Cofund 2 - Additional joint call: Towards prediction of operational lifetime of perovskite photovoltaics: Acceleration factors in stability study through machine learning - PrOperPhotoMiLe

Abstract

Halide perovskite solar cells (PSCs) have revolutionized the photovoltaic arena providing power conversion efficiencies currently above 25 %, low cost and ease of fabrication. Their low-weight, semi-transparency and flexibility make them ideal energy sources for applications in self-powered devices required for the future internet of things (IoT). Their combination in tandem architectures with Silicon solar cells will permit building terawatt-scale energy production required for low-carbon economy, shaping the energy future of our society. However, the limited lifetime of PSC is a drawback for the deployment and commercialization of this technology. Accelerated stability testing is needed to identify promising materials and device architectures, as well as to predict the expected PSC lifetime. Despite world-wide efforts in stability related investigations, large variability is noted between different devices, experimental procedures and parameters reported, making the required analysis extremely challenging. We have recently published a consensus on how to test PSCs for stability, and how to accurately report data, setting the base for international concerted efforts. However, correlating large amounts of data is still a challenge. Towards that end, herein we propose to join forces with the emerging field of big data analysis using automated machine learning. This interdisciplinary research is aimed at developing an automated scheme for analysing PSC stability data, generated using standardized accelerated testing. Such analysis will determine the accelerated test most relevant to normal operating conditions, as well as the acceleration factor (which relates the measured stability parameters with PSC operational lifetimes) and expected PSC lifetimes. The results will be used to train multiple Machine learning models to both shed light to the mechanisms as well as speed up the fabrication and characterization process. The knowledge gained will be implemented to strengthen the productivity and competitiveness of European PV industry, as well as within the PSC research and manufacturing sector.