New publication - Chemical Properties from Graph Neural Network-Predicted Electron Densities

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The electron density is one of the most important quantities we use DFT to calculate. It is the foundation of how we compute energy, forces, and many other properties. DFT is expensive though, so in this work we show that we can build a graph neural network that can be used to predict electron densities directly from the atomic coordinates of a system. We show that the predicted densities can also be used to estimate dipole moments and Bader charges.

  author =       {Sunshine, Ethan M. and Shuaibi, Muhammed and Ulissi, Zachary
                  W. and Kitchin, John R.},
  title =        {Chemical Properties From Graph Neural Network-Predicted
                  Electron Densities},
  journal =      {The Journal of Physical Chemistry C},
  volume =       0,
  number =       0,
  pages =        {null},
  year =         2023,
  doi =          {10.1021/acs.jpcc.3c06157},
  url =          {},
  eprint =       {},

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

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