The Kitchin Research Group

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.

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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 =          {https://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.

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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: https://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 =          {https://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.

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Org-mode version = 8.2.10

Congratulations Zhongnan!

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

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Org-mode version = 8.2.10

Zhongnan successfully defended his PhD dissertation yesterday!

Title: Towards Accurate Predictions and Mechanistic Understanding of the Catalytic Activity of Transition Metal Oxides.

Committee Members: Professor John Kitchin (chair), Professor Andrew Gellman, Professor Erik Ydstie, Professor Paul Salvador

Congratulations!

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

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Org-mode version = 8.2.10