摘要
Artificial intelligence(AI)is profoundly reshaping the discovery and design of organic light-emitting diode(OLED)materials,shifting conventional intuition-driven development into an integrated,datadriven paradigm.The increasing demand for high-performance OLED emitters with ultra-narrow emission spectrum and enhanced operational stability has highlighted the urgent need for a dedicated,multi-scale computational framework tailored to OLED-specific challenges.This review proposes a systematic AI-driven framework that combines quantum chemistry calculations,property prediction models,and generative algorithms to enable high-throughput screening and inverse design workflows for organic luminescent materials.Each component is critically analyzed in terms of theoretical underpinnings,practical benefits,inherent limitations,and avenues for further optimization.By presenting detailed case studies,we elucidate how AI approaches can tackle key bottlenecks in OLED material discovery and development.Moreover,we highlight essential future directions,including the integration of domain-specific expertise,the establishment of high-quality experimentally validated datasets,and the creation of molecular generation models specifically adapted for luminescent materials.Overall,this review aims to provide a comprehensive roadmap for advancing AI-guided materials research,offering transferable insights that extend beyond OLEDs to a broad range of organic optoelectronic materials.
基金
supported by the Beijing Natural Science Foundation(2242054)
the National Natural Science Foundation of China(62075006 and 62475177)。
