% 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{Schweidtmann:917552,
      author       = {Schweidtmann, Artur M. and Rittig, Jan G. and Weber, Jana
                      M. and Grohe, Martin and Dahmen, Manuel and Leonhard, Kai
                      and Mitsos, Alexander},
      title        = {{P}hysical {P}ooling {F}unctions in {G}raph {N}eural
                      {N}etworks for {M}olecular {P}roperty {P}rediction},
      publisher    = {arXiv},
      reportid     = {FZJ-2023-00754},
      year         = {2022},
      abstract     = {Graph neural networks (GNNs) are emerging in chemical
                      engineering for the end-to-end learning of physicochemical
                      properties based on molecular graphs. A key element of GNNs
                      is the pooling function which combines atom feature vectors
                      into molecular fingerprints. Most previous works use a
                      standard pooling function to predict a variety of
                      properties. However, unsuitable pooling functions can lead
                      to unphysical GNNs that poorly generalize. We compare and
                      select meaningful GNN pooling methods based on physical
                      knowledge about the learned properties. The impact of
                      physical pooling functions is demonstrated with molecular
                      properties calculated from quantum mechanical computations.
                      We also compare our results to the recent set2set pooling
                      approach. We recommend using sum pooling for the prediction
                      of properties that depend on molecular size and compare
                      pooling functions for properties that are molecular
                      size-independent. Overall, we show that the use of physical
                      pooling functions significantly enhances generalization.},
      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.2207.13779},
      url          = {https://juser.fz-juelich.de/record/917552},
}