% 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{Flores:1019519,
      author       = {Flores, Eibar and Wölke, Christian and Yan, Peng and
                      Winter, Martin and Vegge, Tejs and Cekic-Laskovic, Isidora
                      and Bhowmik, Arghya},
      title        = {{L}earning the laws of lithium-ion transport inelectrolytes
                      using symbolic regression†},
      journal      = {Digital discovery},
      volume       = {1},
      number       = {4},
      issn         = {2635-098X},
      address      = {Washington DC},
      publisher    = {Royal Society of Chemistry},
      reportid     = {FZJ-2023-05466},
      pages        = {440-447},
      year         = {2022},
      abstract     = {High-throughput experiments (HTE) enable fast exploration
                      of advanced battery electrolytes over vast compositional
                      spaces. Among the multiple properties considered for optimal
                      electrolyte performance, the conductivity is critical. An
                      analytical expression for ionic transport in electrolytes,
                      accurate for practical compositions and operating
                      conditions, would accelerate the process of (i)
                      co-optimizing conductivity alongside other desirable
                      electrolyte properties, and (ii) learning fundamental
                      physical laws from data, which is one of the paramount goals
                      of scientific big-data analytics. Here, we used symbolic
                      regression with an HTE-acquired dataset of electrolyte
                      conductivity and discovered a simple, accurate, consistent
                      and generalizable expression. Notably, despite emerging from
                      a purely statistical approach, the expression reflects
                      functional aspects from established thermodynamic limiting
                      laws, indicating our model is grounded on the fundamental
                      physical mechanisms underpinning ionic transport. We
                      demonstrate the potential of using machine learning with HTE
                      to find accurate and physically-sound models in complex
                      systems without established physico-chemical theories.},
      cin          = {IEK-12},
      ddc          = {004},
      cid          = {I:(DE-Juel1)IEK-12-20141217},
      pnm          = {1222 - Components and Cells (POF4-122) / BIG-MAP - Battery
                      Interface Genome - Materials Acceleration Platform (957189)},
      pid          = {G:(DE-HGF)POF4-1222 / G:(EU-Grant)957189},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001101457500001},
      doi          = {10.1039/D2DD00027J},
      url          = {https://juser.fz-juelich.de/record/1019519},
}