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Volume 66, Issue 2
  • ISSN: 2056-5135
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  • oa Accelerating the Design of Automotive Catalyst Products Using Machine Learning

    Leveraging experimental data to guide new formulations

  • Authors: Thomas M. Whitehead1, Flora Chen2, Christopher Daly2 and Gareth J. Conduit1,3
  • Affiliations: 1 Intellegens LtdEagle Labs, Chesterton Road, CambridgeUK 2 Johnson MattheyOrchard Road, Royston, Hertfordshire, SG8 5HEUK 3 Theory of Condensed Matter, Department of Physics, University of CambridgeJ. J. Thomson Avenue, Cambridge, CB3 0HEUK
  • Source: Johnson Matthey Technology Review, Volume 66, Issue 2, Apr 2022, p. 130 - 136
  • DOI: https://doi.org/10.1595/205651322X16270488736796
    • Received: 06 May 2021
    • Accepted: 22 Jul 2021
    • Published online: 23 Jul 2021

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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2021-07-23
2024-12-26
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/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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