Stability follows efficiency based on the analysis of a large perovskite solar cells ageing dataset

Stability issues remain a critical challenge for perovskite solar cells towards commercialisation. Here, the authors analyse a large homogeneous dataset of Maximum Power Point Tracking operational ageing data and find a correlation between maximum power conversion efficiency and its relative loss. While perovskite solar cells have reached competitive efficiency values during the last decade, stability issues remain a critical challenge to be addressed for pushing this technology towards commercialisation. In this study, we analyse a large homogeneous dataset of Maximum Power Point Tracking (MPPT) operational ageing data that we collected with a custom-built High-throughput Ageing System in the past 3 years. In total, 2,245 MPPT ageing curves are analysed which were obtained under controlled conditions (continuous illumination, controlled temperature and atmosphere) from devices comprising various lead-halide perovskite absorbers, charge selective layers, contact layers, and architectures. In a high-level statistical analysis, we find a correlation between the maximum reached power conversion efficiency (PCE) and the relative PCE loss observed after 150-hours of ageing, with more efficient cells statistically also showing higher stability. Additionally, using the unsupervised machine learning method self-organising map, we cluster this dataset based on the degradation curve shapes. We find a correlation between the frequency of particular shapes of degradation curves and the maximum reached PCE.

Automated summary

Stability issues are a critical challenge for perovskite solar cells towards commercialisation. The authors analyse a large dataset of operational ageing data and find a correlation between maximum power conversion efficiency and its relative loss. Higher efficiency cells also show higher stability, as observed through a statistical analysis and the use of machine learning.