New publication - Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set

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In this work, we show that we can use large graph neural networks to predict transition metal complex energies. We developed an improved dataset at a higher level of theory, and tested models ranging from GemNet-T (best) to SchNet (worst). The model performance saturates with the size of neutral structures, and improves with increasing size of charged structures. Finally, we showed that a pre-trained model from OC20 was even better than training from scratch. This indicates a degree of transferability from heterogeneous catalyst models to homogeneous molecular catalysts.

  author =       {Garrison, Aaron G. and Heras-Domingo, Javier and Kitchin, John
                  R. and dos Passos Gomes, Gabriel and Ulissi, Zachary W. and
                  Blau, Samuel M.},
  title =        {Applying Large Graph Neural Networks To Predict Transition
                  Metal Complex Energies Using the tmQM\_wB97MV Data Set},
  journal =      {Journal of Chemical Information and Modeling},
  volume =       0,
  number =       0,
  pages =        {null},
  year =         2023,
  doi =          {10.1021/acs.jcim.3c01226},
  URL =          {},
  eprint =       {},
  note =         {PMID: 38049389},

Copyright (C) 2023 by John Kitchin. See the License for information about copying.

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