# An Efficient Descriptor Model for Designing Materials for Solar Cells

**Authors:** Fahhad H Alharbi, Sergey N Rashkeev, Fedwa El-Mellouhi, Hans, P L\"uthi, Nouar Tabet, Sabre Kais

arXiv: 1706.01974 · 2017-06-08

## TL;DR

This paper introduces a new, efficient descriptor model for rapidly screening potential solar cell materials, incorporating absorption spectra and charge transport properties, outperforming previous models like Scharber's in accuracy.

## Contribution

The paper presents a novel descriptor model that includes absorption spectra and diffusion length, improving the screening accuracy for various solar cell materials beyond existing models.

## Key findings

- Model surpasses Scharber model in accuracy
- Incorporates absorption spectrum and diffusion length
- Applicable to multiple solar cell technologies

## Abstract

An efficient descriptor model for fast screening of potential materials for solar cell applications is presented. It works for both excitonic and non-excitonic solar cells materials, and in addition to the energy gap it includes the absorption spectrum ($\alpha(E)$) of the material. The charge transport properties of the explored materials are modeled using the characteristic diffusion length ($L_{d}$) determined for the respective family of compounds. The presented model surpasses the widely used Scharber model developed for bulk-heterojunction solar cells [Scharber \textit{et al., Advanced Materials}, 2006, Vol. 18, 789]. Using published experimental data, we show that the presented model is more accurate in predicting the achievable efficiencies. Although the focus of this work is on organic photovoltaics (OPV), for which the original Scharber model was developed, the model presented here is applicable also to other solar cell technologies. To model both excitonic and non-excitonic systems, two different sets of parameters are used to account for the different modes of operation. The analysis of the presented descriptor model clearly shows the benefit of including $\alpha(E)$ and $L_{d}$ in view of improved screening results.

## Full text

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## Figures

10 figures with captions in the complete paper: https://tomesphere.com/paper/1706.01974/full.md

## References

75 references — full list in the complete paper: https://tomesphere.com/paper/1706.01974/full.md

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Source: https://tomesphere.com/paper/1706.01974