Machine learning framework for the analysis and prediction of energy loss for non-fullerene organic solar cells

Abstract

The efficiency of organic solar cells (OSCs) has been improved more than 19% recently with the development of non-fullerene acceptor materials. Further improvement is still attainable with the optimal combinations of donors and acceptors that provide minimal energy losses. In this work, a data-enabled machine-learning (ML) framework was employed to predict the energy losses in the polymer:non-fullerene acceptor based devices. Based on the collected experimental dataset, the prediction accuracies of various machine learning models were systematically compared by estimating mean absolute percentage errors (MAPE), root mean squared errors (RMSE), and person’s r coefficient. The Random Forest regression model showed the best performance in predicting the energy losses with a correlation coefficient of 0.83 and relative error in the range of 0 – 20%. The predictive ability of this model was further validated using the different parameters of devices with power conversion efficiency range of 6 – 18%. Three different donor–acceptor combinations were chosen for fabricating the photovoltaic devices to fit this model into practical devices and experimentally obtained energy loss values were compared with the predicted values. In addition, the device parameters with the molecular descriptors to understand the correlation and energy loss is highly correlated with the HOMO offset. This study demonstrates that the ML approach provide an effective method to predict and virtual screen of promising donor–acceptor pairs with minimal energy loss and would be useful for developing next-generation high performance solar cell materials.