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@ARTICLE{Rittig:917555,
      author       = {Rittig, Jan G. and Ritzert, Martin and Schweidtmann, Artur
                      M. and Winkler, Stefanie and Weber, Jana M. and Morsch,
                      Philipp and Heufer, K. Alexander and Grohe, Martin and
                      Mitsos, Alexander and Dahmen, Manuel},
      title        = {{G}raph {M}achine {L}earning for {D}esign of
                      {H}igh-{O}ctane {F}uels},
      publisher    = {arXiv},
      reportid     = {FZJ-2023-00757},
      year         = {2022},
      abstract     = {Fuels with high-knock resistance enable modern
                      spark-ignition engines to achieve high efficiency and thus
                      low CO2 emissions. Identification of molecules with desired
                      autoignition properties indicated by a high research octane
                      number and a high octane sensitivity is therefore of great
                      practical relevance and can be supported by computer-aided
                      molecular design (CAMD). Recent developments in the field of
                      graph machine learning (graph-ML) provide novel, promising
                      tools for CAMD. We propose a modular graph-ML CAMD framework
                      that integrates generative graph-ML models with graph neural
                      networks and optimization, enabling the design of molecules
                      with desired ignition properties in a continuous molecular
                      space. In particular, we explore the potential of Bayesian
                      optimization and genetic algorithms in combination with
                      generative graph-ML models. The graph-ML CAMD framework
                      successfully identifies well-established high-octane
                      components. It also suggests new candidates, one of which we
                      experimentally investigate and use to illustrate the need
                      for further auto-ignition training data.},
      keywords     = {Machine Learning (cs.LG) (Other) / FOS: Computer and
                      information sciences (Other)},
      cin          = {IEK-10},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {1121 - Digitalization and Systems Technology for
                      Flexibility Solutions (POF4-112)},
      pid          = {G:(DE-HGF)POF4-1121},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2206.00619},
      url          = {https://juser.fz-juelich.de/record/917555},
}