Materials Data-science Approach for Comprehensive Understanding of Organic Thin Film Solar Cells

Abstract

In recent years, competition in organic photovoltaic cells (OPVs) performance improvement and organic semiconductor development has intensified. In response, there has been an upsurge in the development of predictive models for OPV performance utilizing machine learning. Until now, chemistry researchers have used various approaches when creating OPV cells as well as developing new materials to improve power conversion efficiency (PCE). However, not many of those original approaches have been used for performance prediction due to the small sample size. In this study, we conducted Data-science approach where we collected information from 115 scientific literatures and constructed a dataset with the addition of some new proposed variables to describe the structure and material composition of the active layer. This allows us to use 25 variables to describe OPVs in which the active layer forms a 1~3-level structure (1-layer, two- tiered and three-tiered). Proposed work also includes post-processing and measurement data that have not been addressed in existing studies. Several regression models were constructed with coefficients of determination exceeding 0.9 by supervised learning methods (random forest (RF), monmlp, etc.) using this data.