Machine learning assisted identification of the matched energy level of materials for high open circuit voltage in binary organic solar cells

Abstract

With the application of new materials and the optimization of device structure, binary bulk heterojunction organic solar cells (OSCs) have exhibited the outstanding performance in recent years. However, the open-circuit voltage (V_oc) of binary OSCs is normally below 1 V and the matched energy levels of the donor, acceptor and transport materials with high V_oc in binary OSCs have been rarely proposed. Herein, four different machine learning (ML) algorithms are applied to investigate V_oc in binary OSCs according to the energy level of donor, acceptor and transport materials. Among them, the eXtreme Gradient Boosting (XGBoost) model provides the best prediction ability. Its prediction accuracy and root mean square error reach 0.94 and 0.04, respectively. Therefore, SHapley Additive exPlanations of XGBoost is selected and showed that the highest occupied molecular orbital (HOMO) of the donor plays the most important role for the improvement of V_oc in all the energy level of donor, acceptor and transport materials. More importantly the energy level matching strategy of binary OSC materials for high V_oc is delivered by machine learning, where the HOMO of the donor is about −5.45 ± 0.1 eV, the lowest unoccupied molecular orbital (LUMO) of the acceptor is about −3.80 ± 0.1 eV, and the work functions of the matched electron and hole transport materials are about −3.6 ± 0.2 eV and −5.1 ± 0.1 eV, respectively. In addition, the experimental verification results display that the measured V_oc just has a relatively low error compared with the predicted V_oc. Likewise, the predicted V_oc based on the XGBoost model of PTB7:PC₇₁BM is 0.79 V, and the experimental value is 0.76 V. The relative error is only 3.95%, which indicates the reliability of the ML prediction for high V_oc in binary OSCs.