Higher living standards and a growing world population are the drivers behind continuous increases in greenhouse gas emission and industrial energy use. This provides growing pressure on chemical industries to develop more sustainable and efficient chemical transformations based on innovative new technologies. Light-driven plasmonic catalysis offers a promising route to more sustainable and energy efficient chemical transformations than conventional industrial-scale catalysis by replacing petrochemical reactants and energy sources with abundant feedstocks such as carbon dioxide from the atmosphere and renewable energy from sunlight. In addition, light energy can selectively be transferred via excited electrons in metal nanoparticles, so-called "hot" electrons, to molecules and enables more specific chemical reactions than conventional catalysis, potentially increasing yield and decreasing unwanted side products. Underlying this unconventional form of chemistry is the intricate coupling of light, hot electrons, and reactant molecules, the lack of understanding of which has inhibited systematic design and study of reaction parameters such as particle size, shape, and optimal light exposure. A predictive theory of hot-electron chemistry will support the adaptation of this technology in the chemical industry, which holds the potential to significantly reduce the industry's carbon footprint. The aim of this project is to develop and exploit a computational simulation framework to understand, predict, and design light-driven chemical reactions on light-sensitive metallic nanoparticles and surfaces, so-called plasmonic nanocatalysts. The vision behind this fellowship is to provide quantum theoretical methods that fill a conceptual and methodological gap by providing accurate and feasible computational prediction of experimentally measurable chemical reaction rates as a function of catalyst design parameters relevant to the real-world application of this technology. In synergy with experimental project partners, the fellow will lead a research team of 2 postdoctoral researchers to develop highly efficient computational chemistry methodology, which will be applied to scrutinize mechanistic proposals, support and guide experimental efforts on light-driven plasmonic carbon dioxide reduction chemistry, and to construct reaction rate models relevant to improve the industrial viability of this technology. The aim is to provide a step-change in the mechanistic understanding of light-driven plasmonic reduction catalysis on the example of carbon monoxide and carbon dioxide transformation to enable rational design of catalyst materials with wide implications for continuous photochemistry and electrochemistry applications in industry. These applications will be explored by continuous engagement efforts of the fellow with leading chemical and petrochemical companies. With this project, the fellow will establish an international track record by fostering existing and establishing new collaborations with the goal to become a recognized researcher in this comparably young field.