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@ARTICLE{Malek:872923,
      author       = {Malek, Ali and Eslamibidgoli, Mohammad Javad and Mokhtari,
                      Mehrdad and Wang, Qianpu and Eikerling, Michael H. and
                      Malek, Kourosh},
      title        = {{V}irtual {M}aterials {I}ntelligence for {D}esign and
                      {D}iscovery of {A}dvanced {E}lectrocatalysts},
      journal      = {ChemPhysChem},
      volume       = {20},
      number       = {22},
      issn         = {1439-7641},
      address      = {Weinheim},
      publisher    = {Wiley-VCH Verl.},
      reportid     = {FZJ-2020-00388},
      pages        = {2946 - 2955},
      year         = {2019},
      abstract     = {Similar to advancements gained from big data in genomics,
                      security, internet of things, and e‐commerce, the
                      materials workflow could be made more efficient and prolific
                      through advances in streamlining data sources, autonomous
                      materials synthesis, rapid characterization, big data
                      analytics, and self‐learning algorithms. In
                      electrochemical materials science, data sets are large,
                      unstructured/heterogeneous, and difficult to process and
                      analyze from a single data channel or platform.
                      Computer‐aided materials design together with advances in
                      data mining, machine learning, and predictive analytics are
                      expected to provide inexpensive and accelerated pathways
                      towards tailor‐made functionally optimized energy
                      materials. Fundamental research in the field of
                      electrochemical energy materials focuses primarily on
                      complex interfacial phenomena and kinetic electrocatalytic
                      processes. This perspective article critically assesses
                      AI‐driven modeling and computational approaches that are
                      currently applied to those objects. An application‐driven
                      materials intelligence platform is introduced, and its
                      functionalities are scrutinized considering the development
                      of electrocatalyst materials for CO2 conversion as a use
                      case.},
      cin          = {IEK-13},
      ddc          = {540},
      cid          = {I:(DE-Juel1)IEK-13-20190226},
      pnm          = {113 - Methods and Concepts for Material Development
                      (POF3-113)},
      pid          = {G:(DE-HGF)POF3-113},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31587461},
      UT           = {WOS:000501279800005},
      doi          = {10.1002/cphc.201900570},
      url          = {https://juser.fz-juelich.de/record/872923},
}