Machine Learning Prediction of Aggregation‐Induced Emission Energies for Organic Luminogens and Metal Complexes Using Physicochemical Descriptors
Aggregation‐induced emission (AIE) has revolutionized the design of photoluminescent materials by enabling strong solid‐state emission from molecularly nonemissive compounds. However, rational prediction of AIE properties remains challenging because photophysical behavior depends not only on molecular structure but also on aggregate‐state packing and measurement conditions. This study develops a quantitative and interpretable machine learning (ML) framework for predicting experimentally reported emission energies of AIE‐active molecules using continuous physicochemical descriptors derived from molecular structures. A dataset of 590 AIE luminogens—including conjugated organics, donor–acceptor (D–A) systems, silicon‐containing luminogens, and transition‐metal complexes—was analyzed using Gaussian process regression (GPR) combined with SHapley Additive exPlanations (SHAP). The optimized descriptor‐based model achieved moderate predictive performance (test R 2 = 0.58) and provided chemically interpretable structure–property trends. Feature attribution indicated that nitrogen‐ and sulfur‐containing motifs, electrotopological‐state descriptors, Burden–CAS–University of Texas (BCUT) descriptors, and stereodefined vinylene units are statistically associated with lower emission energies within the present dataset. Morgan fingerprint baseline models showed higher random‐split accuracy, whereas leave‐one‐cluster‐out validation revealed cluster‐dependent degradation for structurally separated regions. This work therefore provides an interpretable initial screening strategy for AIE luminogens while clarifying the need for future models incorporating measurement conditions, solid‐state structural descriptors, and electronic‐structure‐informed features.