Data-Driven Analysis of Hole-Transporting Materials for Perovskite Solar Cells Performance.

ABSTRACT: We have created a dataset of 269 perovskite solar cells, containing information about their perovskite family, cell architecture, and multiple hole-transporting materials features, including fingerprints, additives, and structural and electronic features. We propose a predictive machine learning model that is trained on these data and can be used to screen possible candidate hole-transporting materials. Our approach allows us to predict the performance of perovskite solar cells with reasonable accuracy and is able to successfully identify most of the top-performing and lowest-performing hole-transporting materials in the dataset. We discuss the effect of data biases on the distribution of perovskite families/architectures on the model's accuracy and offer an analysis with a subset of the d

SUBMITTER: Del Cueto M 

PROVIDER: S-EPMC9376947 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature