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@ARTICLE{Rittig:917554,
      author       = {Rittig, Jan G. and Hicham, Karim Ben and Schweidtmann,
                      Artur M. and Dahmen, Manuel and Mitsos, Alexander},
      title        = {{G}raph {N}eural {N}etworks for {T}emperature-{D}ependent
                      {A}ctivity {C}oefficient {P}rediction of {S}olutes in
                      {I}onic {L}iquids},
      publisher    = {arXiv},
      reportid     = {FZJ-2023-00756},
      year         = {2022},
      abstract     = {Ionic liquids (ILs) are important solvents for sustainable
                      processes and predicting activity coefficients (ACs) of
                      solutes in ILs is needed. Recently, matrix completion
                      methods (MCMs), transformers, and graph neural networks
                      (GNNs) have shown high accuracy in predicting ACs of binary
                      mixtures, superior to well-established models, e.g.,
                      COSMO-RS and UNIFAC. GNNs are particularly promising here as
                      they learn a molecular graph-to-property relationship
                      without pretraining, typically required for transformers,
                      and are, unlike MCMs, applicable to molecules not included
                      in training. For ILs, however, GNN applications are
                      currently missing. Herein, we present a GNN to predict
                      temperature-dependent infinite dilution ACs of solutes in
                      ILs. We train the GNN on a database including more than
                      40,000 AC values and compare it to a state-of-the-art MCM.
                      The GNN and MCM achieve similar high prediction performance,
                      with the GNN additionally enabling high-quality predictions
                      for ACs of solutions that contain ILs and solutes not
                      considered during training.},
      keywords     = {Machine Learning (cs.LG) (Other) / Chemical Physics
                      (physics.chem-ph) (Other) / FOS: Computer and information
                      sciences (Other) / FOS: Physical 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.2206.11776},
      url          = {https://juser.fz-juelich.de/record/917554},
}