  We present materials informatics approach to search for superconducting
hydrogen compounds, which is based on a genetic algorithm and a genetic
programming. This method consists of four stages: (i) search for stable crystal
structures of materials by a genetic algorithm, (ii) collection of physical and
chemical property data by first-principles calculations, (iii) development of
superconductivity predictor based on the database by a genetic programming, and
(iv) discovery of potential candidates by regression analysis. By repeatedly
performing the process as (i) $\rightarrow$ (ii) $\rightarrow$ (iii)
$\rightarrow$ (iv) $\rightarrow$ (i) $\rightarrow$ $\dots$, the
superconductivity of the discovered candidates is validated by first-principles
calculations, and the database and predictor are further improved, which leads
to an efficient search for superconducting materials. We applied this method to
hypothetical ternary hydrogen compounds and predicted KScH$_{12}$ with a
modulated hydrogen cage showing the superconducting critical temperature of 122
K at 300 GPa and GaAsH$_{6}$ showing 98 K at 180 GPa.

Superconductivity