The Kitchin Research Group

I am excited to launch a new project this year: https://pointbreezepubs.gumroad.com/. This venture exists to publish booklets to help people learn how to use Python in science and engineering. Why am I doing this? I think computing skills are as important as domain knowledge today. I have spent the last 25 years learning how to use computing in science and engineering, and I have been teaching other people how to do that for the past 15 years. In that time huge changes have occurred in both hardware and software, data science and machine learning have emerged and they are playing a role almost everywhere. It has never been more important for people to learn how to use computers than it is today.

Solving science and engineering problems with computers requires first, and foremost, domain knowledge. Without that, you won't know what problem to solve, or know if the solution makes sense after you get it. It also requires complementary computational skills. Similar to a math education, where you first learn algebra, then geometry, and then calculus, you should not simply jump into data science or machine learning without a foundation of computational skills. I think of these skills like this:

Level 1 is basic programming in Python. Although everything rests on this foundation, this level alone does not solve many interesting problems in science and engineering. You have to combine this with some mathematical domain knowledge in level 2 to get to those. Levels 1 and 2 are adequate for many common science and engineering problems. If you move in a direction of specialization, especially using computers, it is often found that even though levels 1 and 2 are adequate, they may become tedious in large problems, or when used frequently. The solution is almost always to create an abstraction, a framework, that removes the tedium, and is more convenient to use. This is level 3. The abstraction hides a lot of detail, and can make it more difficulty to customize behavior, but the payoff is convenience. Finally, and this is debatable, I think level 4 contains today's machine learning frameworks. I separate them from level 3 because they are often used to write tools used in level 3, and they typically require skills that are not learned in level 2.

So how does Point Breeze Publishing help here? We have published the first step in this booklet:

1. Introduction to Python computations in science and engineering

These booklets come in two forms: PDF and ipynb. The first is traditional, and easy to read. The second format is less traditional, but it allows you to execute the code yourself to see how it works.

Over the next few weeks, I will publish these additional booklets, with some supplementary materials.

1. Intermediate Python computations in science and engineering
2. Python computations for lab courses
3. Ordinary differential equations
4. Optimization in Python

These booklets cover most of what chemical engineering undergraduate students need (my opinion of course), and lay a solid foundation for levels 3 and 4 as described above. These are not reference books, or documentation from the packages. They are a guided tour through the topics to help you get started, learn how to think about these topics, and become a self-learner in them.

Where to from here? Over the summer, I will work on some more advanced booklets on data science and machine learning. I will also explore some other ways to deliver these booklets. I use PDF and ipynb now because I know how to do it, but other options exist.

This whole venture is possible because of scimax, and I hope this becomes a route to publish books about using scimax for scientists and engineers.

Want to keep up with what we are doing?

1. Head over to https://pointbreezepubs.gumroad.com/ and follow it.
2. Head over to https://www.youtube.com/channel/UCQp2VLAOlvq142YN3JO3y8w and follow the channel.
3. Follow me at https://twitter.com/johnkitchin
4. Follow me at https://www.linkedin.com/in/john-kitchin-6b959038/

If you are in those places, you won't miss what is happening! Thanks for coming along!

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

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Org-mode version = 9.5.1

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!

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.

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

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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.

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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.

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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.

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Org-mode version = 9.5