# Sustainable design of organic solar cells utilized machine and deep learning

## TL;DR

This paper uses simulations and AI to optimize organic solar cells, improving efficiency and supporting sustainable energy goals.

## Contribution

A novel combined approach of detailed simulations and AI-based predictions is introduced to optimize organic solar cell design.

## Key findings

- PFN-Br as an ETL achieves a maximum PCE of 12.04% at 5 nm thickness.
- CNN outperforms SVR in predicting PCE with high accuracy.
- Optimizing layer thicknesses can lead to a simulated PCE of 19.50%.

## Abstract

In this work, an Organic Solar Cell (OSC) with a structure of ITO/PEDOT: PSS/PBDB-T: IT-M/PFN-Br/Al is extensively simulated and optimized. The impact of layer thicknesses and materials on device performance is simulated using a one-dimensional solar cell simulator (SCAPS-1D). The simulation model is first validated using experimental data, and it shows a high degree of alignment. Among the various Electron Transport Layers (ETLs) that are investigated, PFN-Br has the highest Power Conversion Efficiency (PCE) of 12.04%. The PFN-Br thickness is shown to be most effective at 5 nm. A simulated PCE of 19.50% results from the active layer reaching its optimum efficiency at 300 nm. PEDOT: PSS is the most effective Hole Transport Layer (HTL) with reliable performance at thicknesses ranging from 30 to 100 nm. Due to optical interference, the short-circuit current density (\documentclass[12pt]{minimal}
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