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Reference Number EP/W03722X/1
Title IDEA: Inverse Design of Electrochemical Interfaces with Explainable AI
Status Started
Energy Categories Other Power and Storage Technologies (Energy storage) 30%;
Other Cross-Cutting Technologies or Research (Other Supporting Data) 10%;
Fossil Fuels: Oil Gas and Coal (CO2 Capture and Storage) 30%;
Hydrogen and Fuel Cells (Hydrogen) 30%;
Research Types Basic and strategic applied research 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Chemistry) 25%;
PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 50%;
ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 25%;
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Dr J Xuan
No email address given
School of Engineering and Physical Sciences
Heriot-Watt University
Award Type Standard
Funding Source EPSRC
Start Date 01 October 2023
End Date 30 September 2028
Duration 60 months
Total Grant Value £2,177,756
Industrial Sectors Chemicals; Energy
Region Scotland
Programme Energy and Decarbonisation
 
Investigators Principal Investigator Dr J Xuan , School of Engineering and Physical Sciences, Heriot-Watt University (100.000%)
  Industrial Collaborator Project Contact , Intelligent Energy (0.000%)
Project Contact , Johnson Matthey plc (0.000%)
Project Contact , Fraunhofer-Gesellschaft, Germany (0.000%)
Project Contact , Princeton University (0.000%)
Web Site
Objectives
Abstract The IDEA Fellowship is a 5-year programme to pave the way for the UK's industrial decarbonisation and digitalisation, via emerging AI, digital transformations applied to fundamental electrochemical engineering research.Electrochemical engineering is at the heart of many key energy technologies for the 21st century such as H2 production, CO2 reduction, energy storage, etc. Further developments in all these areas require a better understanding of the electrode-electrolyte interfaces in the electrochemical systems because almost all critical phenomena occur at such interface, which eventually determine the kinetics, thermodynamics and long-term performance of the systems. Designing the next generation of electrochemical interfaces to fulfil future requirements is a common challenge for all types of electrochemical applications.Designing an electrochemical interface traditionally relies on high throughput screening experiments or simulations. Given the complex nature of the design space, it comes with no surprise that this brute-force approach is highly iterative with low success rates, which has become a common challenge faced by the electrochemical research community.The vision of the fellowship is to make a paradigm-shift in how future electrochemical interfaces can be designed, optimised and self-evolved throughout their entire life cycle via novel Explainable AI (XAI) and digital solutions. It will create an inverse design framework, where we use a set of desired performance indicators as input for the XAI models to generate electrochemical interface designs that satisfy the requirements, in a physically-meaningful way interpretable by us. The methodology, once developed, will tackle exemplar challenges of central importance to the net zero roadmap, which include improving current systems such as H2 production/fuel cell and CO2 reduction, but also developing new electrochemical systems which do not yet exist today at industrial scale such as N2 reduction and multi-ion energy storage.
Publications (none)
Final Report (none)
Added to Database 18/10/23