用机器学习解耦有机光伏载流子动态，实现可解释的预测分析
Generative Machine Learning Models for the Deconvolution of Charge Carrier Dynamics in Organic Photovoltaic Cells
- 基于隐变量微分方程建模，从时间分辨电荷提取数据中分离出载流子行为
- 发现载流子衰减符合压缩指数规律，且模型可外推不同实验条件下的结果
- 适合光伏器件研究者用于优化设计，兼具可解释性与预测能力
载流子动力学对有机光伏器件的效率与稳定性至关重要，但传统分析方法难以建模。本文提出 {eta}-线性解码的隐式常微分方程（{eta}-LLODE），从 P3HT:PCBM 电池的时间分辨电荷提取测量中解耦并重构载流子提取动态。该模型能独立分析载流子行为，其特征被发现符合压缩指数衰减规律。此外，学习到的可解释隐空间支持对实验条件的插值与外推模拟，为太阳能电池研究提供预测工具，助力器件分析与优化。
原文摘要 · Abstract (English)
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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