Interpretable Machine Learning Model for the Highly Accurate Prediction of Efficiency of Ternary Organic Solar Cells Based on Nonfullerene Acceptor using Effective Molecular Descriptors

Abstract

The challenge of accurately predicting the power conversion efficiency (PCE) of ternary organic solar cells (OSCs) based on a nonfullerene acceptor holds the key to the rational design of a ternary blend. Developing an effective descriptor with experimentally measurable and theoretically computable signatures for accurately predicting the PCE of OSCs based on nonfullerene acceptors is an important step toward achieving this goal. Herein, the electronegativity is first proposed as an effective molecular descriptor for predicting the PCE of OSCs based on nonfullerene acceptors and further analyzing the underlying relationships between material property and device performance. Remarkably, the high accuracy (Coefficient of Determination) > 0.9) can be achieved by constructing the machine learning model with a fewer number of descriptors. In addition, the SHapley Additive exPlanations approach is introduced to provide both local and global interpretations for extracting a deep understanding of complex molecular descriptor–PCE relationships. These results in this study validate the effectiveness of the molecular descriptor, providing an efficient modality for rapid and precise screening of high‐performance ternary materials.