New publication on segregation in ternary alloy surfaces

Posted January 20, 2022 at 12:44 PM | categories: publication, news | tags:

Updated January 20, 2022 at 12:44 PM

In this paper we combine density functional theory, machine learning, Monte Carlo simulations and experimental data to study segregation in a ternary alloy Cu-Pd-Au surface across the composition space. We found varying agreement and disagreement between tehe simulated and experimental results, and discuss the origins of these. Overall, Au segregates significantly across the composition space, and we learned a lot about the contributions to the discrepancies observed in Cu-Pd segregation and Au-Cu segregration.

Find the paper at https://pubs.acs.org/doi/10.1021/acs.jpcc.1c09647. 

@article{yang-2022-simul-segreg,
  author =       {Yilin Yang and Zhitao Guo and Andrew J. Gellman and John R.
                  Kitchin},
  title =        {Simulating Segregation in a Ternary Cu-Pd-Au Alloy With
                  Density Functional Theory, Machine Learning, and Monte Carlo
                  Simulations},
  journal =      {The Journal of Physical Chemistry C},
  volume =       {nil},
  number =       {nil},
  pages =        {acs.jpcc.1c09647},
  year =         2022,
  doi =          {10.1021/acs.jpcc.1c09647},
  url =          {http://dx.doi.org/10.1021/acs.jpcc.1c09647},
  DATE_ADDED =   {Thu Jan 20 12:39:49 2022},
}

Copyright (C) 2022 by John Kitchin. See the License for information about copying.

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