The growing global energy consumption, along with the negative effects of fossil fuels, demands for new and improved renewable energy technologies. Promising candidates in this regard are solar cells based on organic materials. However, to make them commercially viable their power conversion efficiencies need to be increased. In this research proposal, the origin of the relatively low power conversion efficiencies is investigated. By using a combined approach based on theory and machine-learning, I propose a methodology for predicting and considerably improving the power conversion efficiency in organic solar cells

2023

2027