## New publication on segregation in ternary alloy surfaces

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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},
}