Towards a fully differentiable digital twin for solar cells

TL;DR

This paper introduces Sol(Di)$^2$T, a fully differentiable digital twin for solar cells that unifies multiple simulation levels to enable accurate energy yield prediction and gradient-based optimization, advancing solar cell design.

Contribution

The paper presents a novel differentiable digital twin framework that integrates material, optical, electrical, and climatic simulations for comprehensive solar cell optimization.

Findings

Accurately predicts energy yield for organic solar cells.

Enables gradient-based optimization of solar cell parameters.

Extends predictions to new conditions for tailored solar cell design.

Abstract

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.