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

Abstract

The rational design of thermally activated delayed fluorescence (TADF) and inverted singlet–triplet (INVEST) emitters demands accurate prediction of critical photophysical properties, particularly singlet–triplet energy gaps (ΔEST) and oscillator strengths (f). Conventional machine learning (ML) models often neglect the underlying physics, limiting their transferability and interpretability across chemical space. In this work, we develop a physics-informed machine learning (PIML) framework that leverages physically meaningful molecular descriptors to predict ΔEST and f with high accuracy and robust generalization. Training on a chemically diverse dataset of over 39 000 compounds, our models achieve correlation coefficients (r) between 0.77 and 0.88 and mean absolute errors (MAE) below 0.1 eV for ΔEST and 0.02 for f on unseen test data. The reliability of the PIML models is further validated via leave-one-out cross-validation and external datasets, including 28 experimentally reported emitters, for which our model outperforms state-of-the-art quantum chemical and ML approaches. Beyond predictive accuracy, integrating interpretability tools reveals the exchange integral, dynamic spin polarization, and excited-state energies as dominant factors controlling the target properties—offering mechanistic insights often inaccessible in standard black-box models. Finally, leveraging the predictive power of the trained models, we performed high-throughput screening of 400 newly designed TADF emitters, successfully identifying promising candidates with optimal ΔEST and f combinations for OLED applications. This study highlights the strength of combining physical intuition with data-driven modeling, offering an efficient, scalable, and interpretable route for accelerating the discovery of next-generation optoelectronic materials.