Kunze Lukas, Froitzheim Thomas, Hansen Andreas, Grimme Stefan, Mewes Jan-Michael
Mulliken Center for Theoretical Chemistry, Clausius Institute for Physical and Theoretical Chemistry, Rheinische Friedrich-Wilhelms Universität Bonn, Beringstraße 4, 53115 Bonn, Germany.
beeOLED GmbH, Niedersedlitzer Str. 75c, 01257 Dresden, Germany.
J Phys Chem Lett. 2024 Aug 8;15(31):8065-8077. doi: 10.1021/acs.jpclett.4c01649. Epub 2024 Jul 31.
Efficient OLEDs need to quickly convert singlet and triplet excitons into photons. Molecules with an inverted singlet-triplet energy gap (INVEST) are promising candidates for this task. However, typical INVEST molecules have drawbacks like too low oscillator strengths and excitation energies. High-throughput screening could identify suitable INVEST molecules, but existing methods are problematic: The workhorse method TD-DFT cannot reproduce gap inversion, while wave function-based methods are too slow. This study proposes a state-specific method based on unrestricted Kohn-Sham DFT with common hybrid functionals. Tuned on the new INVEST15 benchmark set, this method achieves an error of less than 1 kcal/mol, which is traced back to error cancellation between spin contamination and dynamic correlation. Applied to the larger and structurally diverse NAH159 set in a black-box fashion, the method maintains a small error (1.2 kcal/mol) and accurately predicts gap signs in 83% of cases, confirming its robustness and suitability for screening workflows.
高效的有机发光二极管（OLED）需要迅速将单线态和三线态激子转化为光子。具有反转单线态-三线态能隙（INVEST）的分子是完成这项任务的有潜力的候选者。然而，典型的INVEST分子存在诸如振子强度和激发能过低等缺点。高通量筛选可以识别出合适的INVEST分子，但现有方法存在问题：常用的含时密度泛函理论（TD-DFT）方法无法重现能隙反转，而基于波函数的方法又过于缓慢。本研究提出了一种基于无限制Kohn-Sham密度泛函理论（DFT）和常用杂化泛函的态特异性方法。在新的INVEST15基准集上进行调谐后，该方法实现了小于1千卡/摩尔的误差，这可追溯到自旋污染和动态相关之间的误差抵消。以黑箱方式应用于更大且结构多样的NAH159集时，该方法保持较小的误差（1.2千卡/摩尔），并在83%的情况下准确预测能隙符号，证实了其稳健性和适用于筛选工作流程的特性。
