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@ARTICLE{Osterrieder:1010389,
      author       = {Osterrieder, Tobias and Schmitt, Frederik and Lüer, Larry
                      and Wagner, Jerrit and Heumüller, Thomas and Hauch, Jens
                      and Brabec, Christoph J.},
      title        = {{A}utonomous optimization of an organic solar cell in a
                      4-dimensional parameter space},
      journal      = {Energy $\&$ environmental science},
      volume       = {16},
      number       = {9},
      issn         = {1754-5692},
      address      = {Cambridge},
      publisher    = {RSC Publ.},
      reportid     = {FZJ-2023-03033},
      pages        = {3984-3993},
      year         = {2023},
      abstract     = {Optimizing solution-processed organic solar cells is a
                      complex and challenging task due to the vast parameter space
                      in organic photovoltaics (OPV). Classical Edisonian or
                      one-variable-at-a-time (OVAT) optimization approaches are
                      laborious, time-consuming, and may not find the optimal
                      parameter set in multidimensional design spaces. To tackle
                      this problem, we demonstrate here for the first time
                      artificial intelligence (AI) guided closed-loop autonomous
                      optimization for fully functional organic solar cells. We
                      empower our LineOne, an automated materials and device
                      acceleration platform with a Bayesian Optimizer (BO) to
                      enable autonomous operation for solving complex optimization
                      problems without human interference. The system is able to
                      fabricate and characterize complete OPV devices and navigate
                      efficiently through the design space spanned by composition
                      and processing parameters. In addition, a Gaussian Progress
                      Regression (GPR) based early prediction model is employed to
                      predict the efficiency of the cells from cheap proxy
                      measurements, in our case, thin film absorption spectra,
                      which are analyzed using a spectral model based on physical
                      properties to generate microstructure features as input for
                      the GPR. We demonstrate our generic and complete autonomous
                      approach by optimizing composition and processing conditions
                      of a ternary OPV system (PM6:Y12:PC70BM) in a
                      four-dimensional parameter space. We identify the best
                      parameter set for our system and obtain a precise objective
                      function over the whole parameter space with a minimal
                      number of samples. We demonstrate autonomous optimization of
                      a complex opto-electronic device within 40 samples only,
                      whereas an Edisonian approach would have required about 1000
                      samples. Even larger acceleration factors are expected for
                      higher dimensional parameter spaces. This raises an
                      important discussion on the necessity of autonomous
                      platforms to accelerate Material science.},
      cin          = {IEK-11},
      ddc          = {690},
      cid          = {I:(DE-Juel1)IEK-11-20140314},
      pnm          = {1213 - Cell Design and Development (POF4-121)},
      pid          = {G:(DE-HGF)POF4-1213},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001044390000001},
      doi          = {10.1039/D3EE02027D},
      url          = {https://juser.fz-juelich.de/record/1010389},
}