Material selection method for a perovskite solar cell design based on the genetic algorithm

TL;DR

This paper introduces a genetic algorithm-based method for selecting optimal material combinations in perovskite solar cell design, balancing stability, efficiency, and cost.

Contribution

It presents a novel approach using genetic algorithms to optimize material selection for perovskite solar cells based on multiple indices.

Findings

Identified TiO2/CH3NH3PbI2.1Br0.9/Spiro-OMeTAD as the best balanced material combination.

Demonstrated the method's potential to improve material selection in solar cell design.

The approach can be extended to other perovskite-based solar cell configurations.

Abstract

In this work, we propose a method of selecting the most desirable combinations of material for a perovskite solar cell design utilizing the genetic algorithm. Solar cells based on the methylammonium lead halide, CH3NH3PbX3, attract researchers due to the benefits of their high absorption coefficient and sharp Urbach tail, long diffusion length and carrier lifetime, and high carrier mobility. However, their poor stability under exposure to moisture still poses a challenge. In our work, we assigned stability index, power conversion efficiency index, and cost-effectiveness index for each material based on the available experimental data in the literature, and our algorithm determined the TiO2/CH3NH3PbI2.1Br0.9/Spiro-OMeTAD as the most well balanced solution in terms of cost, efficiency, and stability. The proposed method can be extended further to aid the material selection in all-perovskite multijunction solar cell design as more data on perovskite materials become available in the future.