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

Abstract

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 MAPbI 3 degrades and discover universal behaviors where the carrier diffusion length (L D ) decays before quasi-Fermi-level splitting (ΔE F ), 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 L D 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.

How machine learning can help select capping layers to suppress perovskite degradation

“…Following sample fabrication, perovskite films are tested unencapsulated under rigorous accelerated aging conditions (85% RH, 85°C temperature, and 0.16 Sun illumination). We photograph the samples in situ every 3 min, calibrate color using calibration tiles with thin-plate spline color warping method 30 , and extract numerical values for degradation onset and rate from the time series images as a proxy for film stability 28,[31][32][33] .…”

“…Inspired by recent studies on inverse design of polymers and inorganic solids [23][24][25] , as well as on using machine learning to understand PSCs' properties [26][27][28] , we present a machine-learning framework to investigate LD organic-inorganic perovskites serving as a capping layer for MAPbI 3 . We elucidate which properties of capping layers are responsible for enhancing stability, and the underlying mechanisms whereby they work.…”

A data fusion approach to optimize compositional stability of halide perovskites

“…[26][27][28] We hence employ a colour-based metric as a proxy to capture the macroscopic evolution of the highbandgap, non-perovskite phases through the optical change of the near-black perovskite films. 29 0.15 Sun visible only illumination is applied to enable automatic image capture by an RGB camera (~200 µm resolution). We define Instability Index ( ) (Eq.…”

A Physical Data Fusion Approach to Optimize Compositional Stability of Halide Perovskites

“…As per Figure 1d, we hence employ a colour-based metric as a proxy to capture the macroscopic evolution of the high-bandgap, nonperovskite phases. [24][25][26] We define Instability Index ( ) as the integrated colour change of an unencapsulated perovskite film over accelerated degradation test duration T.…”