摘要
This paper provides an attempt to utilize machine learning algorithm,explicitly random-forest algorithm,to optimize the performance of dye sensitized solar cells(DSSCs)in terms of conversion efficiency.The optimization is implemented with respect to both the mesoporous TiO_(2) active layer thickness and porosity.Herein,the porosity impact is reflected to the model as a variation in the effective refractive index and dye absorption.Database set has been established using our data in the literature as well as numerical data extracted from our numerical model.The random-forest model is used for model regression,prediction,and optimization,reaching 99.87%accuracy.Perfect agreement with experimental data was observed,with 4.17%conversion efficiency.
