The development of efficient catalysts which utilise solar energy to convert CO2 to produce value-added chemicals or fuels is extremely appealing since it can simultaneously reduce the greenhouse gas emissions and provide energy storage solutions, with direct applications in both terrestrial and non-terrestrial environments. In recent years, novel solid catalysts such as metal-organic frameworks (MOFs) have demonstrated great potential in energy conversion systems. Compared with porous carbons and porous metals, MOFs possess unique characteristics such as crystallinity, structurally tuneable electrical and optical performance, catalytically active sites, and the coexistence of open pores and high surface areas which facilitate mass transport and molecule diffusion, crucial properties for photocatalytic water splitting as well as CO2 conversion (Wang et al. Adv.Sci. 2017, 4, 1600371; Diercks et al. Nat. Mater. 2018, 17, 301; Bo et al. Nanoscale, 2020, 12, 12196; Nam et al. Nat. Mater. 2020, 19, 266). The modular synthesis of MOFs opens up the possibility to generate thousands of structures – indeed, we have identified ca. 100,000 structures synthesised so far in the Cambridge Structural Database (CSD). This huge number of materials clearly creates exciting opportunities, but it also creates the following challenge: how does one identify the most promising MOFs catalysts for fuel production, among the thousands of possibilities, without requiring complex and time-consuming calculations and experiments? In this project, a combined computational and experimental multiscale approach will be developed to design heuristics to speed up the way novel MOF catalysts are discovered for advancing fuel production technologies via photocatalytic CO2 reduction.
