The Kitchin Research Group

In this work we show that prediction errors from graph neural networks for related atomistic systems tend to be correlated, and as a result the differences in energy are more accurate than the absolute energies. This is similar to what is observed in DFT calculations where systematic errors also cancel in differences. We show this quantitatively through differences of systems with systematically different levels of similarity. This article is part of the Special Collection: 2023 JCP Emerging Investigators Special Collection.

@article{ock-2023-beyon-indep,
  author =       {Janghoon Ock and Tian Tian and John Kitchin and Zachary
                  Ulissi},
  title =        {Beyond Independent Error Assumptions in Large {GNN} Atomistic
                  Models},
  journal =      {The Journal of Chemical Physics},
  volume =       158,
  number =       21,
  pages =        {nil},
  year =         2023,
  doi =          {10.1063/5.0151159},
  url =          {http://dx.doi.org/10.1063/5.0151159},
  DATE_ADDED =   {Sun Sep 24 14:53:46 2023},
}

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

org-mode source

Org-mode version = 9.7-pre