% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Diddens:906100,
      author       = {Diddens, Diddo and Appiah, Williams Agyei and Mabrouk,
                      Youssef and Heuer, Andreas and Vegge, Tejs and Bhowmik,
                      Arghya},
      title        = {{M}odeling the {S}olid {E}lectrolyte {I}nterphase:
                      {M}achine {L}earning as a {G}ame {C}hanger?},
      journal      = {Advanced materials interfaces},
      volume       = {9},
      number       = {8},
      issn         = {2196-7350},
      address      = {Weinheim},
      publisher    = {Wiley-VCH},
      reportid     = {FZJ-2022-01222},
      pages        = {2101734 -},
      year         = {2022},
      abstract     = {The solid electrolyte interphase (SEI) is a complex
                      passivation layer that forms in situ on many battery
                      electrodes such as lithium-intercalated graphite or lithium
                      metal anodes. Its essential function is to prevent the
                      electrolyte from continuous electrochemical degradation,
                      while simultaneously allowing ions to pass through, thus
                      constituting an electronically insulating, but ionically
                      conducting material. Its properties crucially affect the
                      overall performance and aging of a battery cell. Despite
                      decades of intense research, understanding the SEI's precise
                      formation mechanism, structure, composition, and evolution
                      remains a conundrum. State-of-the-art computational modeling
                      techniques are powerful tools to gain additional insights,
                      although confronted with a trade-off between accuracy and
                      accessible time- and length scales. In this review, it is
                      discussed how recent advances in data-driven models,
                      especially the development of fast and accurate surrogate
                      simulators and deep generative models, can work with
                      physics-based and physics-informed approaches to enable the
                      next generation of breakthroughs in this field. Machine
                      learning-enhanced multiscale models can provide new pathways
                      to inverse the design of interphases with desired
                      properties.},
      cin          = {IEK-12},
      ddc          = {600},
      cid          = {I:(DE-Juel1)IEK-12-20141217},
      pnm          = {1221 - Fundamentals and Materials (POF4-122)},
      pid          = {G:(DE-HGF)POF4-1221},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000749645800001},
      doi          = {10.1002/admi.202101734},
      url          = {https://juser.fz-juelich.de/record/906100},
}