New publication in Molecular Simulation

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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.

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New publication in J. Phys. Chem. C

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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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New publication in ACS Applied Materials & Interfaces

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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 =       { https://doi.org/10.1021/acsami.6b11791 },
  note =         {PMID: 28004912},
}

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

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2016 in a nutshell for the Kitchin Research group

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2016 was another good year for the Kitchin Research Group. Here are a few highlights.

1 Student accomplishments

Elif Erdinc joined the group to work on her PhD in CO2 capture.

Sanjna Bhartiya and Jean Haddad joined the group as undergraduate researchers.

Zhongnan Xu and Alex Hallenbeck completed their PhDs and graduated. Zhongnan is doing postdoctoral work with Dane Morgan at the University of Wisconsin.

Chen Wang, Akshay Tharval, Teng Ma, Feiyang Geng, Devon Walker, and Tianyu Gao completed their MS degrees and have graduated.

Congratulations everyone!

2 Publications

2016 was a moderate year for publications for us. We currently have five manuscripts under review. Here are the papers published in 2016.

Papers on org-mode and publishing:

kitchin-2015-data-surfac-scien
A perspective on data sharing in surface science
kitchin-2016-autom-data
concept to automate data embedding in publications

Machine learning and molecular simulation:

boes-2016-neural-networ
Training neural networks with DFT for molecular simulations

Collaborations:

bligaard-2016-towar-bench
A perspective on benchmarking in catalysis
deshpande-2016-quant-uncer
Uncertainty in volcano relationships
calfa-2016-proper-predic
A machine learning approach to materials design
kitchin-2016-high-throug
A perspective on highthroughput methods in engineering

Accepted in 2016:

boes-2016-neural-networ-pdo
Neural networks for coverage dependent adsorption properties
xu-2017-first-princ
Predictions of epitaxial stabilization of oxide films

For the first year, it looks like we got fewer citations than the previous year. I am not sure what that means.

Bibliography

3 Emacs and org-mode

We have continued to develop Emacs and org-mode into a fantastic scientific writing tool. scimax (https://github.com/jkitchin/scimax) replaced jmax as our emacs starterkit.

org-ref (https://github.com/jkitchin/org-ref) has been downloaded more than 10,000 times now from MELPA! I helped rewrite the link code for org-mode version 9 to make it easier to do some things we invented in org-ref (custom colored links, link keymaps, etc…). org-mode 9 is out now, and our standard. org-ref continues to get better.

We released ox-clip (https://melpa.org/#/ox-clip) which lets you copy formatted org-mode into applications like MS Word.

This year I hope to focus on integrating org-mode files with a backend database to make searching more powerful and to make it easier to create novel agendas. Another goal is figuring how to get human-readable, semantically marked up data in scientific documents. Finally, I hope to make some progress in developing interactive tutorials to help people learn how to use scimax.

4 vaspy

We rewrote the Python library for Vasp in ASE (https://github.com/jkitchin/vasp) and updated dft-book to use it. This new version is ase-compliant, and allows a more functional style of scripting with integration to the queue system.

5 Social media

5.1 github

It was a busy year for me on https://github.com/jkitchin. We use github for everything from software development to scientific paper writing.

5.2 Youtube

Wow, over 100,000 minutes of watch time on our videos in 2016! Check out our channel: https://www.youtube.com/channel/UCQp2VLAOlvq142YN3JO3y8w if you have not already.

Here are the most popular videos of 2016:

5.3 DONE kitchingroup.cheme.cmu.edu

Our research blog (this one) continues to grow bit by bit. We only had about 58 blog posts in 2016. For the first time it got slightly more pageviews than matlab.cheme.cmu.edu. That is pretty amazing since I have not added anything to matlab.cheme.cmu.edu since the summer of 2013!

Google analytics on kitchingroup.cheme.cmu.edu for 2016.

Google analytics on matlab.cheme.cmu.edu for 2016.

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

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