Decoding donor/acceptor hierarchy in DAD triads via fragment-centric machine learning

The rational design of donor-acceptor-donor (DAD) triads is often hindered by the complex, nonlinear interactions between molecular fragments. This study develops an interpretable machine learning (ML) framework utilizing Fragment Orbital Descriptors (FODs) to predict key energetic criteria of hot-exciton materials and establish quantitative structure-property relationships. Through a synergy of micro-interpretability and macro-statistical analysis, we uncover a definitive fragment-selectivity hierarchy: acceptor molecular orbitals act as the primary regulators, contributing over 70% to energy gap determination, while donor fragments serve as secondary modulators for fine-tuning. We identify specific chemical manifestations of this hierarchy: DPP-based acceptors preferentially maximize the triplet-triplet energy gap (ΔE_TT) through enhanced high-lying triplet separation, whereas MI-containing systems minimize the singlet-triplet energy gap (ΔE_ST) via optimized singlet-triplet coupling. This work provides a deterministic informatics framework that decodes the “fragment-to-whole” regulation logic, offering practical guidelines for the high-throughput discovery and precision engineering of high-performance optoelectronic materials.