Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.12857v1</id>\n    <title>Quantifying Perovskite Solar Cell Degradation via Machine Learning from Spatially Resolved Multimodal Luminescence Time Series</title>\n    <updated>2026-03-13T10:03:02Z</updated>\n    <link href='https://arxiv.org/abs/2603.12857v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.12857v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>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.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cond-mat.mtrl-sci'/>\n    <published>2026-03-13T10:03:02Z</published>\n    <arxiv:comment>32 pages, 14 figures</arxiv:comment>\n    <arxiv:primary_category term='cond-mat.mtrl-sci'/>\n    <author>\n      <name>Giulio Barletta</name>\n    </author>\n    <author>\n      <name>Simon Ternes</name>\n    </author>\n    <author>\n      <name>Saif Ali</name>\n    </author>\n    <author>\n      <name>Zohair Abbas</name>\n    </author>\n    <author>\n      <name>Chiara Ostendi</name>\n    </author>\n    <author>\n      <name>Marialucia D'Addio</name>\n    </author>\n    <author>\n      <name>Erica Magliano</name>\n    </author>\n    <author>\n      <name>Pietro Asinari</name>\n    </author>\n    <author>\n      <name>Eliodoro Chiavazzo</name>\n    </author>\n    <author>\n      <name>Aldo Di Carlo</name>\n    </author>\n  </entry>"
}