Computational optimisation of photoactive dye pairs for designing novel, highly efficient dye-sensitised solar cells.

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The overarching goal of this project is to further the power conversion efficiency of dye-sensitized solar cells (DSCs) - a renewable energy technology that can be effectively integrated into windows and wearables. One tactic to improve output current from a DSC device is by using multiple materials that absorb light in complementary regions of the solar spectrum. In particular, the aims of this project are to optimise key light-harvesting materials and combinations thereof when used synergistically in the same device. Using a large, custom-made database of such light-harvesting materials, optimum partner materials for well known, highly performing ones have been identified. Electronic structure techniques are being used to model the identified material pairs in their working environment, yielding information that will then be used to predict device parameters purely by computational means. This is the first time, to our knowledge, that this predictive method will be applied to multiple light-harvesting materials within the same device. By demonstrating that key parameters, such as power conversion efficiency, can be accurately predicted without relying on experimental data for input, automating the design of complementary materials is enabled via computational screening. The following work will then involve designing a workflow that can predict improved, hitherto unseen DSC materials, potentially using machine learning to tackle the optimisation. This effort is motivated by the increasingly urgent need for a diverse set of efficient renewable energy technologies. With an estimated 40% of energy consumption occurring in an urban environment, integrating efficient DSCs in windows of urban buildings would maximise harvested energy from light falling on surfaces that would otherwise go unused.