A dual-descriptor machine learning framework for evaluating perovskite passivation materials

Molecular passivation plays a crucial role in improving the efficiency and stability of perovskite optoelectronic devices. However, quantitative evaluation of passivation materials remains challenging because interfacial binding strength and lattice distortion must be considered simultaneously. Here, we develop a dual descriptor machine learning framework based on a density functional theory derived dataset to evaluate binding energy (BE) and lattice distortion value (LDV) from molecular structural descriptors. Among the evaluated algorithms, random forest achieves the best performance for both targets, with a root mean squared error of 0.524 and a correlation coefficient of 0.976 for BE prediction, and a root mean squared error of 0.048 and a correlation coefficient of 0.848 for LDV prediction. Feature analysis reveals that BE is primarily governed by descriptors related to ammonium group electronic effects and molecular polarity, whereas LDV is influenced by a broader set of electronic and steric features, reflecting the more complex origin of lattice distortion. Validation using representative modifiers further shows that the strongest binding does not necessarily correspond to the most desirable passivation behavior. This work establishes a physically informed dual descriptor strategy for evaluating perovskite passivation materials and suggests that promising modifiers should combine sufficient interfacial binding, moderate molecular polarity, and limited lattice perturbation.