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@ARTICLE{Brozos:1025675,
      author       = {Brozos, Christoforos and Rittig, Jan G. and Bhattacharya,
                      Sandip and Akanny, Elie and Kohlmann, Christina and Mitsos,
                      Alexander},
      title        = {{P}redicting the {T}emperature {D}ependence of {S}urfactant
                      {CMC}s {U}sing {G}raph {N}eural {N}etworks},
      publisher    = {arXiv},
      reportid     = {FZJ-2024-03064},
      year         = {2024},
      abstract     = {The critical micelle concentration (CMC) of surfactant
                      molecules is an essential property for surfactant
                      applications in industry. Recently, classical QSPR and Graph
                      Neural Networks (GNNs), a deep learning technique, have been
                      successfully applied to predict the CMC of surfactants at
                      room temperature. However, these models have not yet
                      considered the temperature dependency of the CMC, which is
                      highly relevant for practical applications. We herein
                      develop a GNN model for temperature-dependent CMC prediction
                      of surfactants. We collect about 1400 data points from
                      public sources for all surfactant classes, i.e., ionic,
                      nonionic, and zwitterionic, at multiple temperatures. We
                      test the predictive quality of the model for following
                      scenarios: i) when CMC data for surfactants are present in
                      the training of the model in at least one different
                      temperature, and ii) CMC data for surfactants are not
                      present in the training, i.e., generalizing to unseen
                      surfactants. In both test scenarios, our model exhibits a
                      high predictive performance of R$^2 \geq $ 0.94 on test
                      data. We also find that the model performance varies by
                      surfactant class. Finally, we evaluate the model for
                      sugar-based surfactants with complex molecular structures,
                      as these represent a more sustainable alternative to
                      synthetic surfactants and are therefore of great interest
                      for future applications in the personal and home care
                      industries.},
      keywords     = {Chemical Physics (physics.chem-ph) (Other) / Machine
                      Learning (cs.LG) (Other) / FOS: Physical sciences (Other) /
                      FOS: Computer and information sciences (Other)},
      cin          = {IEK-10},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2403.03767},
      url          = {https://juser.fz-juelich.de/record/1025675},
}