Donor-acceptor-donor “hot exciton” triads for high reverse intersystem crossing in OLEDs

Hot exciton materials have the potential to improve the quantum efficiency of organic light-emitting diodes (OLEDs) by promoting high Reversed InterSystem Crossing (hRISC) between a high-lying triplet (Tn, n≥2) and a radiative singlet (Sm). In recent years, donor–acceptor-donor (D-A-D) molecular systems have shown great promise in its ability to enhance the hRISC process under certain conditions. However, strategies to find appropriate D-A-D combinations beyond trial-and-error are still elusive. This work exposes the limited applicability of the current fragment-based design rules and proposes high-throughput screening as the optimal route to find promising candidates that fulfill the energy criteria for hot exciton materials. The strategy consists of first establishing the thresholds for large triplet-triplet splitting and small singlet-triplet gap, then filtering combinations through rate comparison of competitive crossing pathways, and finally confirming hRISC with spin-orbital coupling (SOC) evaluation. Based on a curated dataset of 234 D-A-D compounds, our protocol identifies 31 promising candidates with potential for hRISC, 4 of which have been previously reported in the literature. Remarkably, while most of the promising systems show prominent hybridized local and CT (HLCT) character, several candidates do not fulfill this condition, indicating that different routes are possible to design efficient OLED materials.

Identifier
Source https://archive.materialscloud.org/record/2022.59
Metadata Access https://archive.materialscloud.org/xml?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:1323
Provenance
Creator Zhu, Yanan; Vela, Sergi; Meng, Hong; Corminboeuf, Clémence; Fumanal, Maria
Publisher Materials Cloud
Publication Year 2022
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Contact archive(at)materialscloud.org
Representation
Language English
Resource Type Dataset
Discipline Materials Science and Engineering