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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.12857 |
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| _version_ | 1866915859265486848 |
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| author | Barletta, Giulio Ternes, Simon Ali, Saif Abbas, Zohair Ostendi, Chiara D'Addio, Marialucia Magliano, Erica Asinari, Pietro Chiavazzo, Eliodoro Di Carlo, Aldo |
| author_facet | Barletta, Giulio Ternes, Simon Ali, Saif Abbas, Zohair Ostendi, Chiara D'Addio, Marialucia Magliano, Erica Asinari, Pietro Chiavazzo, Eliodoro Di Carlo, Aldo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12857 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quantifying Perovskite Solar Cell Degradation via Machine Learning from Spatially Resolved Multimodal Luminescence Time Series Barletta, Giulio Ternes, Simon Ali, Saif Abbas, Zohair Ostendi, Chiara D'Addio, Marialucia Magliano, Erica Asinari, Pietro Chiavazzo, Eliodoro Di Carlo, Aldo Materials Science 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. |
| title | Quantifying Perovskite Solar Cell Degradation via Machine Learning from Spatially Resolved Multimodal Luminescence Time Series |
| topic | Materials Science |
| url | https://arxiv.org/abs/2603.12857 |