Data-driven modelling for electrolyte optimisation in dye-sensitised solar cells and photochromic solar cells.

Because they can be made semi-transparent, dye-sensitised solar cells (DSSCs) have great potential for glazing applications. Their photovoltaic performance and light transmission depend not only on the dye used, but also on the electrolyte they contain. A few years ago, we introduced the concept of solar cells with dynamic optical properties based on the use of photochromic photosensitizers. These cells allow variable light transmission according to sunlight conditions, while producing electrical energy. We found that the electrolytes commonly used in DSSCs are not optimal for this class of photosensitisers and need to be tuned. In this work, we have developed and characterised two new photochromic dyes for use in solar cells and we present a study aimed at developing electrolytes specifically adapted to these dyes. Using a methodology based on the design of experiments (DoE) combined with a machine learning (ML) approach, we show that it is possible to quickly find an optimal formulation for iodine-based electrolytes to achieve good transparency of photochromic devices with an AVT ranging from 57% to 23% across the photochromic process, while keeping the photovoltaic conversion efficiency above 2.9%. We show that this approach can be applied to other classes of electrolytes with different redox systems, such as TEMPO/TEMPO