The goal of achieving zero-carbon emissions by 2050 has driven the search for alternative industry solutions that can replace the traditional fossil fuel-based economy. With the technology and infrastructure in place to produce clean electricity from renewable sources such as solar or wind, the ability to generate it on a large scale is rapidly increasing. This presents a prime opportunity for the production of carbon-free fuels and chemicals through the use of electrocatalysis. Electrocatalysis enables the conversion of green electricity into chemicals and fuels, and vice versa, providing a path towards a sustainable future.
A grant challenge in electrocatalysis remains the design of efficient, effective, and selective catalyst materials and reaction environment. Traditionally, molecular modelling has helped address this by offering insights into reaction mechanisms and guiding material and electrolyte design. Increasingly, artificial intelligence is playing a transformative role in this space. AI models trained on quantum mechanical data or experimental results can rapidly screen material candidates, identify structure-performance relationships, and uncover hidden patterns that may not be obvious through conventional approaches.
My talk will highlight how we have used molecular modelling in the past for clean energy conversion reactions based on electrocatalysis, the emerging shift toward machine learning, and how it is expected to drive the next wave of breakthroughs in predicting and optimising electrocatalysts for clean energy applications.