Advancing efficiency in deep-blue OLEDs: Exploring a machine learning-driven multiresonance TADF molecular design.

ABSTRACT: The pursuit of boron-based organic compounds with multiresonance (MR)-induced thermally activated delayed fluorescence (TADF) is propelled by their potential as narrowband blue emitters for wide-gamut displays. Although boron-doped polycyclic aromatic hydrocarbons in MR compounds share common structural features, their molecular design traditionally involves iterative approaches with repeated attempts until success. To address this, we implemented machine learning algorithms to establish quantitative structure-property relationship models, predicting key optoelectronic characteristics, such as full width at half maximum (FWHM) and main peak wavelength, for deep-blue MR candidates. Using these methodologies, we crafted ν-DABNA-O-xy and developed deep-blue organic light-emitting diodes featu

SUBMITTER: Kim HS 

PROVIDER: S-EPMC11753437 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature