Challenges and Opportunities in Machine Learning for Light‐Emitting Polymers

Light-emitting polymers (LEPs) combine the luminescent properties of organic emitters with the structural versatility of polymers, supporting applications in solid-state display, chemical sensing, and bioimaging, owning to the efficient tuning of their performance across multiple scales, from monomer units and chain sequence to solid-state packing and solution processing. Recent strategies have expanded emission color space, improved quantum yields, and simplified design rules, evolving from traditional π-conjugated systems to mechanisms driven by aggregation and charge transfer. Yet this multiscale flexibility also creates a vast and complex design space, where the interplay of monomer choice, polymer architecture, and processing methods makes it impossible to exhaustively map their structure-property relationships by empirical means. In this perspective, we review the development of recent design strategies in LEPs, highlighting the key experimental challenges they reveal, and discuss how data-driven approaches, particularly machine learning, can help navigate this complexity and accelerate the discovery and optimization of next-generation LEPs.