Generative Machine Learning Models for the Deconvolution of Charge Carrier Dynamics in Organic Photovoltaic Cells

• URL: http://arxiv.org/abs/2511.01118v1
• Date: Sun, 02 Nov 2025 23:32:05 GMT
• Title: Generative Machine Learning Models for the Deconvolution of Charge Carrier Dynamics in Organic Photovoltaic Cells
• Abstract summary: beta-LLODE is a machine learning framework that disentangles and reconstructs extraction dynamics from P3HT:PCBM cells.<n>The learnt interpretable latent space enables simulation, including both extrapolation and extrapolation of experimental measurement conditions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Charge carrier dynamics critically affect the efficiency and stability of organic photovoltaic devices, but they are challenging to model with traditional analytical methods. We introduce \b{eta}-Linearly Decoded Latent Ordinary Differential Equations (\b{eta}-LLODE), a machine learning framework that disentangles and reconstructs extraction dynamics from time-resolved charge extraction measurements of P3HT:PCBM cells. This model enables the isolated analysis of the underlying charge carrier behaviour, which was found to be well described by a compressed exponential decay. Furthermore, the learnt interpretable latent space enables simulation, including both interpolation and extrapolation of experimental measurement conditions, offering a predictive tool for solar cell research to support device study and optimisation.

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