Identifying the Key Parameters for Organic Solar Cells Using the Machine Learning Method

Abstract

Organic solar cells (OSCs) are lightweight, flexible, and highly transparent; however, their power conversion efficiency is currently subpar. This has motivated researchers to intensify their efforts to augment the performance of these devices. In nonfullerene-acceptor (NFA) OSCs, the impact of spin-triplet states has proved to be significant. In addition, it has been shown that the low exciton binding energies in NFAs, such as Y6, can lead to selfdissociation. These findings call for a deeper exploration. In this paper, we introduce an extended hot kinetic model that accounts for all spin states and the direct dissociation of local exciton states to evaluate their effects on OSC performance. The machine learning technique, specifically a regression neural network algorithm, is employed to handle the complex calculations related to the model. This algorithm generates the current density−voltage (J−V) curves for OSCs, from which three crucial factors are identified out of 28 examined factors, and their influence on the OSC performance parameters is thoroughly investigated. The analysis indicates that improving charge carrier mobility enhances the fill factor (FF), raising the energy of singlet charge transfer states increases the open-circuit voltage (V OC ), and thicker film generally boosts short-circuit current density (J SC ) but reduces the FF. Additionally, we explore how these three parameters affect device performance collectively. This research offers fresh insights into designing more efficient OSC devices.