New publication in J. Phys. Chem. C
Posted January 31, 2017 at 09:30 AM | categories: news, publication | tags: | View Comments
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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