Advancing excited-state simulations for TADF emitters: An eXtended Tight-Binding framework for high-throughput screening and design

TL;DR

This paper introduces a fast, semi-empirical computational framework combining extended tight-binding and simplified TD-DFT methods to accurately predict excited-state properties of TADF emitters, enabling high-throughput screening and material design.

Contribution

The work develops an efficient, accurate computational approach for TADF emitters that significantly reduces cost while maintaining predictive quality, facilitating rapid material discovery.

Findings

Accurately predicts singlet-triplet gaps and fluorescence spectra

Achieves over 99% reduction in computational cost

Identifies correlation between molecular torsion and emission redshift

Abstract

We present a computationally efficient framework for predicting the excited-state properties of thermally activated delayed fluorescence (TADF) emitters, integrating extended tight-binding (\xtb), simplified Tamm-Dancoff approximation (\stda), and simplified time-dependent density functional theory (\stddft) methods. Benchmarking against Tamm-Dancoff approximation (noted full \tda) demonstrates that this approach accurately captures key photophysical properties, including singlet-triplet energy gaps, excitation energies, and fluorescence spectra, in both vacuum and solvent environments, while achieving over 99\% reduction in computational cost. We analyze a series of representative TADF emitters, revealing a strong correlation between the torsional angle between donor and acceptor units and the solvent-induced redshift in the emission spectrum. This work highlights the potential of semi-empirical methods for high-throughput screening of TADF materials and provides valuable insights for designing next-generation optoelectronic devices. The multi-objective function is one of a kind, and it further enhances our results with an original solution. While acknowledging the limitations of semi-empirical methods for highly complex systems, we outline promising future directions, including hybrid computational approaches and integration with machine learning techniques, to further improve predictive accuracy and accelerate the discovery of advanced functional materials.