## New publication in SoftwareX

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Bibliometrics are increasingly used to quantify scholarly productivity. In this paper, we introduce a Python package called pybliometrics that provides a scriptable interface to Scopus to aggregate publication data for analysis. The package provides pretty comprehensive coverage of the APIs for author, abstract, affiliation and citation queries. The manuscript shows examples for downloading abstracts in bulk, building collaboration network graphs, and analyzing citation trends. You have to get a key from Scopus to access their databases, and the package provides some guidance on how to get it and configure the package. If you are interested in bibliometrics, this package may be useful to you!

@article{rose-2019-pybliom,
author =       {Michael E. Rose and John R. Kitchin},
title =        {Pybliometrics: Scriptable Bibliometrics Using a Python
Interface To Scopus},
journal =      {SoftwareX},
volume =       10,
number =       {nil},
pages =        100263,
year =         2019,
doi =          {10.1016/j.softx.2019.100263},
url =          {https://doi.org/10.1016/j.softx.2019.100263},
DATE_ADDED =   {Mon Jul 8 07:06:58 2019},
}


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## New publication in Nature Catalysis

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Machine learning (ML) is impacting many fields, including catalysis. In this comment, I briefly discuss the major directions that ML is influencing the field of catalysis, along with some outlook on future directions. There were strict word and reference limits, so apologies in advance if I left out your work!

@article{kitchin-2018-machin-learn-catal,
author =       {John R. Kitchin},
title =        {Machine Learning in Catalysis},
journal =      {Nature Catalysis},
volume =       1,
number =       4,
pages =        {230-232},
year =         2018,
doi =          {10.1038/s41929-018-0056-y},
url =          {https://doi.org/10.1038/s41929-018-0056-y},
DATE_ADDED =   {Mon Apr 16 12:50:43 2018},
}


You can see a read-only version of the paper here: https://rdcu.be/LGrM

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## New publication in Catalysis Today

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In this paper we continue our exploration of using high-dimensional neural networks (NN) to model metal surface properties. Our first work started with modeling Au in a variety of structures using ReaxFF and a NN boes-2016-neural-networ. We then modeled atomic oxygen adsorbates on a Pd (111) surface boes-2017-neural-networ, and segregation of an Au-Pd alloy surface boes-2017-model-segreg. Our goal throughout this work has been to systematically build up complexity in the systems we are modeling, and to explore the limitations of these potentials for modeling surfaces. This current work happened in parallel with those works, and focused on modeling Pd adatom diffusion on Pd(111) surfaces. We show another example of how to train a neural network, and then to use it model the temperature dependent diffusion of adatoms on a metal surface using molecular dynamics and Arrhenius analysis.

@article{gao-2018-model-pallad,
author =       {Tianyu Gao and John R. Kitchin},
title =        {Modeling Palladium Surfaces With Density Functional Theory,
Neural Networks and Molecular Dynamics},
journal =      {Catalysis Today},
year =         2018,
doi =          {10.1016/j.cattod.2018.03.045},
url =          {https://doi.org/10.1016/j.cattod.2018.03.045},
DATE_ADDED =   {Sun Apr 1 18:47:55 2018},
}


# Bibliography

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## New publication in Topics in Catalysis

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Single atom alloys are alloys in the extreme dilute limit, where single atoms of a reactive metal are surrounded by comparatively unreactive metals. This makes the single reactive atoms like single atom sites where reactions can occur. These sites are interesting because they are metallic, but their electronic structure is different than the atoms in more concentrated alloys. This means there is the opportunity for different, perhaps better catalytic performance for the single atom alloys. In this paper, we studied the electronic structure and some representative reaction pathways on a series of single atom alloy surfaces.

@article{Thirumalai2018,
author =       "Thirumalai, Hari and Kitchin, John R.",
title =        "Investigating the Reactivity of Single Atom Alloys Using
Density Functional Theory",
journal =      "Topics in Catalysis",
year =         "2018",
month =        "Jan",
day =          "25",
abstract =     "Single atom alloys are gaining importance as atom-efficient
catalysts which can be extremely selective and active towards
the formation of desired products. They possess such desirable
characteristics because of the presence of a highly reactive
single atom in a less reactive host surface. In this work, we
calculated the electronic structure of several representative
single atom alloys. We examined single atom alloys of gold,
silver and copper doped with single atoms of platinum,
palladium, iridium, rhodium and nickel in the context of the
d-band model of Hammer and N{\o}rskov. The reactivity of these
alloys was probed through the dissociation of water and nitric
oxide and the hydrogenation of acetylene to ethylene. We
observed that these alloys exhibit a sharp peak in their atom
projected d-band density of states, which we hypothesize could
be the cause of high surface reactivity. We found that the
d-band centers and d-band widths of these systems correlated
linearly as with other alloys, but that the energy of
adsorption of a hydrogen atom on these surfaces could not be
correlated with the d-band center, or the average reactivity
of the surface. Finally, the single atom alloys, with the
exception of copper--palladium showed good catalytic behavior
by activating the reactant molecules more strongly than the
bulk atom behavior and showing favorable reaction pathways on
the free energy diagrams for the reactions investigated.",
issn =         "1572-9028",
doi =          "10.1007/s11244-018-0899-0",
url =          "https://doi.org/10.1007/s11244-018-0899-0"
}


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## New publication in Molecular Simulation

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This paper is our latest work using neural networks in molecular simulation. In this work, we build a Behler-Parinello neural network potential of bulk zirconia. The potential can describe several polymorphs of zirconia, as well as oxygen vacancy defect formation energies and diffusion barriers. We show that we can use the potential to model oxygen vacancy diffusion using molecular dynamics at different temperatures, and to use that data to estimate the effective diffusion activation energy. This is further evidence of the general utility of the neural network-based potential for molecular simulations with DFT accuracy.

@article{wang-2018-densit-funct,
author =       {Chen Wang and Akshay Tharval and John R. Kitchin},
title =        {A Density Functional Theory Parameterised Neural Network Model
of Zirconia},
journal =      {Molecular Simulation},
volume =       0,
number =       0,
pages =        {1-8},
year =         2018,
doi =          {10.1080/08927022.2017.1420185},
url =          {https://doi.org/10.1080/08927022.2017.1420185},
eprint =       { https://doi.org/10.1080/08927022.2017.1420185 },
publisher =    {Taylor \& Francis},
}