Simulating excited states in metal organic frameworks: from light-absorption to photochemical CO2 reduction

Abstract

Metal–organic frameworks (MOFs) have a wide range of optoelectronic and photochemical applications, many of which are directly dependent on their excited states. Computational modelling of excited state processes could aid the rational design of effective catalysts, but simulating MOFs in their excited state is challenging. This is due to the inherent molecule/crystal duality of MOFs, their large and diverse unit cells, and the unfavourable scalability of quantum chemical methods. However, periodic and cluster models have been developed and applied to characterise the excited states of MOFs and their properties, such as charge transfer, luminescence, and photocatalytic mechanisms. Additionally, embedding techniques provide a means of explicitly incorporating the crystal environment in such models. Although many high-quality reviews have assessed computational modelling in MOFs, most have focused on the study of ground-state electronic properties. In this perspective, we focus on the computational methods available to describe the excited states of MOFs from the molecular, periodic, and embedding perspectives. To illustrate the performance of cluster and periodic models, we compare the results obtained using both approaches at different levels of theory for an exemplary MOF. We also analyse examples from modelling relevant photochemical and photophysical including charge transfer, exciton effects, chemosensing, host–guest mechanisms, thermally activated delayed fluorescence and room temperature phosphorescence. Additionally, we show how such methods can be applied to predict MOF-based photocatalytic CO₂ reduction to value-added chemicals. We emphasise the advantages and limitations of current methodologies, as well as the potential for utilising databases and machine learning models in this context.