Traditional optical organic vapor sensors with solvatochromic shift mechanisms have lower sensitivity due to weak intermolecular interactions. Here, we report a general strategy to prepare a higher sensitivity optical organic vapor sensor through polymeric swelling-induced variation of fluorescent intensity. We combine one-dimensional polymeric structures and aggregation-induced emission (AIE) molecules together to form a polymer/AIE microwires array as a sensor. The prepared sensors based on different commercial polymers can successfully classify and identify various organic vapors. Among them, the poly(vinyl butyral)/AIE microwires array can detect methanol vapor as low as 0.05% of its saturation vapor pressure. According to the theory of like dissolves like, we further fabricate a polymer/AIE microwires array derived from designable polyethersulfones, through regulating their side chains, to distinguish similar organic vapors of benzene and toluene. Both experimental and theoretical simulation results reveal that specific molecular interactions between the polyethersulfones and organic vapors can improve the specific recognition performance of the sensors.

Machine learning (ML), material genome, and big data approaches are highly overlapped in their strategies, algorithms, and models. They can target various definitions, distributions, and correlations of concerned physical parameters in given polymer systems, and have expanding applications as a new paradigm indispensable to conventional ones. Their inherent advantages in building quantitative multivariate correlations have largely enhanced the capability of scientific understanding and discoveries, thus facilitating mechanism exploration, target prediction, high‐throughput screening, optimization, and rational and inverse designs. This article summarizes representative progress in the recent two decades focusing on the design, preparation, application, and sustainable development of polymer materials based on the exploration of key physical parameters in the composition–process–structure–property–performance relationship. The integration of both data‐driven and physical insights through ML approaches to deepen fundamental understanding and discover novel polymer materials is categorically presented. Despite the construction and application of robust ML models, strategies and algorithms to deal with variant tasks in polymer science are still in rapid growth. The challenges and prospects are then presented. We believe that the innovation in polymer materials will thrive along the development of ML approaches, from efficient design to sustainable applications.

To explore the inhibitory mechanism of catechins for digestive enzymes, we investigated the binding mode of catechins to a typical digestive enzyme-trypsin and analyzed the structure-activity relationship of catechins, using an integration of molecular docking, molecular dynamics simulation and binding free energy calculation. We found that catechins with different structures bound to a conservative pocket S1 of trypsin, which is comprised of residues 189–195, 214–220 and 225–228. In the trypsin-catechin complexes, Asp189 by forming strong hydrogen bonding, and Gln192, Trp215 and Gly216 through hydrophobic interactions, all significantly contribute to the binding of catechins. The number and the position of hydroxyl and aromatic groups, the structure of stereoisomers, and the orientation of catechins in the binding pocket S1 of trypsin all affect the binding affinity. The binding affinity is in the order of Epigallocatechin gallate (EGCG) > Epicatechin gallate (ECG) > Epicatechin (EC) > Epigallocatechin (EGC), and 2R-3R EGCG shows the strongest binding affinity out of other stereoisomers. Meanwhile, the synergic conformational changes of residues and catechins were also analyzed. These findings will be helpful in understanding the knowledge of interactions between catechins and trypsin and referable for the design of novel polyphenol based functional food and nutriceutical formulas.