| categories: | tags: | View Comments

Alloys can have rich, complex phase behavior. Cu-Pd alloys for example show an unusual behavior where a BCC lattice forms for some compositions, even though the alloy is made from two metals that are exclusively FCC in structure! Being able to model and predict this kind of behavior is a major challenge. In this work, we use cluster expansions to model the configurational degrees of freedom in the FCC and BCC lattices and show qualitatively that we can predict the region where the B2 phase (the BCC one) forms. The agreement with experiment is not quantitative though, and we show that part this disagreement is due to the lack of vibrational entropy in the cluster expansion. When we include vibrational entropy, the qualitative agreement improves.

@article{geng-2017-first-princ,
author =       "Feiyang Geng and Jacob R. Boes and John R. Kitchin",
title =        {First-Principles Study of the Cu-Pd Phase Diagram},
volume =       56,
pages =        "224 - 229",
year =         2017,
url =
abstract =     "Abstract The equilibrium phase diagram of a Cu-Pd alloy has
been computed using cluster expansion and Monte Carlo
simulation methods combined with density functional theory.
The computed phase boundaries show basic features that are
consistent with the experimentally reported phase diagram.
Without vibrational free energy contributions, the
order-disorder transition temperature is underestimated by 100
K and the critical point is inconsistent with experimental
result. The addition of vibrational free energy contributions
yields a more qualitatively correct Cu-Pd phase diagram in the
Cu rich region. ",
issn =         "0364-5916",
keywords =     "Density functional theory",
}


org-mode source

Org-mode version = 9.0.3

## New publication in Molecular Simulation

| categories: | tags: | View Comments

Molecules interact with each other when they adsorb on surfaces and these interactions are coverage dependent. Modeling these interactions is a challenge though, because there are many configurations of adsorbates on the surface, and the surface changes due to the interactions. To mitigate these challenges, one often simplifies the model, e.g. by using a cluster expansion or lattice gas Hamiltonian. These approaches have their own limitations though, and are not that useful for modeling dynamic processes like diffusion. Using molecular potentials enables the dynamic simulations, but not at the same level of accuracy as density functional theory. In this work we use density functional theory to train a neural network, and then use the neural network to model coverage-dependent adsorption isotherms and the dynamics of oxygen diffusion on a Pd(111) surface. We show the neural network can capture the onset of surface oxidation, and that the simulation results have comparable accuracy to the DFT calculations it was trained from.

@article{boes-2017-neural-networ,
author =       {Jacob R. Boes and John R. Kitchin},
title =        {Neural Network Predictions of Oxygen Interactions on a Dynamic
Pd Surface},
journal =      {Molecular Simulation},
pages =        {1-9},
year =         2017,
doi =          {10.1080/08927022.2016.1274984},
url =          {https://doi.org/10.1080/08927022.2016.1274984},
keywords =     {CBET-1506770},
}


org-mode source

Org-mode version = 9.0.3

## New publication in J. Phys. Chem. C

| categories: | 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 =       { http://dx.doi.org/10.1021/acs.jpcc.6b12752 },
}


org-mode source

Org-mode version = 9.0.3

## Elif Erdinc selected for 2016-2017 Gulf Oil Foundation Graduate Fellowship in Chemical Engineering!

| categories: | tags: | View Comments

This fellowship was established by a donation from the Gulf Oil Foundation, and it will cover part of Elif's tuition and stipend. Congratulations Elif!

org-mode source

Org-mode version = 9.0.3

## New publication in ACS Applied Materials & Interfaces

| categories: | tags: | View Comments

Titania can be grown as an epitaxial thin film on many perovskites. The structure of the film depends on the perovskite, as well as the orientation of the surface the film grows on. In this work, we show which factors determine this, including epitaxial strain, and interface energies. In general no single factor determines all the behavior, but when considered collectively, our computational analysis correctly predicts which thin film polymorph is observed experimentally most of the time.

@article{xu-2017-first-princ,
author =       {Xu, Zhongnan and Salvador, Paul A. and Kitchin, John R.},
title =        {First-Principles Investigation of the Epitaxial Stabilization
of Oxide Polymorphs: \ce{TiO2} on \ce{(Sr,Ba)TiO3}},
journal =      {ACS Applied Materials \& Interfaces},
volume =       0,
number =       {ja},
pages =        {null},
year =         2017,
doi =          {10.1021/acsami.6b11791},
url =          {https://doi.org/10.1021/acsami.6b11791},
eprint =       { http://dx.doi.org/10.1021/acsami.6b11791 },
note =         {PMID: 28004912},
}