## 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 =          "http://dx.doi.org/10.1007/s00799-016-0173-7"
}


org-mode source

Org-mode version = 8.3.4

## Alex Hallenbeck successfully defended his PhD

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Alex successfully defended his PhD on Tuesday, April 19, 2016!

Title: Micro-scale Approaches to the Bench-scale Evaluation of CO2 Capture System Properties

Committee Members: Professor John Kitchin (chair), Professor Shelley Anna, Professor Neil Donahue, and Professor Newell Washburn.

Congratulations Alex!

org-mode source

Org-mode version = 8.2.10

## 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 =          {http://dx.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},
}


org-mode source

Org-mode version = 8.2.10

## New publication in International Journal of Quantum Chemistry

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It is well known that DFT calculations are expensive, which limits the size of the calculations that can be performed, the number of them that can be performed, and their use in simulation methods such as molecular dynamics. Molecular potentials are more suitable for these types of simulations, but they must be parameterized by some means. In this paper, we use a database of DFT calculations to train ReaxFF and a neural network potential. We compare and contrast these potentials with respect to their accuracy, trainability, and speed of calculation with application to properties of Au bulk, cluster and surface properties. There are clear tradeoffs with these two approaches, but both have advantages for different purposes. Congratulations Jake and Mitch! See the paper here: http://dx.doi.org/10.1002/qua.25115 .

@article {boes-2016-neural-reaxf,
author =       {Boes, Jacob R. and Groenenboom, Mitchell C. and Keith, John A.
and Kitchin, John R.},
title =        {Neural network and ReaxFF comparison for Au properties},
journal =      {International Journal of Quantum Chemistry},
issn =         {1097-461X},
url =          {http://dx.doi.org/10.1002/qua.25115},
doi =          {10.1002/qua.25115},
pages =        {n/a--n/a},
keywords =     {Kohn-Sham density functional theory, neural networks, reactive
force fields, potential energy surfaces, machine learning},
year =         2016,
}


org-mode source

Org-mode version = 8.2.10

## Zhongnan Xu selected for the 2015-16 Dighe Fellowship in Chemical Engineering

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Congratulations Zhongnan!