Machine-Learning-Accelerated Rational Design of Aggregation-Induced Emission Luminogens.

The rational design of aggregation-induced emission (AIE) luminogens presents a significant challenge in molecular photophysics, requiring approaches that connect mechanistic understanding with practical molecular screening. This work uses the electronic-energy difference between the S1/S0 minimum-energy conical intersection (MECI) and the vertically accessed Franck-Condon S1 state, ΔE(MECI - FCS1), as an efficient descriptor of conical-intersection accessibility. We compile quantum-chemical labels for 228 structure-matched polycyclic aromatic molecules and develop a dual-model strategy that combines an interpretable fingerprint-based model with a Uni-Mol model for rapid property prediction. In an external panel of literature luminogens, the predicted values are significantly lower for AIE molecules than those for aggregation-caused-quenching (ACQ) molecules, supporting an empirical operating threshold near 0.6 eV. This threshold provides an efficient first-pass guide for prioritizing candidates with accessible CI channels, substantially narrowing the pool for subsequent validation. The resulting workflow connects mechanistic insight, interpretable design rules, and high-throughput screening, providing an efficient platform for accelerating the discovery of novel AIEgens.