## 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},
}


org-mode source

Org-mode version = 9.0.3

## Using Twitter cards for better tweets

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@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",
}


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 ;)

org-mode source

Org-mode version = 8.3.5

## New publication in ACS Catalysis

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DFT calculations are not exact, and the uncertainties in a calculation can impact conclusions you draw from the results. In this work, we quantify the uncertainty in the adsorption energies on Pt(111) and (100) of oxygenated intermediates relevant to the oxygen reduction reaction mechanism. We then propagate these uncertainties to the volcano plot of activity that results from them, and show how this approach helps inform us about the reliability of the predicted trends.

@article{deshpande16_quant_uncer_activ_volcan_relat,
author =       {Siddharth Deshpande and John R. Kitchin and Venkatasubramanian
Viswanathan },
title =        {Quantifying Uncertainty in Activity Volcano Relationships for
Oxygen Reduction Reaction},
journal =      {ACS Catalysis},
volume =       0,
number =       {ja},
pages =        {null},
year =         2016,
doi =          {10.1021/acscatal.6b00509},
URL =          { https://doi.org/10.1021/acscatal.6b00509 },
eprint =       { https://doi.org/10.1021/acscatal.6b00509 },
}


org-mode source

Org-mode version = 8.3.4

## New publication in International Journal of Digital Libraries

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We have a new paper out on using org-mode in publishing. The idea is to use org-mode to automate data embedding in publications. For example, in org-mode tables can serve as data sources. We show how you can automatically embed the tables as csv files in PDF or HTML when the org-file is exported. Similarly, all the code blocks are embedded as extractable files at export time. This increases the reusability of the data and code in papers.

Check out the preprint here: https://github.com/KitchinHUB/kitchingroup-66

@Article{Kitchin2016,
author =       "Kitchin, John R. and Van Gulick, Ana E. and Zilinski, Lisa D.",
title =        "Automating data sharing through authoring tools",
journal =      "International Journal on Digital Libraries",
year =         "2016",
pages =        "1--6",
abstract =     "In the current scientific publishing landscape, there is a
need for an authoring workflow that easily integrates data and
code into manuscripts and that enables the data and code to be
published in reusable form. Automated embedding of data and
code into published output will enable superior communication
and data archiving. In this work, we demonstrate a proof of
concept for a workflow, org-mode, which successfully provides
this authoring capability and workflow integration. We
illustrate this concept in a series of examples for potential
uses of this workflow. First, we use data on citation counts
to compute the h-index of an author, and show two code
examples for calculating the h-index. The source for each
example is automatically embedded in the PDF during the export
of the document. We demonstrate how data can be embedded in
image files, which themselves are embedded in the document.
automatically included in the exported PDF, and accessed by
computer programs. In our customized export, we embedded
metadata about the attached files in the PDF in an Info field.
A computer program could parse this output to get a list of
embedded files and carry out analyses on them. Authoring tools
such as Emacs + org-mode can greatly facilitate the
integration of data and code into technical writing. These
tools can also automate the embedding of data into document
formats intended for consumption.",
issn =         "1432-1300",
doi =          "10.1007/s00799-016-0173-7",
url =          "https://doi.org/10.1007/s00799-016-0173-7"
}


org-mode source

Org-mode version = 8.3.4

## New publication in AICHE J.

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This paper uses a kernel regression method trained on a large set of DFT calculations from the Materials Project to design new materials. A notable feature of this approach is it opens the door to inverse design, since the mathematical form of the regression is accessible. In the paper we predict electronic properties and elastic constants for a large number of metal oxides. Congratulations Bruno for this work!

See the paper here: http://onlinelibrary.wiley.com/doi/10.1002/aic.15251/full

@article {AIC:AIC15251,
author =       {Calfa, Bruno A. and Kitchin, John R.},
title =        {Property prediction of crystalline solids from composition and
crystal structure},
journal =      {AIChE Journal},
issn =         {1547-5905},
url =          {https://doi.org/10.1002/aic.15251},
doi =          {10.1002/aic.15251},
pages =        {n/a--n/a},
keywords =     {crystal property prediction, data analytics, kernel
regression, crystal composition and structure, exhaustive
enumeration algorithm},
year =         {2016},
}