A new fellowship programme will train artificial intelligence to design the chemical interfaces inside batteries, fuel cells, and electrolysers—the critical surfaces where energy conversion reactions actually happen. Electrochemical systems are central to the UK’s net-zero transition: they produce hydrogen, convert CO₂ into useful chemicals, and store renewable energy. But the electrode-electrolyte interfaces that govern their performance are extraordinarily complex. Designing better ones currently requires brute-force trial-and-error—running thousands of experiments or simulations with low success rates. This slow, iterative process has become a bottleneck for developing next-generation energy technologies. The IDEA Fellowship aims to replace that approach with an “inverse design” framework. Instead of testing random candidates, researchers will specify the desired performance—efficiency, durability, reaction rate—and let explainable AI models generate interface designs that meet those targets, in forms that humans can interpret. The methodology will first tackle existing challenges in hydrogen production and CO₂ reduction, then target systems that do not yet exist at industrial scale, such as nitrogen reduction for ammonia synthesis or multi-ion energy storage. If successful, the work could accelerate the development of cleaner industrial processes and more efficient energy storage, directly supporting the UK’s industrial decarbonisation and digitalisation goals.
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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.
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