电介质
聚合物
材料科学
电容器
玻璃化转变
光电子学
复合材料
电气工程
电压
工程类
作者
Di‐Fan Liu,Qi‐Kun Feng,Yongxin Zhang,Shao‐Long Zhong,Zhi‐Min Dang
摘要
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.
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