The Kitchin Research Group

Water splitting is a crucial technology for renewable hydrogen generation. Under acid conditions most metals that would be used for the oxidation reaction tend to dissolve, limiting their utility. Iridium oxide is widely regarded as the most active and stable material, but it is very expensive. Ruthenium oxide is the next most active material, but it is less stable and tends to dissolve over time. In this work we studied 36,000 mixed metal oxides to identify potential compositions that would stabilize ruthenium from dissolution. We found a candidate Ru_0.6Cr_0.2Ti_0.2O₂ with promise. We synthesized this material anf show that it has superior stability and improved activity compared to RuO₂.

@article{abed-2024-pourb-machin,
  author =       {Jehad Abed and Javier Heras-Domingo and Rohan Yuri Sanspeur
                  and Mingchuan Luo and Wajdi Alnoush and Debora Motta Meira and
                  Hsiaotsu Wang and Jian Wang and Jigang Zhou and Daojin Zhou
                  and Khalid Fatih and John R. Kitchin and Drew Higgins and
                  Zachary W. Ulissi and Edward H. Sargent},
  title =        {Pourbaix Machine Learning Framework Identifies Acidic Water
                  Oxidation Catalysts Exhibiting Suppressed Ruthenium
                  Dissolution},
  journal =      {Journal of the American Chemical Society},
  volume =       {nil},
  number =       {nil},
  pages =        {nil},
  year =         2024,
  doi =          {10.1021/jacs.4c01353},
  url =          {http://dx.doi.org/10.1021/jacs.4c01353},
  DATE_ADDED =   {Sat Jun 8 13:12:31 2024},
}

Copyright (C) 2024 by John Kitchin. See the License for information about copying.

org-mode source

Org-mode version = 9.7-pre

Alloy segregation is hard to model; you need large unit cells to get fine-grained compositions, and a lot of DFT calculations to sample all the possible configurations. The challenge gets even bigger when you consider a ternary alloy, and want to model segregation over the entire ternary alloy composition space, and across multiple surfaces. We tackle this problem in this work using the Open Catalyst Project machine learned potentials (MLPs) that are fine-tuned on a few thousand DFT calculations. We use those MLPs with Monte Carlo simulations to predict segregation on three ternary alloy (111), (110), and (100) surfaces. We compare our predictions to experimental measurements on a polycrystalline CSAF. Similar to previous work of ours, we find qualitative and quantitative agreements in some composition ranges, and disagreement in others. We trace the limitations of quantitative accuracy to limitations in the DFT calculations.

@article{broderick-2024-surfac-segreg,
  author =       {Kirby Broderick and Robert A. Burnley and Andrew J. Gellman
                  and John R. Kitchin},
  title =        {Surface Segregation Studies in Ternary Noble Metal Alloys:
                  Comparing Dft and Machine Learning With Experimental Data},
  journal =      {ChemPhysChem},
  volume =       {nil},
  number =       {nil},
  pages =        {nil},
  year =         2024,
  doi =          {10.1002/cphc.202400073},
  url =          {http://dx.doi.org/10.1002/cphc.202400073},
  DATE_ADDED =   {Thu Jun 6 08:37:37 2024},
}

Copyright (C) 2024 by John Kitchin. See the License for information about copying.

org-mode source

Org-mode version = 9.7-pre

You can get higher rates of reaction on a catalyst by dynamically changing the adsorbate and reaction energetics. It has been an open challenge though to find ways to obtain the optimal waveform. In this work we present a problem formulation that is easy to solve and optimize waveforms in programmable catalysis.

https://doi.org/10.1021/acs.jpcc.4c01543

@article{tedesco-2024-cyclic-stead,
  author =       {Carolina Colombo Tedesco and John R. Kitchin and Carl D.
                  Laird},
  title =        {Cyclic Steady-State Simulation and Waveform Design for
                  Dynamic/programmable Catalysis},
  journal =      {The Journal of Physical Chemistry C},
  volume =       {nil},
  number =       {nil},
  pages =        {nil},
  year =         2024,
  doi =          {10.1021/acs.jpcc.4c01543},
  url =          {http://dx.doi.org/10.1021/acs.jpcc.4c01543},
  DATE_ADDED =   {Thu May 23 16:35:52 2024},
}

Copyright (C) 2024 by John Kitchin. See the License for information about copying.

org-mode source

Org-mode version = 9.7-pre

Geometry optimization is an expensive part of DFT; each step requires a DFT step. The Open Catalyst Project provides pre-trained machine learned potentials that provide cheap forces for a broad range of metallic, intermetallic materials. In this work we use models trained on the OC20 dataset to accelerate geometry optimization of materials outside that domain including larger adsorbates, oxides, and zeolites. With fine-tuning, we are able to reduce the number of DFT calls required substantially for these systems.

@article{10.1088/2632-2153/ad37f0,
        author={Wang, Xiaoxiao and Musielewicz, Joseph and Tran, Richard and Ethirajan, Sudheesh Kumar and Fu, Xiaoyan and Mera, Hilda and Kitchin, John R and Kurchin, Rachel and Ulissi, Zachary W},
        title={Generalization of Graph-Based Active Learning Relaxation Strategies Across Materials},
        journal={Machine Learning: Science and Technology},
        url={http://iopscience.iop.org/article/10.1088/2632-2153/ad37f0},
        year={2024}
}

Copyright (C) 2024 by John Kitchin. See the License for information about copying.

org-mode source

Org-mode version = 9.7-pre

Modeling magnetic materials with DFT is hard. In this work we develop a machine learning approach to predicting magnetic properties of materials based on their structure. Our two stage model first predicts if a material is magnetic, and then if it is, what the magnetic moments on each atom are. We show this can lead to faster and lower energy DFT solutions.

@article{sanspeur-2024-circum-data,
  author =       {Rohan Yuri Sanspeur and John R Kitchin},
  title =        {Circumventing Data Imbalance in Magnetic Ground State Data for
                  Magnetic Moment Predictions},
  journal =      {Machine Learning: Science and Technology},
  volume =       {5},
  number =       {1},
  pages =        {015023},
  year =         2024,
  doi =          {10.1088/2632-2153/ad23fb},
  url =          {http://dx.doi.org/10.1088/2632-2153/ad23fb},
  DATE_ADDED =   {Tue Feb 6 20:13:47 2024},
}

Copyright (C) 2024 by John Kitchin. See the License for information about copying.

org-mode source

Org-mode version = 9.7-pre