Generative AI-powered inverse design for tailored narrowband molecular emitters

Abstract

As organic display technology progresses, the urgent and daunting challenge lies in the development of next-generation molecular emitters capable of delivering an extensive color gamut with unparalleled color purity. The existing process for uncovering new emitters is largely reliant on a time-consuming and costly trial-and-error method. However, with the integration of AI, the pace of materials discovery is accelerated dramatically. Here, a molecular generation framework, MEMOS, which harnesses the efficiency of Markov molecular sampling techniques alongside multi-objective optimization for the inverse design of molecules, is presented. MEMOS facilitates the precise engineering of molecules capable of emitting narrow spectral bands at desired colors. Utilizing a self-improving iterative process, it can efficiently traverse millions of molecular structures within hours, pinpointing thousands of target emitters with an impressive success rate up to 80%, as validated by density functional theory calculations. Through the use of MEMOS, well-documented multiple resonance cores from the experimental literature have been successfully retrieved, and a broader color gamut has been achieved with the newly identified tricolor narrowband emitters. These findings underscore the immense potential of MEMOS as an efficient tool for expediting the exploration of the uncharted chemical territory of molecular emitters and their experimental discovery.