Physics-guided machine learning of excited-state properties for the design of high-performance TADF emitters

Physics-informed ML accurately predicts singlet–triplet gaps (Δ E ST ) and oscillator strengths ( f ), outperforming QM/ML baselines. Interpretable models reveal key factors and accelerate high-throughput discovery of TADF emitters for OLEDs.