2021 in a nutshell

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I skipped the last two years of these it looks like. This year is ending in a better place than last year, so here is a nutshell for the past year!

1. Service accomplishments

This year I led an effort to create a Diversity, Equity and Inclusion committee within the Department of Chemical Engineering. I also serve on our college level DEI committee. It has been a challenging year to do this work mostly by Zoom, but I am proud of what our group has been able to do in the department and college. This isn't really new work, I have been involved in some way or another for six or seven years, but this past year was a major elevation of that work.

2. Research group accomplishments

Jenny (Ni) Zhan successfully defended her PhD and has graduated in 2021. Congratulations Jenny!

Two new MS students joined the group, Luyang Liu and Ananya Srivastava. They are starting a new research direction in natural language processing.

Omar Jimènez-Negrón finished his REU work with us, and has accepted a graduate position at Ga Tech.

3. Publications

This year was a better year than last year for publications. Our work this year covered a pretty broad range of topics from liquid metal alloys, to hydrogen production, to algorithms in uncertainty quantification and molecular simulation. My students and collaborators did a fantastic job on this work. Thank you!

  1. Zhan, N., & Kitchin, J. R. (2021). Origin of the Stokes-Einstein deviation in liquid Al-Si. Molecular Simulation, 1–11. http://dx.doi.org/10.1080/08927022.2021.2012572
  2. Bhat, M., Lopato, E., Simon, Z. C., Millstone, J. E., Bernhard, S., & Kitchin, J. R. (2022). Accelerated optimization of pure metal and ligand compositions for light-driven hydrogen production. Reaction Chemistry & Engineering. http://dx.doi.org/10.1039/d1re00441g
  3. Simon, Z. C., Lopato, E. M., Bhat, M., Moncure, P. J., Bernhard, S. M., Kitchin, J. R., Bernhard, S., Millstone, J. E. (2021). Ligand enhanced activity of in situ formed nanoparticles for photocatalytic hydrogen evolution. ChemCatChem, http://dx.doi.org/10.1002/cctc.202101551
  4. Zhan, N., & Kitchin, J. R. (2021). Uncertainty quantification in machine learning and nonlinear least squares regression models. AIChE Journal, http://dx.doi.org/10.1002/aic.17516
  5. Yang, Y., Omar A. Jimènez-Negrón, & Kitchin, J. R. (2021). Machine-learning accelerated geometry optimization in molecular simulation. The Journal of Chemical Physics, 154(23), 234704. http://dx.doi.org/10.1063/5.0049665
  6. Liu, M., Yang, Y., & Kitchin, J. R. (2021). Semi-grand canonical Monte Carlo simulation of the acrolein induced surface segregation and aggregation of AgPd with machine learning surrogate models. The Journal of Chemical Physics, 154(13), 134701. http://dx.doi.org/10.1063/5.0046440

4. org-ref version 3

I wrote and released org-ref version 3. The new version provides much better support for pre and post notes, and uses CSL for non-LaTeX exports, which is should be a big improvement over version 2. org-ref has been downloaded from MELPA more than 180,000 times now! It is amazing this tool I wrote to make my life easier has had such an impact.

The citation space in org-mode is a little complicated now because org-mode now has its own citation syntax. I tried hard to find a way to use that in org-ref, but I was unable to find a way that would also support legacy org-ref documents, and that also supports the cross-references, indexes, glossary and acronym features of org-ref. org-ref remains focused on supporting these in scientific documents, and staying close to the way they would look in LaTeX.

5. YouTube

Back in September I started making YouTube videos again around the release of org-ref v3. I started a new pycse playlist about Python. Check it out and subscribe if you like it! I also started making video briefs on our research papers and some research talks as an experiment. You can see here it made a difference in the channel traffic. At 2200 hours of watch time, this is an interesting kind of outreach and public impact from our work.

Why is this a good idea? Check out our publication page https://kitchingroup.cheme.cmu.edu/publications.html. You can see the Altmetric scores on our papers are generally pretty good, and even our newer papers are getting cited. It certainly has not hurt our citations per year according to Google Scholar.

It is also kind of fun.

6. Outlook for 2022

Things look ok for 2022. Probably I will start traveling for work again, so I hope to see some of you in person soon! I will limit travel pretty strictly, and remain vigilant about masking, so don't plan on any meals or outings to bars just yet!

I am teaching my Data science in chemical engineering course again and a new software engineering course this Spring which should be fun! Finally, stay tuned, hopefully I will have some news on a new venture I am trying to get started early in 2022!

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

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New publication - Origin of the Stokes-Einstein Deviation in Liquid Al-Si

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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.

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New publication - Accelerated Optimization of Pure Metal and Ligand Compositions for Light-driven Hydrogen Production

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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.

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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},
}

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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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