Machine Learning-Guided Discovery of Sterically Protected High Triplet Exciplex Hosts for Ultra-Bright Green OLEDs

Machine learning (ML) has been widely used to accelerate the discovery of organic light-emitting diode (OLED) materials, but its application to improving device-level performance has been limited. Here, we develop an ML workflow that explicitly incorporates exciplex-specific design criteria, including exciplex-considered high triplet energy criteria, deep lowest-unoccupied molecular orbital (LUMO) alignment, high bond dissociation energy (BDE), and proper reorganization energy, as factors that directly link the molecular structure with device stability and exciton dynamics. Based on this physics-informed approach, we screen n-type hosts for phosphorescence-sensitized fluorescent (PSF) OLEDs, and identify two silane-functionalized n-type hosts, DPSiTrz and DBiPSiTrz, that successfully form an exciplex with p-type BPP-BCZ. Bulky silane groups are introduced to prevent aggregation-induced quenching while maintaining donor-acceptor electronic coupling to form an exciplex. As a result, these exciplex hosts yield high triplet energies (>2.50 eV), reduced nonradiative decay, and minimal back energy transfer (BET) from the phosphorescent sensitizer Ir(ppy)₂(acac). The fabricated green PSF OLEDs based on these exciplex hosts show external quantum efficiencies (EQEs) of up to 39.4%, with limited efficiency roll-off (L₉₀ > 100,000 cd m^-2) and long operational stability (LT₉₅ = 134.4 h at 5000 cd m^-2), validating that the exciplex-informed ML design rules translate into experimentally robust devices. These results demonstrate that an ML-enabled molecular design strategy that can represent device-level exciton behavior and long-term stability is an effective method to discover high-efficiency, durable OLEDs.