The Kitchin Research Group

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.

org-mode source

Org-mode version = 9.5.5

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.

org-mode source

Org-mode version = 9.5.5

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},
}

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

org-mode source

Org-mode version = 9.5.5

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},
}

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

org-mode source

Org-mode version = 9.5.1

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.

You can read more about this work here (it is Open Access): Yilin Yang, Mingie Liu, John Kitchin, Digital Discovery, 2022, https://doi.org/10.1039/D2DD00055E

@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},
}

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

org-mode source

Org-mode version = 9.5.1