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Open Access Accelerating the Design of Automotive Catalyst Products Using Machine Learning : Leveraging experimental data to guide new formulations

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.

Document Type: Research Article

Affiliations: 1: Intellegens Ltd Eagle Labs, Chesterton Road, Cambridge UK 2: Johnson Matthey Orchard Road, Royston, Hertfordshire, SG8 5HE UK

Publication date: April 1, 2022

This article was made available online on July 22, 2021 as a Fast Track article with title: "Accelerating the Design of Automotive Catalyst Products Using Machine Learning".

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  • Johnson Matthey's international journal of research exploring science and technology in industrial applications. The Johnson Matthey Technology Review publishes reviews, articles, book reviews, conference reviews, short reports and abstracts focused on science and technology in a range of areas relevant to industry.

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