In this work, a generalized quantitative structure–property relationship (QSPR) model is developed for predicting k p by using norm index (NI)-based descriptors, which is the so-called k p (T, NI)-QSPR model. The as-developed model enables the use of one unified formula to calculate k p values for a wide range of monomers, including linear and branched (meth)­acrylates, nitrogen-containing methacrylates, hydroxyl-containing (meth)­acrylates, and so forth. Importantly, the model exhibits excellent performance when compared with the benchmark k p values from the literature, and model validation proves the reasonable goodness-of-fit, robustness, predictivity, and reliability of the as-developed model. Meanwhile, the Arrhenius parameters show a clear kinetic behavior, indicating that acrylates have smaller fit, robustness, predictivity, and reliability of the as-developed model. Meanwhile, the Arrhenius parameters show a clear kinetic behavior, indicating that acrylates have smaller E a values than methacrylates, which render higher k p values and activities in free-radical polymerization for acrylates. Notably, the model allows the prediction of k p values of monomer mixtures and new monomers. In view of the satisfactory accuracy in determining k p values, it is expected that our proposed method will contribute to the determination of kinetic parameters beyond propagation kinetics for a wide monomer range, and the obtained Arrhenius parameters can further improve the fundamental understanding of radical polymerization kinetics.

Unique structure representation of polymers plays a crucial role in developing models for polymer property prediction and polymer design by data-centric approaches. Currently, monomer and repeating unit (RU) approximations are widely used to represent polymer structures for generating feature descriptors in the modeling of quantitative structure−property relationships (QSPR). However, such conventional structure representations may not uniquely approximate heterochain polymers due to the diversity of monomer combinations and the potential multi-RUs. In this study, the so-called ring repeating unit (RRU) method that can uniquely represent polymers with a broad range of structure diversity is proposed for the first time. As a proof of concept, an RRU-based QSPR model was developed to predict the associated glass transition temperature (T g ) of polyimides (PIs) with deterministic values. Comprehensive model validations including external, internal, and Y-random validations were performed. Also, an RU-based QSPR model developed based on the same large database of 1321 PIs provides nonunique prediction results, which further prove the necessity of RRU-based structure representation. Promising results obtained by the application of the RRU-based model confirm that the as-developed RRU method provides an effective representation that accurately captures the sequence of repeat units and thus realizes reliable polymer property prediction by data-driven approaches.

The well‐defined 2D or 3D structure of covalent organic frameworks (COFs) makes it have great potential in photoelectric conversion and ions conduction fields. Herein, a new donor–accepter (D–A) COF material, named PyPz‐COF, constructed from electron donor 4,4′,4″,4′″‐(pyrene‐1,3,6,8‐tetrayl)tetraaniline and electron accepter 4,4′‐(pyrazine‐2,5‐diyl)dibenzaldehyde with an ordered and stable π‐conjugated structure is reported. Interestingly, the introduction of pyrazine ring endows the PyPz‐COF a distinct optical, electrochemical, charge‐transfer properties, and also brings plentiful CN groups that enrich the proton by hydrogen bonds to enhance the photocatalysis performance. Thus, PyPz‐COF exhibits a significantly improved photocatalytic hydrogen generation performance up to 7542 µmol g−1 h−1 with Pt as cocatalyst, also in clear contrast to that of PyTp‐COF without pyrazine introduction (1714 µmol g−1 h−1). Moreover, the abundant nitrogen sites of the pyrazine ring and the well‐defined 1D nanochannels enable the as‐prepared COFs to immobilize H3PO4 proton carriers in COFs through hydrogen bond confinement. The resulting material has an impressive proton conduction up to 8.10 × 10−2 S cm−1 at 353 K, 98% RH. This work will inspire the design and synthesis of COF‐based materials with both efficient photocatalysis and proton conduction performance in the future.

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Deterministic structure representation of polymers plays a crucial role in developing models for polymer property prediction and polymer design by data-centric approaches. Currently, unique structure representations of polymers, especially the polymers with heteroatomic backbones, are unavailable. In this contribution, we propose a so-called ring repeating unit (RRU) method that can uniquely represent polymers with a broad range of structure diversity. To prove the rationality of RRU-based structure representation for generating feature descriptors, a quantitative structure property relationship (QSPR) model for glass transition temperature (Tg) was established for 1321 polyimides with good accuracy (R2 = 0.8793). Comprehensive model validations including external, internal, and Y-random validations were performed, providing Tg prediction result with an average absolute error (AAE) of 19.38 ℃. It is believed that the as-developed RRU method allows for dealing with any macromolecular structure and targeted property, enabling for reliable polymer property prediction and high-performance polymer design by data-driven approaches.