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@ARTICLE{Sun:891312,
      author       = {Sun, Shijing and Tiihonen, Armi and Oviedo, Felipe and Liu,
                      Zhe and Thapa, Janak and Zhao, Yicheng and Hartono, Noor
                      Titan P. and Goyal, Anuj and Heumueller, Thomas and Batali,
                      Clio and Encinas, Alex and Yoo, Jason J. and Li, Ruipeng and
                      Ren, Zekun and Peters, I. Marius and Brabec, Christoph J.
                      and Bawendi, Moungi G. and Stevanovic, Vladan and Fisher,
                      John and Buonassisi, Tonio},
      title        = {{A} data fusion approach to optimize compositional
                      stability of halide perovskites},
      journal      = {Matter},
      volume       = {4},
      number       = {4},
      issn         = {2590-2385},
      address      = {[New York, NY]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2021-01419},
      pages        = {1305-1322},
      year         = {2021},
      abstract     = {Despite recent intensive efforts to improve the
                      environmental stability of halide perovskite materials for
                      energy harvesting and conversion, traditional
                      trial-and-error explorations face bottlenecks in the
                      navigation of vast chemical and compositional spaces. We
                      develop a closed-loop optimization framework that seamlessly
                      marries data from first-principle calculations and
                      high-throughput experimentation into a single machine
                      learning algorithm. This framework enables us to achieve
                      rapid optimization of compositional stability for
                      CsxMAyFA1−x−yPbI3 perovskites while taking the human out
                      of the decision-making loop. We envision that this data
                      fusion approach is generalizable to directly tackle
                      challenges in designing multinary materials, and we hope
                      that our successful showcase on perovskites will encourage
                      researchers in other fields to incorporate knowledge of
                      physics into the search algorithms, applying hybrid machine
                      learning models to guide discovery of materials in
                      high-dimensional spaces.},
      cin          = {IEK-11},
      ddc          = {600},
      cid          = {I:(DE-Juel1)IEK-11-20140314},
      pnm          = {121 - Photovoltaik und Windenergie (POF4-121)},
      pid          = {G:(DE-HGF)POF4-121},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000637800400003},
      doi          = {10.1016/j.matt.2021.01.008},
      url          = {https://juser.fz-juelich.de/record/891312},
}