## 2022 in a nutshell

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2022 was an interesting one. I still did all of my teaching remotely, but spent more time going in to the office for meetings, and have gotten back to professional travel for meetings.

## 1. Research group accomplishments

Minjie Liu and Yilin Yang both defended their PhDs and graduated in 2022. Luyang Liu, Ananya Srivastava and Karan Waghela all completed their MS degrees and graduated in 2022 also. Congratulations to all of them!

Maya Bhat and I participated in an iCorps workshop on a concept around design of experiments.

We welcomed seven PhD students from the Ulissi group into the group while he is on leave at Meta. We also welcomed two new first year PhD students who will begin new collaborative projects with Carl Laird and Andy Gellman. The group is suddenly quite large!

## 2. Publications

Our work this past year was divided in a few efforts. We had some work in method development, e.g. in uncertainty quantification, automatic differentiation, and vector search.

1. Yang, Y., Achar, S. K., & Kitchin, J. R. (2022). Evaluation of the degree of rate control via automatic differentiation. AIChE Journal, 68(6). http://dx.doi.org/10.1002/aic.17653
2. Yang, Y., Liu, M., & Kitchin, J. R. (2022). Neural network embeddings based similarity search method for atomistic systems. Digital Discovery. http://dx.doi.org/10.1039/d2dd00055e
3. Zhan, N., & Kitchin, J. R. (2022). Model-specific to model-general uncertainty for physical properties. Industrial & Engineering Chemistry Research, 1–04706. http://dx.doi.org/10.1021/acs.iecr.1c04706

This collaborative work on large catalyst models is was especially exciting. Stay tuned for many advances in this area in 2023.

1. Kolluru, A., Shuaibi, M., Palizhati, A., Shoghi, N., Das, A., Wood, B., Zitnick, C. L., … (2022). Open challenges in developing generalizable large-scale machine-learning models for catalyst discovery. ACS Catalysis, 12(14), 8572–8581. http://dx.doi.org/10.1021/acscatal.2c02291

We also wrote some collaborative papers on our work in high-throughput discovery of hydrogen evolution catalysts and segregation.

1. Broderick, K., Lopato, E., Wander, B., Bernhard, S., Kitchin, J., & Ulissi, Z. (2022). Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptions. Applied Catalysis B: Environmental, 121959. http://dx.doi.org/10.1016/j.apcatb.2022.121959
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. Yilin Yang, Zhitao Guo, Andrew Gellman and John Kitchin, Simulating segregation in a ternary Cu-Pd-Au alloy with density functional theory, machine learning and Monte Carlo simulations, J. Phys. Chem. C, 126, 4, 1800-1808. (2022). https://pubs.acs.org/doi/abs/10.1021/acs.jpcc.1c09647

It was another big year in citations for us (https://scholar.google.com/citations?user=jD_4h7sAAAAJ)!

## 3. Point Breeze Publishing, LLC

I started a publishing company this year Point Breeze Publishing, LLC. It is a way for me to sell booklets on using Python in Science and Engineering and to sustain the effort it takes to produce these. It has been a modest success so far, with about a dozen booklets that can help anyone get started from basic Python usage through data science and machine learning and design of experiments. For reading this, you can get 50% off all purchases with checkout code 2022-nutshell. Check it out, and leave a review if you get anything!

I am still working out what the next steps for this are. I have written most of the pycse content I had in mind now, 400+ pages of it. I would like to get these booklets in the hands of more students, and my stealthy advertising scheme on Twitter and YouTube has not made that happen yet. I have some ideas around molecular simulation, maybe a reboot of the DFT-book?, maybe something around scimax? Who knows, stay tuned!

## 4. Outlook for 2023

There will be lots of changes for the Kitchin Research Group in 2023. We had a massive growth at the end of 2022 as we welcomed many members of the Ulissi research group into our group while he is on leave at Meta for 2023. Last summer we had one PhD student and three MS students. Now we have 10 PhD students. That means we will start a lot of new research directions in large catalyst models and everything they enable. We have started several collaborations in the area of design of experiments, and look forward to seeing these grow. It should be exciting!

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## New publication - Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptions

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Hydrogen adsorption energies have long been used in screening to identify promising hydrogen evolution catalysts. In this work we combine a high-throughput experimental study of 5300 different bimetallic catalysts with a high-throughput computational screen of 16M adsorption energies to see how well adsorption energies work for this purpose. We developed a workflow to combine these data sets that accounts for surface stability and adsorption site distributions on alloy surfaces. We find that thermodynamically favorable adsorption energies are necessary to observe high activity, but they are not sufficient, and do not always lead to high activity.

@article{broderick-2022-ident-limit,
author = {Kirby Broderick and Eric Lopato and Brook Wander and Stefan Bernhard and John Kitchin and Zachary Ulissi},
title = {Identifying Limitations in Screening High-Throughput Photocatalytic Bimetallic Nanoparticles With Machine-Learned Hydrogen Adsorptions},
journal = {Applied Catalysis B: Environmental},
volume = {nil},
number = {nil},
pages = {121959},
year = {2022},
doi = {10.1016/j.apcatb.2022.121959},
url = {http://dx.doi.org/10.1016/j.apcatb.2022.121959},
DATE_ADDED = {Thu Sep 22 07:46:56 2022},
}


9

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## New publication - Neural network embeddings based similarity search method for atomistic systems

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Searching for atomic structures in databases is like finding a needle in the haystack. It is difficult to construct a query that finds what you want, without finding nothing, or everything! It is difficult to use atomic coordinates because they are sensitive to translations, rotations and permutations. There are many ways to construct equivalent unit cells that also make it difficult to uniquely query materials.

In this paper we show how to construct queries for atomic structures that allow you to quickly find similar atomic structures. We achieve this by using invariant fingerprint vectors from machine learning models coupled with approximate nearest neighbor vector search algorithms. We apply it to molecules, bulk materials and adsorbates on surfaces. We show how the geometric similarity in found atomic systems leads to better data sets for building new machine learning models, and that the found systems tend to show geometric and electronic structure similarity.

@article{yang-2022-neural-networ,
author =       {Yilin Yang and Mingjie Liu and John R. Kitchin},
title =        {Neural Network Embeddings Based Similarity Search Method for
Atomistic Systems},
journal =      {Digital Discovery},
volume =       {},
number =       {},
pages =        {},
year =         2022,
doi =          {10.1039/d2dd00055e},
url =          {https://doi.org/10.1039/D2DD00055E},
DATE_ADDED =   {Mon Sep 12 17:21:30 2022},
}


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## New publication - Evaluation of the Degree of Rate Control via Automatic Differentiation

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Determining which steps in a chemical reaction network are important in controlling the reaction rate is challenging. The degree of rate control is a valuable tool for this, but it requires the derivatives of the reaction rate with respect to rate parameters. In many scenarios we do not have an analytical expression for the reaction rate, and even when we do the derivatives may be tedious to derive and implement. In this work, we show how to use automatic differentiation to address this difficulty, enabling straightforward evaluation of the degree of rate control and sensitivity analysis of complex reaction networks.

@article{yang-2022-evaluat,
author =       {Yang, Yilin and Achar, Siddarth K. and Kitchin, John R.},
title =        {Evaluation of the degree of rate control via automatic
differentiation},
journal =      {AIChE Journal},
volume =       {n/a},
number =       {n/a},
pages =        {e17653},
year =         2022,
keywords =     {catalysis, reaction kinetics},
doi =          {10.1002/aic.17653},
url =          {https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/aic.17653},
eprint =       {https://aiche.onlinelibrary.wiley.com/doi/pdf/10.1002/aic.17653},
abstract =     {Abstract The degree of rate control (DRC) quantitatively
identifies the kinetically relevant (sometimes known as
rate-limiting) steps of a complex reaction network. This
concept relies on derivatives which are commonly implemented
numerically, for example, with finite differences (FDs).
Numerical derivatives are tedious to implement, and can be
problematic, and unstable or unreliable. In this study, we
demonstrate the use of automatic differentiation (AD) in the
evaluation of the DRC. AD libraries are increasingly available
through modern machine learning frameworks. Compared with the
FDs, AD provides solutions with higher accuracy with lower
computational cost. We demonstrate applications in
steady-state and transient kinetics. Furthermore, we
illustrate a hybrid local-global sensitivity analysis method,
the distributed evaluation of local sensitivity analysis, to
assess the importance of kinetic parameters over an uncertain
space. This method also benefits from AD to obtain
high-quality results efficiently.}
}


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## New publication - Model-Specific to Model-General Uncertainty for Physical Properties

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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},
}