New Publication - An Inverse Mapping Approach for Process Systems Engineering Using Automatic Differentiation and the Implicit Function Theorem

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Solving inverse problems, where we know what outputs we want from a model and seek the inputs that provide them, is a difficult task. A conventional approach to this problem is to use a nonlinear program (NLP) solver to iteratively find the inputs for a specific output. If you seek a desired output space, then you must solve the NLP many times to map out the corresponding input space. This is often expensive, and tedious to perform. In this work, we demonstrate a new approach to solving this problem that avoids the NLP formulation, and is faster. The idea is simple; we compute a system of differential equations that maps how the input space changes with the output space. Then from a single known point we can integrate a path in the output space to automatically trace the corresponding path in the input space! We compute the system of differential equations using automatic differentiation, and the implicit derivative theorem. We show two examples of this using a steady state continuously stirred tank reactor, which is a set of nonlinear algebraic equations that define the output space from input variables, and another plug flow reactor where the output space is defined by a set of differential equations that must be numerically integrated. In both cases we use automatic differentiation to define the system of ODEs that relate outputs and inputs, and show that the path integration method developed here is as accurate and faster than even the best NLP approach. The idea in this paper is general and applicable to many other systems, not just chemical reactors.

@article{alves-2023,
  author =       {Alves, Victor and Kitchin, John R. and Lima, Fernando V.},
  title =        {An inverse mapping approach for process systems engineering
                  using automatic differentiation and the implicit function
                  theorem},
  journal =      {AIChE Journal},
  year =         2023,
  volume =       {n/a},
  number =       {n/a},
  pages =        {e18119},
  keywords =     {automatic differentiation, implicit function theorem, inverse
                  mapping, inverse problems, process systems engineering},
  doi =          {10.1002/aic.18119},
  url =
                  {https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/aic.18119}
}

Copyright (C) 2023 by John Kitchin. See the License for information about copying.

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New publication - WhereWulff A Semiautonomous Workflow for Systematic Catalyst Surface Reactivity under Reaction Conditions

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Suppose you want to explore metal oxides as potential water oxidation electrocatalysts. There are many steps to do this. You can use databases of materials to get compositions and structures, but for each one you have to determine the ground state structure, including magnetic states, for each bulk structure, and filter out bulk materials that are not stable under water oxidation conditions. Then, using the remaining structures you have to construct slabs and determine which surfaces are likely to be stable, and most relevant. After that you have to compute adsorption energies on those surfaces to see which surfaces have the most relevant reactivity (while also being stable). This results in hundreds to thousands of calculations that depend on each other in important ways. It is very useful to use software workflow tools to facilitate and manage this process. In this paper we develop a workflow like this for exploring metal oxides for water oxidation. The software is open source and available at https://github.com/ulissigroup/wherewulff.

The paper is free to read for 6 months at https://pubs.acs.org/doi/10.1021/acs.jcim.3c00142.

@article{sanspeur-2023,
  author = {Rohan Yuri Sanspeur and Javier Heras-Domingo and John R. Kitchin and Zachary Ulissi},
  title = {wherewulff: a Semiautonomous Workflow for Systematic Catalyst Surface Reactivity Under Reaction Conditions},
  journal = {Journal of Chemical Information and Modeling},
  volume = {nil},
  number = {nil},
  pages = {nil},
  year = {2023},
  doi = {10.1021/acs.jcim.3c00142},
  url = {http://dx.doi.org/10.1021/acs.jcim.3c00142},
  DATE_ADDED = {Sun Apr 16 09:17:23 2023},
}

Copyright (C) 2023 by John Kitchin. See the License for information about copying.

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New publication - High throughput discovery of ternary Cu-Fe-Ru alloy catalysts for photo-driven hydrogen production

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Finding new ways to make hydrogen with renewable energy and renewable feedstocks using earth abundant materials remains a challenge in catalysis today. Metal nanoparticles are common heterogeneous catalysts for hydrogen production, and their properties can often be improved by using multiple metals at a time. In this work we show a high-throughput experimental approach to discovering a ternary alloy catalyst containing earth abundant metals that is more active at producing hydrogen than any of the pure metals it is made of. It a surprising discovery because these metals are not typically miscible, and they do not form a well characterized material, but rather a distribution of particle sizes and compositions.

@article{bhat-2023-high-throug,
  author = {Maya Bhat and Zoe C Simon and Savannah Talledo and Riti Sen and Jacob H. Smith and Stefan Bernhard and Jill E Millstone and John R Kitchin},
  title = {High Throughput Discovery of Ternary Cu-Fe-Ru Alloy Catalysts for Photo-Driven Hydrogen Production},
  journal = {Reaction Chemistry \& Engineering},
  volume = {nil},
  number = {nil},
  pages = {nil},
  year = {2023},
  doi = {10.1039/d3re00059a},
  url = {http://dx.doi.org/10.1039/D3RE00059A},
  DATE_ADDED = {Sat Apr 15 07:55:55 2023},
}

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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!

Copyright (C) 2023 by John Kitchin. See the License for information about copying.

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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

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

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