Machine Learning-assisted Exploration of a Universal Polymer Platform with Charge Transfer-dependent Full-color Emission

Understanding the color tuning of solid-state emissive materials is essential from a fundamental mechanistic viewpoint, as well as for practical applications. The development of color-tunable fluorescent materials with simple chemical compositions and easy to synthesize is highly desirable, but practically challenging. Despite copious research into molecular design and engineering, a general and facile polymer platform that offers high flexibility and broad extensibility in emission color tuning is still lacking. Here, we report a universal yet simple platform based on through-space charge transfer (TSCT) polymers, that has full-color tunable emission and was developed with the aid of predictive machine learning models. Using a single acceptor (A) fluorophore as the initiator for atom transfer radical polymerization (ATRP), a series of electron donor (D) groups containing simple polycyclic aromatic moieties (e.g., pyrene) are introduced either by one-step copolymerization or by end-group functionalization of a pre-synthesized polymer. By manipulating donor-acceptor (D-A) interactions via controlled polymer synthesis, continuous blue-to-red emission color tuning was easily achieved in solid polymers. Theoretical investigations confirm the structurally dependent TSCT-induced emission redshifts. We also exemplify how these TSCT polymers can be used as a general design platform for solid-state stimuli-responsive materials with high-contrast photochromic emission, by applying them to proof-of-concept information encryption.