Quantifying Perovskite Solar Cell Degradation via Machine Learning from Spatially Resolved Multimodal Luminescence Time Series

Perovskite solar cells (PSCs) have experienced a remarkable rise in power conversion efficiency (PCE) over the past 15 years, positioning them as a promising alternative or complement to silicon for large-scale photovoltaic deployment. However, beyond scalable fabrication, operational stability remains a major bottleneck for commercialization. Reliable and rapid methods to assess device health and degradation mechanisms - ideally compatible with field applications - are therefore essential. We present a deep-learning framework to estimate efficiency retention, $R_\mathrm{PCE}=\mathrm{PCE}_t/\mathrm{PCE}_0$, directly from multimodal luminescence imaging acquired during device aging. Each training sample includes electroluminescence (EL), open-circuit photoluminescence (PLoc), and short-circuit photoluminescence (PLsc) images at an aged state, together with device-specific reference images at $t=0$. This design enables the model to learn spatially resolved degradation patterns relative to the pristine condition. The dataset was collected over 5-70 hours using an automated, in-house measurement platform. We introduce LumPerNet, a compact convolutional neural network that regresses $R_\mathrm{PCE}$ from stacked multimodal image tensors, and benchmark it against an intensity-only multilayer perceptron baseline. Using a leakage-aware protocol with device-level hold-out testing and four-fold cross-validation, restricted to $R_\mathrm{PCE}\in[0.8,1.2]$, LumPerNet achieves substantially improved and more robust performance (MAE -23.4%, RMSE -25.6%, $R^2$ +0.417). Ablation studies highlight the importance of complementary physical contrast across modalities for generalization. Overall, this work establishes a reproducible pipeline linking automated luminescence imaging to electrical labels, enabling accelerated stability testing and non-invasive degradation monitoring in PSCs.