ABSTRACT The discovery of novel organic optoelectronic materials for applications such as organic light‐emitting diodes, organic photovoltaics, and organic field‐effect transistors has traditionally been constrained by the inefficiency of trial‐and‐error experimentation and the vastness of chemical space. Artificial intelligence (AI) has emerged as a transformative paradigm to accelerate this process via data‐driven strategies. This review provides a comprehensive overview of AI integration in organic optoelectronics, tracing the trajectory from fundamental predictive concepts to fully autonomous systems. We first examine core workflows, including the curation of specialized databases and the deployment of machine learning models for high‐throughput virtual‐screening. Subsequently, we detail the pivotal shift toward inverse design, where generative models create de novo molecular structures with targeted functionalities. Particular emphasis is placed on emerging frontiers that are reshaping the field, including leveraging large‐language models for literature mining, the rise of cognitive AI agents for experimental planning, and the integration of robotics to establish self‐driving laboratories. These integrated systems close the design‐synthesis‐characterization loop, enabling materials discovery at an unprecedented pace. This review aims to elucidate the evolving landscape of AI in organic optoelectronics, highlighting critical challenges in data fidelity, transformative strategies for generative design, and the future development direction toward fully autonomous research paradigms.
Zhang et al. (2026) studied this question.