Optimizing and extending ion dielectric polarizability database for microwave frequencies using machine learning methods

Abstract

Abstract Permittivity at microwave frequencies determines the practical applications of microwave dielectric ceramics. The accuracy and universality of the permittivity prediction by Clausius–Mossotti equation depends on the dielectric polarizability (α D ) database. The most influential α D database put forward by Shannon is facing three challenges in the 5 G era: (1) Few data, (2) Simplistic relation and (3) Low frequency (kHz–MHz) oriented. Here, we optimized and extended the Shannon’s database for microwave frequencies by the four-stage multiple linear regression and support vector machine model. In comparison with the conventional database, the optimized and extended databases achieved higher accuracy and expanded the amount of data from 60 to more than 900. Besides, we analyzed the relationships between α D and ion characteristics, including ionic radius (IR), atomic number (N), valence state (V) and coordination number (CN). We found that the positive cubic law of “α D ~ IR 3 ” discussed in Shannon’s work was valid for the IR changed by the N, but invalid for the change caused by the CN.