An Interpretable Multimodal Architecture for the Discovery of High-Efficiency Thermally Activated Delayed Fluorescence Emitters.

Abstract

The scarcity of high-quality experimental data often hampers data-driven materials discovery. This work introduces HoloMat-TADF, a holographic machine learning framework that provides predictive and interpretable insights from limited data sets. Its core feature is the synergistic integration of physicochemical data with robust features distilled via self-supervised pretraining on approximately 650,000 sequences and 130,000 molecular graphs and images. Combined with film-based host properties, this approach bridges the gap from theoretical molecular models to nanoscale organic light-emitting diode (OLED) devices, achieving a predictive <i>R</i>² of 0.847. Furthermore, the deeply interpretable architecture moves beyond black-box predictions to elucidate learned photophysical principles governing nanoscale exciton behaviors. This dual power was prospectively validated through <i>in silico</i> discovery and synthesis of two thermally activated delayed fluorescence (TADF) emitters, Mol-5 and Mol-9. Experimental validation confirmed their favorable properties, including high photoluminescence quantum yield (PLQY >98%), small singlet-triplet energy splitting (Δ<i>E</i>_ST ≈ 0.1 eV), and short delayed fluorescence lifetime (τ_d < 6 μs), congruent with predictions. Consequently, the resulting OLEDs achieved a maximum external quantum efficiency (EQE) of 31.3%. This work establishes an effective framework to accelerate discovery of functional materials in OLEDs and offers a generalizable approach for other data-scarce material systems.