Wu Chao, Chen Lihua, Deshmukh Ajinkya, Kamal Deepak, Li Zongze, Shetty Pranav, Zhou Jierui, Sahu Harikrishna, Tran Huan, Sotzing Gregory, Ramprasad Rampi, Cao Yang
Electrical Insulation Research Center, University of Connecticut, 97 North Eagleville Road, Storrs, Connecticut 06269, United States.
School of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta, Georgia 30332, United States.
ACS Appl Mater Interfaces. 2021 Nov 17;13(45):53416-53424. doi: 10.1021/acsami.1c11885. Epub 2021 Aug 26.
Flexible polymer dielectrics tolerant to electric field and temperature extremes are urgently needed for a spectrum of electrical and electronic applications. Given the complexity of the dielectric breakdown mechanism and the vast chemical space of polymers, the discovery of suitable candidates is nontrivial. We have laid the foundation for a systematic search of the polymer chemical space, which starts with "gold-standard" experimental measurements and data on the temperature-dependent breakdown strength () for a benchmark set of commercial dielectric polymer films. Phenomenological guidelines are derived from this data set on easily accessible properties (or "proxies") that are correlated with . Screening criteria based on these proxy properties (e.g., band gap, charge injection barrier, and cohesive energy density) and other necessary characteristics (e.g., a high glass transition temperature to maintain the thermal stability and a high dielectric constant for high energy density) were then setup. These criteria, along with machine learning models of these properties, were used to screen polymers candidates from a candidate list of more than 13 000 previously synthesized polymers, followed by experimental validation of some of the screened candidates. These efforts have led to the creation of a consistent and high-quality data set of temperature-dependent , and the identification of screening criteria, chemical design rules, and a list of optimal polymer candidates for high-temperature and high-energy-density capacitor applications, thus demonstrating the power of an integrated and informatics-based philosophy for rational materials design.
一系列电气和电子应用迫切需要能够耐受极端电场和温度的柔性聚合物电介质。鉴于介电击穿机制的复杂性以及聚合物庞大的化学空间,找到合适的候选材料并非易事。我们为系统搜索聚合物化学空间奠定了基础,该搜索始于对一组商业介电聚合物薄膜基准样品进行的“金标准”实验测量以及与温度相关的击穿强度()数据。从该数据集得出了关于与相关的易于获取的性质(或“代理性质”)的唯象指导原则。然后基于这些代理性质(例如带隙、电荷注入势垒和内聚能密度)以及其他必要特性(例如用于维持热稳定性的高玻璃化转变温度和用于高能量密度的高介电常数)设定了筛选标准。这些标准连同这些性质的机器学习模型被用于从超过13000种先前合成的聚合物候选列表中筛选聚合物,随后对一些筛选出的候选物进行实验验证。这些工作产生了一个关于与温度相关的的一致且高质量的数据集,并确定了筛选标准、化学设计规则以及用于高温和高能量密度电容器应用的最佳聚合物候选物列表,从而证明了基于集成和信息学理念进行合理材料设计的强大力量。