The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials. However, its practical benefits still remain unproven in real-world applications, particularly in polymer science. We demonstrate the successful discovery of new polymers with high thermal conductivity, inspired by machine-learning-assisted polymer chemistry. This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data, expertise from laboratory synthesis and advanced technologies for thermophysical property measurements. Using a molecular design algorithm trained to recognize quantitative structure-property relationships with respect to thermal conductivity and other targeted polymeric properties, we identified thousands of promising hypothetical polymers. From these candidates, three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications. The synthesized polymers reached thermal conductivities of 0.18-0.41 W/mK, which are comparable to those of state-of-the-art polymers in non-composite thermoplastics .

Corrosion destroys more than three percent of the world's gross domestic product. Therefore, the design of highly corrosion-resistant materials is urgently needed. By breaking the classical alloy-design philosophy, high-entropy alloys (HEAs) possess unique microstructures, which are solid solutions with random arrangements of multiple elements. The particular locally-disordered chemical environment is expected to lead to unique corrosion-resistant properties. In this review, the studies of the corrosion-resistant HEAs during the last decade are summarized. The corrosion-resistant properties of HEAs in various aqueous environments and the corrosion behavior of HEA coatings are presented. The effects of environments, alloying elements, and processing methods on the corrosion resistance are analyzed in detail. Furthermore, the possible directions of future work regarding the corrosion behavior of HEAs are suggested.

Glassy polymers are extremely difficult to self-heal below their glass transition temperature (Tg) due to the frozen molecules. Here, we fabricate a series of randomly hyperbranched polymers (RHP) with high density of multiple hydrogen bonds, which showTgup to 49 °C and storage modulus up to 2.7 GPa. We reveal that the hyperbranched structure not only allows the external branch units and terminals of the molecules to have a high degree of mobility in the glassy state, but also leads to the coexistence of “free” and associated complementary moieties of hydrogen bonds. The free complementary moieties can exchange with the associated hydrogen bonds, enabling network reconfiguration in the glassy polymer. As a result, the RHP shows amazing instantaneous self-healing with recovered tensile strength up to 5.5 MPa within 1 min, and the self-healing efficiency increases with contacting time at room temperature without the intervention of external stimuli.

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Polymers with intrinsic microporosity (PIMs) represent a novel, innovative class of materials with great potential in various applications from high-performance gas-separation membranes to electronic devices. Here, for the first time, for PIM-1, as the archetypal PIM, fast scanning calorimetry provides definitive evidence of a glass transition ( T = 715 K, heating rate 3 × 10 K/s) by decoupling the time scales responsible for glass transition and decomposition. Because the rigid molecular structure of PIM-1 prevents any conformational changes, small-scale bend and flex fluctuations must be considered the origin of its glass transition. This result has strong implications for the fundamental understanding of the glass transition and for the physical aging of PIMs and other complex polymers, both topical problems of materials science.

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In the originally published version of this article, Figure 5C mistakenly included the image of KYSE150 instead of KYSE140. Here we have included the correct image for ZNF750-si KYSE140 cells in Figure 5C. The authors regret the error.