# High Dielectric Design of Polymer Composites by Using Artificial Neural Network
## 1 Introduction  
Dielectric capacitors have been used in pulsed power weapons, electrical power systems and hybrid electric vehicles (HEVs) due to their capability of ultrafast charging-discharging rate and ultrahigh power density [1; 2]. In particular, dielectric polymers such as polyvinylidene fluoride (PVDF) are one of the most promising material candidates for high-density energy storage applications because of their high breakdown strength, low dielectric loss, facile fabrication, low cost, and flexibility [3; 4]. Dielectric materials store electrostatic energy through reversible orientation polarization under applied electric field. High discharged energy density can be attained by high dielectric property. However, most dielectric polymers have a low dielectric constant, which limits their application to energy storage devices. In order to improve their dielectric properties, many studies have been conducted by introducing high dielectric constant ceramic fillers such as BaTiO\({}_{3}\)[5], BaSrTiO\({}_{3}\)[6], and Pb(Zr,Ti)O\({}_{3}\)[7] as fillers into polymer matrices to improve their dielectric properties. It is important to predict the dielectric constant of the polymer composites to achieve a desirable energy density. However, it is difficult to predict the dielectric constant of the polymer composites because of nonlinearity in the increase of a dielectric constant and diverse factors such as filler's morphology, post processing of filler, and frequency [8; 9; 10; 11; 12]. In order to predict the dielectric constant of a polymer composite, the methods of using theoretical prediction models or directly measuring through experiments has been used. [PERSON] et al. [13]introduced modified Rother-Lichtenecker, Maxwell-Wagner, and Jayasundere-Smith models to compare experimental data and prediction models. [PERSON] et al. [14] used Maxwell, Furukawa, and Rayleigh prediction models to compare which models showed high accuracy with experimental data. However, these theoretical prediction models have limitations in predicting the dielectric constant of composites. It is difficult to consider all factors, so the theoretical models approximate many factors except for main variables. These models use only a dielectric constant of a matrix and filler, and filler's volume fraction excluding other conditions such as filler's morphology and size, frequency, and filler's post processing. Therefore, the theoretical models can predict the dielectric properties in only simple composite models. With the advent of machine learning (ML) technology based on experimental and computational data, the machine learning method has been widely applied as a popular and powerful alternative for material and structural design or for predicting electrical properties. [PERSON] et al. [15] introduced a ML model to study the effect of nanoparticles physical properties on the breakdown strength of poly(vinylidene fluoride)-co-hexafluoropropylene [P(VDF-HFP)]-based nanocomposites, which was performed on a dataset from the high-throughput phase-filed simulations. [PERSON] et al. [16] applied a ML model to investigate the influence of the polymer's key molecular descriptors on the dielectric constant. Although these studies opened up chances for ML and data-driven methods in dielectric polymer-based composite, comprehensive research on a more practical predictive model considering various important factors to predict the dielectric constant has not yet been considered. In this study, an ML model based on an artificial neural network is developed to predict the dielectric property of polymer composites using hundreds of experimentally measured data from existing references in consideration of various polymer matrices and ceramic nanofillers. An artificial neural network (ANN) network is trained with collected data considering the dielectric constant of matrix, filler, and shell, volume fraction, diameter and morphology of filler, operating frequency, and shell thickness in core-shell structures.