Enhancing the prediction of TADF emitter properties using Δ-machine learning: a hybrid semi-empirical and deep tensor neural network approach

Abstract

This study presents a machine learning (ML)-augmented framework for accurately predicting excited-state properties critical to thermally activated delayed fluorescence (TADF) emitters. By integrating the computational efficiency of semi-empirical PPP+CIS theory with a Δ-ML approach, the model overcomes the inherent limitations of PPP+CIS in predicting key properties, including singlet (S1) and triplet (T1) energies, singlet–triplet gaps (ΔEST), and oscillator strength (f). The model demonstrated exceptional accuracy across datasets of varying sizes and diverse molecular features, notably excelling in predicting oscillator strength and ΔEST values, including negative regions relevant to TADF molecules with inverted S1–T1 gaps. This work highlights the synergy between physics-inspired models and machine learning in accelerating the design of efficient TADF emitters, providing a foundation for future studies on complex systems and advanced functional materials.