Materials informatics based on evolutionary algorithms: Application to search for superconducting hydrogen compounds

Abstract

We present a materials informatics approach to search for superconducting hydrogen compounds, which is based on a genetic algorithm and a genetic programing. This method consists of five stages: (i) collection of physical and chemical property data, (ii) development of superconductivity predictor based on the collected data by a genetic programing, (iii) prediction of potential candidates for high temperature superconductivity by regression analysis, (iv) crystal structure search of the candidates by a genetic algorithm, and (v) validation of the superconductivity by first-principles calculations. By repeatedly performing the process as (i) $\ensuremath{\rightarrow}$ (ii) $\ensuremath{\rightarrow}$ (iii) $\ensuremath{\rightarrow}$ (iv) $\ensuremath{\rightarrow}$ (v) $\ensuremath{\rightarrow}$ (i) $\ensuremath{\rightarrow}\phantom{\rule{4pt}{0ex}}\ensuremath{\cdots}$, the database and predictor are further improved, which leads to an efficient search for superconducting materials. Using the first-principles data of binary hydrogen compounds, many of which have not been experimentally realized yet, we applied this method to hypothetical ternary ones and predicted ${\mathrm{KScH}}_{12}$ with a modulated hydrogen cage showing the superconducting critical temperature of 122 K at 300 GPa and ${\mathrm{GaAsH}}_{6}$ showing 98 K at 180 GPa.