Research on key technologies for AI-assisted design of high-performance optoelectronic materials

Abstract

Artificial intelligence technology is profoundly changing the research and development paradigm of high-performance optoelectronic materials. This paper systematically summarizes the core methods of AI-assisted design, namely, revealing the structure-activity relationship of materials through multi-scale modeling and intelligent computing; using high-throughput virtual screening and reverse design to
break the traditional trial-and-error limitations; using a combination of dynamic performance prediction and defect analysis to optimize its
stability; relying on cross-modal data fusion to mine implicit rules. It is applied to the discovery of perovskite materials, band regulation of
organic materials, analysis of device attenuation mechanisms and composite electrode optimization scenarios. The results show that this technology can significantly accelerate the development cycle of new materials, enhance the photoelectric conversion efficiency and device life,
and is a new driving force for the new generation of optoelectronic devices.