The Kitchin Research Group

When we fit models to data there are two kinds of uncertainty: the kind that represents uncertainty in the data, e.g. random noise that we cannot fit, and uncertainty in the model, e.g. are we using the right one. With a physics based model, we get model-specific estimates of uncertainty. We show in this paper how to think about and quantify these kinds of errors, and particularly how to use Bayesian models like a Gaussian process to get a model-general error when making predictions about physical properties.

@article{zhan-2022-model-specif,
  author =       {Ni Zhan and John R. Kitchin},
  title =        {Model-Specific To Model-General Uncertainty for Physical
                  Properties},
  journal =      {Industrial \& Engineering Chemistry Research},
  volume =       {nil},
  number =       {nil},
  pages =        {acs.iecr.1c04706},
  year =         2022,
  doi =          {10.1021/acs.iecr.1c04706},
  url =          {http://dx.doi.org/10.1021/acs.iecr.1c04706},
  DATE_ADDED =   {Sun Feb 13 12:08:27 2022},
}

Copyright (C) 2022 by John Kitchin. See the License for information about copying.

org-mode source

Org-mode version = 9.5.1

In this paper we combine density functional theory, machine learning, Monte Carlo simulations and experimental data to study segregation in a ternary alloy Cu-Pd-Au surface across the composition space. We found varying agreement and disagreement between tehe simulated and experimental results, and discuss the origins of these. Overall, Au segregates significantly across the composition space, and we learned a lot about the contributions to the discrepancies observed in Cu-Pd segregation and Au-Cu segregration.

Find the paper at https://pubs.acs.org/doi/10.1021/acs.jpcc.1c09647.

@article{yang-2022-simul-segreg,
  author =       {Yilin Yang and Zhitao Guo and Andrew J. Gellman and John R.
                  Kitchin},
  title =        {Simulating Segregation in a Ternary Cu-Pd-Au Alloy With
                  Density Functional Theory, Machine Learning, and Monte Carlo
                  Simulations},
  journal =      {The Journal of Physical Chemistry C},
  volume =       {nil},
  number =       {nil},
  pages =        {acs.jpcc.1c09647},
  year =         2022,
  doi =          {10.1021/acs.jpcc.1c09647},
  url =          {http://dx.doi.org/10.1021/acs.jpcc.1c09647},
  DATE_ADDED =   {Thu Jan 20 12:39:49 2022},
}

Copyright (C) 2022 by John Kitchin. See the License for information about copying.

org-mode source

Org-mode version = 9.5.1

In many liquid metal alloys the diffusivity and viscosity are related to each other through the Stokes–Einstein–Sutherland (SES) equation. This is useful because it is difficult to measure these properties in liquid metals, and the correlation can be used in design. Near the melting point, however, this relation often fails. In this work we use molecular dynamics to investigate deviations in the SES for molten Si-Al. We find that the viscosity changes faster than the diffusivity due to the formation of atomic clusters. These clusters cause the SES deviation in this liquid alloy system.

@article{zhan-2021-origin-stokes,
  author =       {Ni Zhan and John R. Kitchin},
  title =        {Origin of the Stokes-Einstein Deviation in Liquid Al-Si},
  journal =      {Molecular Simulation},
  volume =       {},
  number =       {},
  pages =        {1-11},
  year =         2021,
  doi =          {10.1080/08927022.2021.2012572},
  url =          {http://dx.doi.org/10.1080/08927022.2021.2012572},
  DATE_ADDED =   {Fri Dec 17 07:41:39 2021},
}

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

org-mode source

Org-mode version = 9.5.1

In this new collaborative work we show how we combine a high-throughput photoreactor with a design of experiment approach to efficiently find optimal in situ synthesis compositions for making metal nanoparticle catalysts for light-driven hydrogen production. The challenge in this work is there are several components that interact with each other including metal salts, stabilizing ligands and photosensitizers, as well as some noise in the measurements. It is difficult to optimize these components one at a time, so we use a design of experiment approach. The high-throughput data enables us to explore the composition space around the optimum and to identify specific compositions for focused and expensive characterization efforts. We use this on Au, Cu, Fe and Ni and show that all of them can have high activity when they are independently optimized. It is interesting to note that Au and Cu form stable metallic nanoparticles, but Ni appears to form oxide nanoparticles and Fe appears to form sulfide nanoparticles.

@article{bhat-2022-accel-optim,
  author =       {Maya Bhat and Eric Lopato and Zoe C Simon and Jill E Millstone
                  and Stefan Bernhard and John R Kitchin},
  title =        {Accelerated Optimization of Pure Metal and Ligand Compositions
                  for Light-Driven Hydrogen Production},
  journal =      {Reaction Chemistry \& Engineering},
  volume =       {},
  number =       {},
  pages =        {},
  year =         2022,
  doi =          {10.1039/d1re00441g},
  url =          {http://dx.doi.org/10.1039/D1RE00441G},
  DATE_ADDED =   {Mon Nov 29 17:00:12 2021},
}

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

org-mode source

Org-mode version = 9.5

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.

org-mode source

Org-mode version = 9.5