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@ARTICLE{Zhang:1044683,
      author       = {Zhang, Jiyun and Tan, Jiayi and Song, Qizhen and DU, Tian
                      and Hauch, Jens and Brabec, Christoph},
      title        = {{F}eature {S}election for {M}achine {L}earning‐{D}riven
                      {A}ccelerated {D}iscovery and {O}ptimization in {E}merging
                      {P}hotovoltaics: {A} {R}eview},
      journal      = {Missing Journal / Fehlende Zeitschrift},
      reportid     = {FZJ-2025-03331},
      pages        = {202500022},
      year         = {2025},
      note         = {Missing Journal: Advanced Intelligent Discovery (adv.
                      intell. discov.) = 2943-9981 (import from CrossRef,
                      Journals: juser.fz-juelich.de)},
      abstract     = {Developing reliable emerging photovoltaic (e-PV)
                      technologies requires high-throughput material discovery,
                      device design, and processing optimization. However, the
                      effective process of the resulting high-dimensional,
                      multivariate datasets remains a significant challenge.
                      Integrating feature selection methods and machine learning
                      (ML) provides a robust solution to reduce data
                      dimensionality, improve predictive accuracy, and uncover
                      material performance mechanisms. This review summarizes the
                      advancements in synergizing feature selection methods,
                      particularly the maximum relevance minimum redundancy (mRMR)
                      method embedded, with Gaussian process regression (GPR) to
                      advance e-PVs research. It highlights the importance of
                      integrating feature selection with ML and high-throughput
                      experimentation (HTE) frameworks to accelerate material
                      screening, optimize manufacturing processes, and predict
                      stability. Additionally, the review discusses key challenges
                      such as data quality and model scalability and offers
                      promising strategies to address these limitations. This
                      data-driven approach offers a systematic pathway toward the
                      accelerated discovery and optimization of e-PV
                      technologies.},
      cin          = {IET-2},
      cid          = {I:(DE-Juel1)IET-2-20140314},
      pnm          = {1212 - Materials and Interfaces (POF4-121) / 1214 -
                      Modules, stability, performance and specific applications
                      (POF4-121)},
      pid          = {G:(DE-HGF)POF4-1212 / G:(DE-HGF)POF4-1214},
      typ          = {PUB:(DE-HGF)36 / PUB:(DE-HGF)16},
      doi          = {10.1002/aidi.202500022},
      url          = {https://juser.fz-juelich.de/record/1044683},
}