Famously described by Nobel Laureate Wolfgang Pauli as "the work of the devil", surfaces are challenging yet crucial systems to understand. They serve as catalysts within countless industrial processes vital towards chemical manufacturing as well as tackling the global climate crisis by enabling reactions that reduce greenhouse gases. Computational simulations have the potential to become essential tools in advancing our understanding of chemical processes which occur on surfaces, providing atomic-level insights that are challenging to obtain from experiments. However, it can be challenging to achieving sufficient accuracy to reliably reproduce experimental results from simulations - particularly for surfaces - limiting their usefulness.

In our paper, "Accurate and efficient framework for modelling the surface chemistry of ionic materials," we introduce a computational framework designed for modelling ionic surfaces accurately, to the level of coupled cluster theory, the 'gold-standard' quantum-mechanical simulation method. Importantly, this framework lowers the cost of applying coupled cluster theory sufficiently to enable routine application to a wide range of surfaces. This framework demonstrates unprecedented agreement with experimental observations for molecules adsorbed onto metal-oxide surfaces, systems used as solid catalysts and which underpin critical technologies such as electronics. With this framework, we have established a robust method for reliably screening and identifying new surface materials, thus fostering advancements across critical technological applications.