New publication in Calphad

| categories: news, publication | 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},
  journal =      "Calphad ",
  volume =       56,
  pages =        "224 - 229",
  year =         2017,
  doi =          {10.1016/j.calphad.2017.01.009},
  url =
                  {https://doi.org/http://dx.doi.org/10.1016/j.calphad.2017.01.009},
  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",
}

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

org-mode source

Org-mode version = 9.0.3

Read and Post Comments

New publication in Molecular Simulation

| categories: news, publication | 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},
}

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

org-mode source

Org-mode version = 9.0.3

Read and Post Comments

New publication in J. Phys. Chem. C

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

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

org-mode source

Org-mode version = 9.0.3

Read and Post Comments

New publication in ACS Applied Materials & Interfaces

| categories: news, publication | 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},
}

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

org-mode source

Org-mode version = 9.0.3

Read and Post Comments

Using Twitter cards for better tweets

| categories: publication | tags: | View Comments

@article{thirumalai-2015-pt-pd,
  author =       "Hari Thirumalai and John R. Kitchin",
  title =        {The Role of Vdw Interactions in Coverage Dependent Adsorption
                  Energies of Atomic Adsorbates on Pt(111) and Pd(111)},
  journal =      "Surface Science ",
  pages =        " - ",
  year =         2015,
  doi =          {10.1016/j.susc.2015.10.001},
  url =
                  "http://www.sciencedirect.com/science/article/pii/S0039602815003052",
  issn =         "0039-6028",
}

See it here: http://www.sciencedirect.com/science/article/pii/S0039602815003052.

The main goal of this post is to test run using a Twitter card to make better tweets about publications.

This post did not work quite like I anticipated, mostly because of the way I publish my blog which focuses only on the HTML body. The meta tags that are needed for Twitter do not seem to get put in the header as needed. If I do a regular org export with HTML_HEAD options to get this page: http://kitchingroup.cheme.cmu.edu/publications/twitter-card.html, it did work. The page is pretty bare, but it could be embellished without much work.

Tweeting that URL led to this tweet:

On Twitter, this showed an image of the picture on the page, and linked directly to the page I made. The image is sized a little large and doesn't fit in card quite right, but this is probably fixable. This whole process could be smoothed out a lot with a custom export to get the twitter meta tags in the right place, and maybe provide links to bibtex files, analytics, etc. Sounds like a fun project ;)

Copyright (C) 2016 by John Kitchin. See the License for information about copying.

org-mode source

Org-mode version = 8.3.5

Read and Post Comments

Next Page ยป