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Invited Seminar: Developing Next Generation Energy Materials And Processes With AI And Machine Learning

Macadam Building, London

21Junbrain circuit


Join us for a seminar from Professor Jin Xuan This event will be held both in person and on Microsoft Teams.


12:00 - 13:00

Registration with networking lunch

13:00 - 14:00

Seminar: Developing next generation energy materials and processes with AI and machine learning

14:00 - 14:30

Questions session

About the Speaker

Professor Jin Xuan


Professor Jin Xuan joined the University of Surrey as the Associate Dean of Research and Innovation for the Faculty of Engineering and Physical Sciences in September 2022. He holds a Chair in Sustainable Processes and an EPSRC Open Fellowship at the School of Chemistry and Chemical Engineering. He is also a Turing Fellow at The Alan Turing Institute. Before moving to Surrey, he was the Head of the Department of Chemical Engineering at Loughborough University. He is the Editor of Energy and AI (Elsevier), and the founding Editor-in-Chief of the new journal Digital Chemical Engineering (IChemE). Professor Xuan is the director of the £4.5 million UKRI Interdisciplinary Centre for Circular Chemical Economy. Professor Xuan is the recipient of the Philip Leverhulme Prize in 2022, the Beilby Medal and Prize in 2020, Highly Commended Prizes for the IChemE Global Awards (Research Project, 2019) and IET Innovation Award in Energy and Power (2018) and Scottish Energy News Researcher of the Year Award in Energy and Materials in 2015.




Title: Developing Next Generation Energy Materials And Processes With AI And Machine Learning

The cross-disciplinary area of energy and artificial intelligence is undergoing rapid development recently. In this talk I will cover a number of innovative applications of AI that address the critical challenges in energy processes and materials, and the recent development of bespoke AI technologies and methodologies for advancing energy, decarbonisation and sustainable development, such as data-driven approaches and optimisation algorithms. I will showcase (1) how we can develop AI-based techno-economics analysis and optimisation for emerging carbon capture and utilisation processes, and (2) the development of performance-driven machine learning design of fuel cell electrodes.

At this event

Miao Guo

Senior Lecturer in Engineering

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