Sunlight hitting a metal catalyst can drive chemical reactions more cleanly and efficiently, but no one can simulate exactly how this happens without months of supercomputer time. Current computer models that predict how light interacts with molecules on metal surfaces are too slow to be useful for designing real catalysts. This project will replace those slow calculations with machine learning models—specifically, message-passing neural networks—that can predict the same results thousands of times faster. The team will combine these AI surrogates with simulations of how molecules move and how light behaves at the nanoscale, then test the method on three industrially important reactions: splitting water to produce hydrogen, and converting carbon monoxide and carbon dioxide into useful chemicals. If successful, this work will let researchers screen catalyst designs in hours instead of years, test proposed reaction mechanisms without expensive lab experiments, and guide experimentalists toward the most promising materials. The immediate impact is on chemical manufacturing and energy storage—processes that quietly underpin fertiliser production, fuel synthesis, and efforts to turn waste carbon into valuable products. The research is fundamentally computational and methodological; it does not build a prototype device or produce a marketable catalyst, but it could remove a major bottleneck in designing the next generation of light-driven chemical reactors.
View original technical description
Plasmonic photocatalysis offers a promising route to more sustainable and efficient chemical transformations. Metal catalysts can harness light via excitation of electrons which selectively transfer energy to molecules and promote chemical reactions. The result is an increase of reaction selectivity and a decrease of unwanted side products. This unconventional form of chemistry involves intricate coupling of light, electronic excitations, and molecular motion, the details of which are still under intense debate. The theoretical study of plasmonic photocatalysis to predict reaction probabilities as a function of catalyst composition, shape, and light exposure is limited by the computational cost of ab initio molecular dynamics simulations of realistic systems. This project seeks to develop and apply new molecular simulation methods that are both accurate and scalable enough to study light-driven chemical reactions on metal catalysts. The major leap this project will take is to develop deep machine learning (ML) surrogate models of electronic structure, based on message-passing neural networks that provide predictions at a fraction of the computational cost of ab initio calculations. Achieving this will decouple computational cost from prediction accuracy. These ML surrogate models will be combined with nonadiabatic molecular simulation methods and mesoscopic light-matter interaction models to enable the simulation of experimentally measurable reaction probabilities by averaging over thousands of reaction events at various reaction conditions. We will showcase the transformational capabilities of our methodology by simulating plasmonic light-enhancement of hydrogen evolution, and carbon monoxide and carbon dioxide reduction as a function of key design parameters. This project will go beyond the state of the art by transforming our ability to design plasmonic catalyst materials, to scrutinize mechanistic proposals, and to guide experiments for key catalytic reactions.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know