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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]

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]