Johnson Matthey

Canvas Category OEM : Primary Metal

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Primary Location London, England, United Kingdom

Financial Status LON: JMAT

As a global leader in sustainable technologies, we apply our cutting-edge science to create solutions with our customers that make a real difference to the world around us. Weโ€™ve been leaders in our field for more than 200 years, applying unrivalled scientific expertise to enable cleaner air, improved health and the more efficient use of our planetโ€™s natural resources. And now, as the world faces the challenges of climate change and resource scarcity, we have an even bigger role to play. Johnson Matthey will be central in accelerating the big transitions needed in transport, energy, chemicals production and creating a circular economy.

Assembly Line

Echion, Johnson Matthey, Britishvolt and UCL to produce demonstrator cells in CASCADE, a Faraday Battery Challenge-funded project

๐Ÿ“… Date:

๐Ÿ”– Topics: Funding Event

๐Ÿข Organizations: Echion, Johnson Matthey, Britishvolt, University College London


Echion Technologies, Cambridge, UK (โ€œEchionโ€), Johnson Matthey (โ€œJMโ€), Britishvolt (โ€œBVโ€) and University College London (โ€œUCLโ€) are grant recipients in the latest round (Round 4) of the Faraday Battery Challenge.

Project CASCADE (Cathode and Anode Supply Chain for Advanced DEmonstrator) brings together these four organisations to develop a next-generation, ultra-high power and fast-charging battery materials system for automotive applications using the cathode and anode technology of JM and Echion, respectively. This follows the successful CORNEA Innovate UK project between Echion and JM, which established the commercial potential and roadmap for this technology system.

Read more at Echion News

Accelerating the Design of Automotive Catalyst Products Using Machine Learning

๐Ÿ“… Date:

โœ๏ธ Authors: Tom Whitehead, Flora Chen, Christopher Daly, Gareth Conduit

๐Ÿ”– Topics: generative design, machine learning

๐Ÿญ Vertical: Automotive

๐Ÿข Organizations: Intellegens, Johnson Matthey


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.

Read more at Ingenta Connect