A Machine Learning–Based Design Rule for Improved Open‐Circuit Voltage in Ternary Organic Solar Cells. (15th December 2019) - Lee, Min-Hsuan
Abstract : Organic solar cells (OSCs) based on ternary blends are among the most promising photovoltaic technologies. To further improve the power conversion efficiency (PCE), the materials selection criteria must be focused on achieving high open‐circuit voltage ( V oc ) through the alignment of the energy levels of the ternary blends. Hence, machine‐learning approaches are in high demand for extracting the complex correlation between V oc and the energy levels of the ternary blends, which are crucial to facilitate device design. Herein, the data‐driven strategies are used to generate a model based on the available experimental data, and the V oc is then predicted using available machine‐learning methods (the Random Forest regression and the Support Vector regression). In addition, the Random Forest regression is developed to find the appropriate energy‐level alignment of ternary OSCs and to reveal the relationship between V oc and electronic features. Finally, an analysis based on the ranking of variables in terms of importance by the Random Forest model is performed to identify the key feature governing the V oc and the performance of ternary OSCs. From the perspective of device design, the machine‐learning approach provides sufficient insights to improve the V oc and advances the comprehensive understanding of ternary OSCs. Abstract : The optimization of the open‐circuit voltage ( V oc ) with composition in ternary blends is correlated with the performance of ternary organic solar cells (OSCs). Herein, the machine‐learning approach is developed to model the correlations between different electronic features and target V oc . This machine‐learning approach may be sufficient to provide the material selection criteria for improving the V oc of ternary OSCs.