{"ID":2847133,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.01118","arxiv_id":"2511.01118","title":"Generative Machine Learning Models for the Deconvolution of Charge Carrier Dynamics in Organic Photovoltaic Cells","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.01118","url_pdf":"https://arxiv.org/pdf/2511.01118v1","authors":"[\"Li Raymond\",\"Salim Flora\",\"Wang Sijin\",\"Wright Brendan\"]","published":"2025-11-02T23:32:05Z","proceeding":"cond-mat.mtrl-sci","tasks":"[\"cond-mat.mtrl-sci\",\"cs.LG\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}