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.
                  Finally, metadata about the embedded files can be
                  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"
}

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

org-mode source

Org-mode version = 8.3.4

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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!

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

org-mode source

Org-mode version = 8.2.10

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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},
}

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

org-mode source

Org-mode version = 8.2.10

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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,
}

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

org-mode source

Org-mode version = 8.2.10

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