Maximizing energy yield (EY) - the total electric energy generated by a solar cell within a year at a specific location - is crucial in photovoltaics (PV), especially for emerging technologies. Computational methods provide the necessary insights and guidance for future research. However, existing simulations typically focus on only isolated aspects of solar cells. This lack of consistency highlights the need for a framework unifying all computational levels, from material to cell properties, for accurate prediction and optimization of EY prediction. To address this challenge, a differentiable digital twin, Sol(Di)$^2$T, is introduced to enable comprehensive end-to-end optimization of solar cells. The workflow starts with material properties and morphological processing parameters, followed by optical and electrical simulations. Finally, climatic conditions and geographic location are incorporated to predict the EY. Each step is either intrinsically differentiable or replaced with a machine-learned surrogate model, enabling not only accurate EY prediction but also gradient-based optimization with respect to input parameters. Consequently, Sol(Di)$^2$T extends EY predictions to previously unexplored conditions. Demonstrated for an organic solar cell, the proposed framework marks a significant step towards tailoring solar cells for specific applications while ensuring maximal performance.

翻译：最大化能量产额（EY）——即太阳能电池在特定地点一年内产生的总电能——对于光伏（PV）技术至关重要，尤其对于新兴技术而言。计算方法为未来研究提供了必要的洞见和指导。然而，现有模拟通常仅关注太阳能电池的孤立方面。这种一致性的缺乏凸显了需要一个统一从材料到电池特性的所有计算层级的框架，以实现EY预测的准确预测与优化。为应对这一挑战，本文引入了一种可微分的数字孪生模型Sol(Di)$^2$T，以实现太阳能电池的全面端到端优化。该工作流程始于材料特性和形态处理参数，随后进行光学和电学模拟。最后，结合气候条件和地理位置来预测EY。每个步骤本质上是可微分的，或被机器学习替代模型所取代，这不仅实现了准确的EY预测，还支持基于梯度的输入参数优化。因此，Sol(Di)$^2$T将EY预测扩展到了先前未探索的条件。以有机太阳能电池为例进行演示，所提出的框架标志着在针对特定应用定制太阳能电池并确保最大性能方面迈出了重要一步。