The Key Descriptors for Predicting the Exciton Binding Energy of Organic Photovoltaic Materials

Exciton binding energy (Eb) is a key parameter to determine the mechanism and performance of organic optoelectronic devices. Small Eb benefits to reduce the interfacial energy offset and the energy loss of organic solar cells. However, quantum‐chemical calculations of the Eb in solid state with considering electronic polarization effects are extremely time‐consuming. Furthermore, current studies lack critical descriptors. Here, we use data‐driven machine learning (ML) to accelerate the computation and identify the key descriptors most relevant to the solid‐state Eb. The results verify two key descriptors associated with molecular and aggregation‐state properties for efficient prediction of the solid‐state Eb. Moreover, a very high accuracy is achieved by using the extreme gradient boosting algorithm, with the Pearson’s correlation coefficient of 0.92. Finally, we use this ML model to predict the Eb of thin films, which is difficult to achieve using the current quantum‐chemical calculations due to the large structural disorder. Remarkably, the predicted thin‐film Eb values are fully consistent with the results of temperature‐dependent photoluminescence spectra. Therefore, our work provides an accurate and efficient approach to predict the solid‐state Eb and would be helpful to accelerate the exploitation of novel promising organic photovoltaic materials.