Forecasting the Decay of Hybrid Perovskite Performance Using Optical Transmittance or Reflected Dark-Field Imaging

The practicality and economic viability of hybrid perovskite solar cells hinge on their operational lifetime, and methods for forecasting the performance of perovskites under different operational stresses are urgently needed. Here, we explore the evolution of material-level optoelectronic properties as MAPbI3 degrades and discover universal behaviors where the carrier diffusion length (LD) decays before quasi-Fermi-level splitting (ΔEF), regardless of the specific stress protocol (oxygen, humidity, thermal stress, or a combination). We employ a machine learning greedy feature selection model that uses initially measured properties to predict the time it takes LD to decrease to 85% of its initial value with a prediction accuracy of 12.8%. This model reveals a strong correlation between the initial rate of transmittance change and the time until loss of transport. We translate this material-level finding to photovoltaic device-level forecasting by demonstrating that the rate of change of transmittance is equivalent to the rate of change of the spatial standard deviation of dark-field image intensity (i.e., scattered light intensity) collected in reflection mode (and thus applicable to devices with opaque contacts). This work demonstrates that transmittance and scattering methods are highly effective for accelerated material (and device) stability evaluation and forecasting.