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Machine learning has shown its great potential in the accelerated discovery of advanced materials in the field of computational molecular design. High-temperature polymer dielectrics are urgently required with the emerging applications of energy-storage dielectric film capacitors under high-temperature conditions. Here, we demonstrate the successful prediction of polymers with a high dielectric constant (ɛ) and high glass transition temperature (Tg) using a Bayesian molecular design model. The model is trained on a joint data set containing 382 computed ɛ values using density functional perturbation theory and experimentally measured Tg values of ∼7000 polymers to build relative quantitative structure–property relationships and identify the promising polymers with specific desired range of dielectric constant and glass transition temperature. From the hypothetical polymer candidates, ten promising polymers are proposed based on their predicted properties and synthetic accessibility score for high-temperature dielectric film capacitors' application. Moreover, 250k novel polymer structures are generated with the model to support future polymer informatics research. This work contributes to the successful prediction of high-temperature polymer dielectrics using machine learning models. [ABSTRACT FROM AUTHOR]

Large deformability and high sensitivity is difficult to be realized simultaneously in flexible sensors. Herein, taking advantage of the high permittivity and highly active surfaces of the ultrasmall barium titanate nanoparticles (BT NPs) and the high stretchability of the p(BA‐GMA) elastomer matrix, we propose a high‐performance soft stretchable sensor. The addition of the ultrasmall BT NPs can not only increase the permittivity and capacitance of polyacrylate‐matrix composite dielectric material to obtain a high sensitivity, but also basically maintains the excellent mechanical properties of the polymer matrix. The dielectric constants of the composite films increase from 5.68 to 13.13 at 10 kHz with the increase of BT NPs content from 0 to 15 vol.%, which results in a high capacitance of 236.16 pF for 15 vol.% BT/p(BA‐GMA) sensor. Combining the high permittivity and the large deformability (a maximal deformation of 87.2%), the 15 vol.% BT/p(BA‐GMA) sensor has high sensitivity and shows high linearity and stable output even if under dynamic measurement. The dual‐mode sensor that utilizes the orthogonality of capacitance‐resistance is designed, which shows excellent performance in monitoring human body movements and noncontact measurement. The results present that the BT/p(BA‐GMA)‐based sensor has high stability and reliability not exceed 65°C, which can meet the application requirements in dynamic monitoring. [ABSTRACT FROM AUTHOR]

Polymer composites with high energy density have been of great interest to scientists for a long time, and this accounts for a great wealth of experimental data that are available for interpretation. One of the important phenomena is the variation rate of the permittivity of composites, which always deviates from the linear increase and rises steeply toward the high concentration. Interestingly, the critical transition concentration is always located in the range of 20 vol. %–25 vol. %, but still far less than the common percolation threshold of composites. Through the analysis of the local electric field distribution and surface polarized charges distribution, the phenomenon is successfully interpreted as the effect of the electrostatic interactions between the adjacent polarized particles. Two specified composite configurations (BCC and FCC) are designed, and their dielectric properties have been systematically studied with the help of the finite element method and the phase field method. The simulation results indicate that the strength of the particles' interaction is short-range effective and depends closely on the gap distance between adjacent particles. When the gap distance is smaller than 0.8 times the radius of the particles, which is tantamount to the concentration larger than 20 vol. % and 25 vol. % for BCC and FCC, respectively, the electrostatic interaction of the particles is rather significant, resulting in an additional contribution on the variation of the dielectric properties of composites and vice versa. The synchronous variation tendency between the simulation results and experimental measurements demonstrates the effect of electrostatic interaction on the dielectric properties of composites. [ABSTRACT FROM AUTHOR]

Dielectric elastomer actuators (DEAs) with large electrically-actuated strain can build light-weight and flexible non-magnetic motors. However, dielectric elastomers commonly used in the field of soft actuation suffer from high stiffness, low strength, and high driving field, severely limiting the DEA's actuating performance. Here we design a new polyacrylate dielectric elastomer with optimized crosslinking network by rationally employing the difunctional macromolecular crosslinking agent. The proposed elastomer simultaneously possesses desirable modulus (~0.073 MPa), high toughness (elongation ~2400%), low mechanical loss (tan δm = 0.21@1 Hz, 20 °C), and satisfactory dielectric properties ( ε r = 5.75, tan δe = 0.0019 @1 kHz), and accordingly, large actuation strain (118% @ 70 MV m−1), high energy density (0.24 MJ m−3 @ 70 MV m−1), and rapid response (bandwidth above 100 Hz). Compared with VHBTM 4910, the non-magnetic motor made of our elastomer presents 15 times higher rotation speed. These findings offer a strategy to fabricate high-performance dielectric elastomers for soft actuators. Dielectric elastomer actuators (DEAs) with large electrically actuated strain can be used in non-magnetic motors, but high stiffness, poor strength and slow response currently limit the application of DEAs. Here, the authors optimize the crosslinking network in a polyacrylate elastomer to enable a DEA with high toughness and actuation strain and use the polyacrylate to build a motor which can be driven under low electric field. [ABSTRACT FROM AUTHOR]