In this paper, we incorporate experimental measurements from high-quality databases to construct a machine learning model that is capable of reproducing and predicting the properties of ionic liquids, such as electrical conductivity. Empirical relations traditionally determine the electrical conductivity with the temperature as the main component, and investigations only focus on specific ionic liquids every time. In addition to this, our proposed method takes into account environmental conditions, such as temperature and pressure, and supports generalization by further considering the liquid atomic weight in the prediction procedure. The electrical conductivity parameter is extracted through both numerical machine learning methods and symbolic regression, which provides an analytical equation with the aid of genetic programming techniques. The suggested platform is capable of providing either a fast, numerical prediction mechanism or an analytical expression, both purely data-driven, that can be generalized and exploited in similar property prediction projects, overcoming expensive experimental procedures and computationally intensive molecular simulations.

The AC conductivity response of disordered materials follows a universal power law of the form ${\sigma }^{\prime }\left(\omega \right)\propto {\omega }^n$ at the low frequency regime, with the power exponent values in the range 0 < n < 1. At the high frequency regime, in many experimental data of different disordered materials, superlinear values of the power exponent n were observed. The observed superlinear values of the power exponent are usually within $1<n<2$, but in some cases values $n>2$ were detected. The present work is based on the definitions of electromagnetic theory as well as the Havriliak–Negami equation and the damped harmonic oscillator equation, which are widely used for the description of dielectric relaxation mechanisms and vibration modes in the THz frequency region, respectively. This work focuses mainly on investigating the parameters that affect the power exponent and the range of possible n values.

In this work magnetic nanoparticles (Fe₃O₄, or ZnFe₂O₄, or SrFe₁₂O₁₉) and BaTiO₃ microparticles were embedded in an epoxy resin for the synthesis of three series of hybrid magnetic polymer nanocomposites. Barium titanate content was kept constant, while magnetic phase content was varied. Fabricated specimens were structurally and morphologically characterized by employing scanning electron microscopy images and X-ray diffraction patterns. Results implied successful synthesis of the hybrid nanocomposites. The magnetic behavior of the pure magnetic nanoparticles and the fabricated nanocomposites was investigated via a Vibrating Sample Magnetometer. The magnetic performance of each type of magnetic phase (i.e., soft and hard) was induced in the nanocomposites, and magnetic performance is strengthened with the increase in magnetic phase content. Initial magnetization curves were used for the determination of mass magnetic susceptibility of all nanocomposites. Magnetic saturation and magnetic remanence have been found to follow a linear relationship with magnetic phase content, giving the opportunity to predict the system’s response in advance.