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Volume 66, Issue 2
  • ISSN: 2056-5135
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Abstract

The design of catalyst products to reduce harmful emissions is currently an intensive process of expert-driven discovery, taking several years to develop a product. Machine learning can accelerate this timescale, leveraging historic experimental data from related products to guide which new formulations and experiments will enable a project to most directly reach its targets. We used machine learning to accurately model 16 key performance targets for catalyst products, enabling detailed understanding of the factors governing catalyst performance and realistic suggestions of future experiments to rapidly develop more effective products. The proposed formulations are currently undergoing experimental validation.

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/content/journals/10.1595/205651322X16270488736796
2021-07-23
2024-11-22
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References

  1. K. Zhang, S. Batterman, Sci. Total Environ., 2013, 450–451, 307 LINK https://doi.org/10.1016/j.scitotenv.2013.01.074 [Google Scholar]
  2. D. Brugge, J. L. Durant, C. Rioux, Environ. Health, 2007, 6, 23 LINK https://doi.org/10.1186/1476-069X-6-23 [Google Scholar]
  3. C. Morgan, Johnson Matthey Technol. Rev., 2014, 58, (4), 217 LINK https://www.technology.matthey.com/article/58/4/217-220/ [Google Scholar]
  4. K. Shakouri, J. Behler, J. Meyer, G.-J. Kroes, J. Phys. Chem. Lett., 2017, 8, (10), 2131 LINK https://doi.org/10.1021/acs.jpclett.7b00784 [Google Scholar]
  5. Z. W. Ulissi, A. J. Medford, T. Bligaard, J. K. Nørskov, Nat. Commun., 2017, 8, 14621 LINK https://doi.org/10.1038/ncomms14621 [Google Scholar]
  6. J. R. Kitchin, Nat. Catal., 2018, 1, (4), 230 LINK https://doi.org/10.1038/s41929-018-0056-y [Google Scholar]
  7. W. Yang, T. T. Fidelis, W.-H. Sun, ACS Omega, 2019, 5, (1), 83 LINK https://doi.org/10.1021/acsomega.9b03673 [Google Scholar]
  8. B. R. Goldsmith, J. Esterhuizen, J.-X. Liu, C. J. Bartel, C. Sutton, AIChE J., 2018, 64, (7), 2311 LINK https://doi.org/10.1002/aic.16198 [Google Scholar]
  9. Z. Li, S. Wang, W. S. Chin, L. E. Achenie, H. Xin, J. Mater. Chem. A, 2017, 5, (46), 24131 LINK https://doi.org/10.1039/c7ta01812f [Google Scholar]
  10. Z. W. Ulissi, M. T. Tang, J. Xiao, X. Liu, D. A. Torelli, M. Karamad, K. Cummins, C. Hahn, N. S. Lewis, T. F. Jaramillo, K. Chan, J. K. Nørskov, ACS Catal., 2017, 7, (10), 6600 LINK https://doi.org/10.1021/acscatal.7b01648 [Google Scholar]
  11. T. Williams, K. McCullough, J. A. Lauterbach, Chem. Mater., 2020, 32, (1), 157 LINK https://doi.org/10.1021/acs.chemmater.9b03043 [Google Scholar]
  12. Z. Li, X. Ma, H. Xin, Catal. Today, 2017, 280, (2), 232 LINK https://doi.org/10.1016/j.cattod.2016.04.013. [Google Scholar]
  13. I. Takigawa, K.-i. Shimizu, K. Tsuda, S. Takakusagi, RSC Adv., 2016, 6, (58), 52587 LINK https://doi.org/10.1039/c6ra04345c [Google Scholar]
  14. K. Suzuki, T. Toyao, Z. Maeno, S. Takakusagi, K.-i. Shimizu, I. Takigawa, ChemCatChem, 2019, 11, (18), 4537 LINK https://doi.org/10.1002/cctc.201900971 [Google Scholar]
  15. B. D. Conduit, N. G. Jones, H. J. Stone, G. J. Conduit, Scr. Mater., 2018, 146, 82 LINK https://doi.org/10.1016/j.scriptamat.2017.11.008 [Google Scholar]
  16. P. Santak, G. Conduit, Fluid Phase Equilib., 2019, 501, 112259 LINK https://doi.org/10.1016/j.fluid.2019.112259 [Google Scholar]
  17. T. M. Whitehead, B. W. J. Irwin, P. Hunt, M. D. Segall, G. J. Conduit, J. Chem. Inf. Model., 2019, 59, (3), 1197 LINK https://doi.org/10.1021/acs.jcim.8b00768 [Google Scholar]
  18. J. Bergstra, R. Bardenet, Y. Bengio, B. Kégl, ‘Algorithms for Hyper-Parameter Optimization’, NIPS’11: Proceedings of the 24th International Conference on Neural Information Processing Systems, 12th–15th December, 2011, Granada, Spain, Curran Associates Inc, New York, USA, 2011, 9 pp LINK https://dl.acm.org/doi/10.5555/2986459.2986743 [Google Scholar]
  19. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, É. Duchesnay, J. Mach. Learn. Res., 2011, 12, 2825 LINK https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf [Google Scholar]
  20. B. Frénay, G. Doquire, M. Verleysen, Neural Networks, 2013, 48, 1 LINK https://doi.org/10.1016/j.neunet.2013.07.003. [Google Scholar]
  21. B. W. J. Irwin, J. R. Levell, T. M. Whitehead, M. D. Segall, G. J. Conduit, J. Chem. Inf. Model., 2020, 60, (6), 2848 LINK https://doi.org/10.1021/acs.jcim.0c00443 [Google Scholar]
  22. L. McInnes, J. Healy, J. Melville, ‘UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction’, arXiv:1802.03426v3 [stat.ML], 18th September, 2020, preprint LINK https://arxiv.org/abs/1802.03426v3 [Google Scholar]
  23. B. D. Conduit, N. G. Jones, H. J. Stone, G. J. Conduit, Mater. Des., 2017, 131, 358 LINK https://doi.org/10.1016/j.matdes.2017.06.007. [Google Scholar]
  24. M.-F. Ng, J. Zhao, Q. Yan, G. J. Conduit, Z. W. Seh, Nat. Mach. Intell., 2020, 2, (3), 161 LINK https://doi.org/10.1038/s42256-020-0156-7 [Google Scholar]
/content/journals/10.1595/205651322X16270488736796
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Accelerating the Design of Automotive Catalyst Products Using Machine Learning
Johnson Matthey Technology Review 66, 130 (2022); https://doi.org/10.1595/205651322X16270488736796
/content/journals/10.1595/205651322X16270488736796
/content/journals/10.1595/205651322X16270488736796
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  • Article Type: Research Article

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