041045,
      author       = {Zhang, Jiyun and Wu, Jianchang and Le Corre, Vincent Marc
                      and Hauch, Jens and Zhao, Yicheng and Brabec, Christoph},
      title        = {{A}dvancing perovskite photovoltaic technology through
                      machine learning‐driven automation},
      journal      = {InfoMat},
      issn         = {2567-3165},
      address      = {Weinheim},
      publisher    = {Wiley},
      reportid     = {FZJ-2025-02109},
      pages        = {e70005},
      year         = {2025},
      abstract     = {Since 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.},
      cin          = {IET-2},
      ddc          = {621.3},
      cid          = {I:(DE-Juel1)IET-2-20140314},
      pnm          = {1212 - Materials and Interfaces (POF4-121)},
      pid          = {G:(DE-HGF)POF4-1212},
      typ          = {PUB:(DE-HGF)36 / PUB:(DE-HGF)16},
      UT           = {WOS:001429146800001},
      doi          = {10.1002/inf2.70005},
      url          = {https://juser.fz-juelich.de/record/1041045},
}