Generative AI-Powered Inverse Design for Tailored Narrowband Molecular Emitters

In organic displays, developing molecules that produce a broad color gamut with exceptional color purity is of critical importance. AI-assisted molecular screening can expedite the design process of emission molecules. However, the efficiency of current methodologies is constrained by their limited candidate pools and poor hit rates. Here we present MEMOS, a cutting-edge molecular generation framework that, through Markov molecular sampling techniques, facilitates the targeted inverse design of molecules across a nearly boundless chemical space, tailored to emit the narrow spectral bands associated with desired colors. Notably, by employing a self-improving iterative process, MEMOS achieves an impressive hit rate of up to 80%. Our method showcases the pioneering capability to rapidly navigate through millions of molecular possibilities, efficiently pinpointing thousands of high-potential candidates within a 24-hour period. This breakthrough accelerates the design of novel organic luminescent materials, setting the stage for the advancement of the next generation of high-quality organic displays.