Particle Swarm Optimisation for Analysing Time-Dependent Photoluminescence Data

Abstract

Next-generation photovoltaic materials such as per-ovskites and organic photovoltaics are promising candidates for cheap, solution-processable solar cells, which have the environmental and financial advantages compared to traditional silicon-based cells. To realise commercial solar cells, the development of new materials to improve the performance is needed. Time-resolved optical spectroscopy is a powerful technique for photovoltaic materials that allows measurement of reveal the energy-dependent dynamics of photoexcited species (charges and excitons), which are responsible for the device performance. Although time-resolved spectroscopy provides contains rich information, the data analysis can be time-consuming and labour-intensive. Automated data-processing is therefore an attractive proposition to facilitate higher throughput. This paper describes a new application of evolutionary computation technique - particle swarm optimisation (PSO) - to parametrise time-resolved photoluminescence (PL) data. PSO is used to convert time- and energy-resolved photoluminescence data into decay rate distributions. From this, the excited state lifetimes can be elucidated – a key parameter for the optimisation of photovoltaic performance. The implementation of PSO in enhanced LumiML proved advantageous, yielding considerable improvements over previous techniques LumiML by two orders of magnitude.