New publication - Ligand Enhanced Activity of in Situ Formed Nanoparticles for Photocatalytic Hydrogen Evolution

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In this paper we show that we can stabilize in situ synthesized nanoparticles by including stabilizing ligands in the synthesis. This gives the in situ particles comparable stability to pre-synthesized particles, while retaining the flexibility of the in situ synthesis. We show that not all stabilizing ligands work for all metals, although PEGSH is a reasonable starting point for the metals we considered in this work. Additionally, we show that the incorporation of PEGSH can make some metals that appear inactive without stabilization due to precipitation, become active when stabilized. Although the addition of stabilizing ligands complicates the optimization of the synthesis conditions, it leads to more reproducible, and in some cases better results than leaving them out.

@article{simon-2021-ligan-enhan,
  author =       {Zoe C. Simon and Eric M. Lopato and Maya
                  Bhat and Paige J. Moncure and Sarah M. Bernhard and John R.
                  Kitchin and Stefan Bernhard and Jill Millstone},
  title =        {Ligand Enhanced Activity of in Situ Formed Nanoparticles for
                  Photocatalytic Hydrogen Evolution},
  journal =      {ChemCatChem},
  volume =       {},
  number =       {},
  pages =        {},
  year =         2021,
  doi =          {10.1002/cctc.202101551},
  url =          {http://dx.doi.org/10.1002/cctc.202101551},
  DATE_ADDED =   {Mon Nov 29 17:01:16 2021},
}

Copyright (C) 2021 by John Kitchin. See the License for information about copying.

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New publication - Uncertainty quantification in machine learning and nonlinear least squares regression models

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Say you have acquired some data, and fitted a nonlinear model to it. The fit looks good, but how good are predictions from the model? In high dimensional space it is tricky to tell if you are extrapolating, what should you do? First, read this paper! We illustrate a simple tool called the delta method that can help you estimate the prediction uncertainty from your model using automatic differentiation to get the required derivatives. We show some examples, and how you can use this method to refine what data you should use in fitting your models. We even show how to handle some tricky cases with models where the Hessian is not invertable! The examples are from molecular simulation, but the approach is general and should work for other models too.

@article{zhan-2021-uncer-quant,
  author =       {Ni Zhan and John R. Kitchin},
  title =        {Uncertainty Quantification in Machine Learning and Nonlinear
                  Least Squares Regression Models},
  journal =      {AIChE Journal},
  volume =       {},
  number =       {},
  pages =        {},
  year =         2021,
  doi =          {10.1002/aic.17516},
  url =          {http://dx.doi.org/10.1002/aic.17516},
  DATE_ADDED =   {Mon Nov 8 08:51:21 2021},
}

Checkout the video brief here:

Copyright (C) 2021 by John Kitchin. See the License for information about copying.

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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},
}

Copyright (C) 2019 by John Kitchin. See the License for information about copying.

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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

Copyright (C) 2018 by John Kitchin. See the License for information about copying.

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