Publication date: 21st July 2025
Understanding the physics and origins of degradation mechanisms in solar cells is a challenging task. As a result, research often concentrates on a few model systems, relying on extensive and often costly characterization to track how material properties evolve over time.
This approach is particularly limiting for technologies like organic and perovskite solar cells, which can use a wide variety of materials. To unlock their full potential, accelerated characterization methods are needed. Without these, building quantitative structure-property relationships in the hope of designing bespoke materials for targeted applications (such as indoor, semi-transparent, and agrivoltaics) will fall short.
In this talk, I will present how modeling and machine learning (ML) can be coupled with high-throughput data from accelerated aging experiments to address this challenge. I will introduce optimPV, a fully open-source framework that integrates multiple physical modeling tools (e.g., PDE solvers, optical simulations, drift-diffusion modeling) with ML-based optimization methods (including Bayesian optimization, Bayesian inference, and genetic algorithms).
I will show how optimPV enables the identification of the dominant degradation process and the extraction of key material parameters, such as charge carrier mobilities and recombination rates. The effectiveness of this framework will be illustrated through a large-scale degradation study on organic solar cells, featuring 25 donor-acceptor blends processed under varied conditions. This analysis revealed key degradation trends and identified problematic material combinations to avoid in future formulations.
Lastly, I will discuss how the framework can be extended to other systems and case studies, incorporating a range of material types (organic and perovskite), physical models (PDE-based and drift-diffusion), and experimental techniques, including: (i) transient absorption spectroscopy, (ii) transient photoluminescence and microwave conductivity, and (iii) light-intensity-dependent current–voltage measurements.