Machine learning-assisted exploration of a versatile polymer platform with charge transfer-dependent full-color emission

Polymerization-Mediated Through-Space Charge Transfer: An Emerging Strategy for Light-Emitting Materials

“…Based on the aforementioned results, we subsequently investigated the NDI polymer systems by introducing polycyclic aromatic monomers with stronger electron-donating ability (Figure a and b), and aimed to develop a general strategy for the design of TSCT fluorescent polymers with widely tunable emission . In recent years, machine learning has emerged as a powerful tool for prediction of physical properties of unknown chemical structures, including polymers. − Before conducting the new polymer synthesis, we built a machine learning model based on the structure and photophysical properties of previously synthesized NDI-initiated styrenic polymers, and then predicted the TSCT emission wavelength of the unknown polymers based on polycyclic aromatic monomers.…”

“…Benefiting from the efficient TSCT emission in solid state, photoresponsive solid films can be prepared based on the polymers synthesized by NDI-initiated polymerization of anthracene-derived electron-donating monomers . The transformation from red TSCT emission to blue local emission (or styrene-to-NDI TSCT emission) was easily realized under UV LED light irradiation for few minutes, due to the interruption of the original TSCT process upon photocycloaddition of anthracene moieties.…”

“…Importantly, this makes it possible that even the donor and acceptor are not adjacent to each other in a polymer structure, efficient TSCT can still occur as long as they are in spatial proximity in the aggregate state. Based on our recent simulation result with simplified model structures, the CT excited state can dominate the emission when donor-to-acceptor distance is within 7 Å in three-dimensional space . This means the CT process could be highly sensitive to the donor–acceptor interactions in the subnanometer scale, which may provide a new space in between the few angstroms of donor–acceptor distance in through-bond CT small molecules, and the sub-10 nm scale in energy transfer systems (i.e., Förster resonance energy transfer, FRET).…”

Advanced Biomaterials for 3D Printing via a Synergistic Dual-Photopolymer Design

“…At the same time, new frontiers in modern materials development can be introduced to 3D-printing dual-photopolymer design. For example, machine learning − can be used to predict which materials will work best for 3D-printed, biodegradable objects for certain applications, based on the database integrating the viscosities, mechanical properties, and degradability of cross-linking networks corresponding to existing photopolymers with different structures and chain lengths. On the other hand, a high-throughput process could be integrated into the material screening and function characterization to accelerate their biomedical applications, such as drug delivery. − Additionally, multiple-photopolymer formulations could also be considered to reach more complex polymer networks beyond the dual-photopolymer design.…”

Automatically Predicting Material Properties with Microscopic Images: Polymer Miscibility as an Example

“…These blends have been extensively researched due to their capacity to combine the advantageous properties of different polymers and their ease of synthesis and processing. Researchers have utilized machine learning methods to investigate their properties, including the mechanical properties, liquid crystal behavior, thermal conductivity, dielectric constants, optical properties, and molecular design . Among all kinds of properties, polymer miscibility is the primary basis for determining the structure and properties of blends, and it is difficult for immiscible polymers to form materials with good properties.…”