Accelerating the Design of Automotive Catalyst Products Using Machine Learning : Leveraging experimental data to guide new formulations. Issue 2 (23rd January 2022)

• Record Type: Journal Article
• Authors: Whitehead, Thomas M.; Chen, Flora; Daly, Christopher; Conduit, Gareth J.
• Is Part Of: Johnson Matthey technology review. Volume 66:Issue 2(2022:Apr.)
• Page Start: 130
• Page End: 136
• Publication Date: 2022-01-23
• Subjects: Technology -- Periodicals; Chemical engineering -- Periodicals; Metallurgy -- Periodicals; Materials -- Periodicals; Research, Industrial -- Periodicals; Periodicals; 605
• DOI: 10.1595/205651322X16270488736796
• Languages: English
• ISSNs: 2056-5135

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.