The Kitchin Research Group

The surface composition of an alloy is rarely the same as the bulk composition due to segregation, and it changes with changing reaction conditions. Segregation is a ubiquitous issue in alloy catalysis, and makes modeling alloy surfaces a challenge, because we need to know the surface composition to model them! In this work, we take a first step in using density functional theory to build a neural network potential that we can use with Monte Carlo simulations to predict the temperature dependent surface composition of an Au-Pd bulk alloy in a vacuum. This approach yielded quantitative predictions in good agreement with experimental measurements over the entire bulk composition range.

@article{boes-2017-model-segreg,
  author =       {Boes, Jacob Russell and Kitchin, John R.},
  title =        {Modeling Segregation on {AuPd}(111) Surfaces With Density
                  Functional Theory and Monte Carlo Simulations},
  journal =      {The Journal of Physical Chemistry C},
  volume =       0,
  number =       {ja},
  pages =        {null},
  year =         2017,
  doi =          {10.1021/acs.jpcc.6b12752},
  url =          {https://doi.org/10.1021/acs.jpcc.6b12752},
  eprint =       { https://doi.org/10.1021/acs.jpcc.6b12752 },
}

Copyright (C) 2017 by John Kitchin. See the License for information about copying.

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