Multiscale modelling-guided design of catalytic interfaces for carbon dioxide conversion

Climate change due to massive emission of CO2 has been recognised as one of the greatest challenges of our time. To pursue carbon neutralisation, it has been long desired to develop highly efficient catalysts for CO2 hydrogenation.

Guided by theoretical modelling, there have been achievements to convert CO2 to value-added chemicals on different catalysts.

However, theory-guided catalyst design is restricted to simple systems due to the lack of accurate simulations of catalytic interfaces under operando conditions.

In this project, multiscale modelling will be performed to obtain comprehensive understandings of dynamic nature of both solid-liquid and solid-gas catalytic interfaces.

The effect of operando conditions will be evaluated by simulation techniques ranging from atomistic density functional theory to molecular dynamics and mesoscale kinetic Monte Carlo.

With obtained first-principles data, machine learning potentials will be trained, aiming at providing fast and accurate predictions of new catalysts.

I will move to the University of Cambridge to carry out the majority of the project, acquiring skills required to reach my goals.

Theoretical identified materials will be synthesised and tested at Linköping University, Stockholm University, and University of Cambridge.

In summary, determined to significantly contribute towards a carbon-emission free society, I will use and develop multiscale modelling techniques to predict new catalysts for CO2 hydrogenation.