## 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.

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.

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:

### 5.3DONE 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.

org-mode source

Org-mode version = 9.0

## 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 =          { http://dx.doi.org/10.1021/acscatal.6b00509 },
eprint =       { http://dx.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 =          "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},
}