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

Article

关键词: CRYSTAL-STRUCTURES;    DOPED HYDROGEN;    T-C;    PRESSURE;    HYDRIDES;    TEMPERATURE;    PREDICTION;    PHASE;    POLYHYDRIDES;    STABILITY;   

DOI  :  10.1103/PhysRevB.100.174506

来源: SCIE

【 摘 要 】

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) -> (ii) -> (iii) -> (iv) -> (v) -> (i) -> . . . , 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 KScH12 with a modulated hydrogen cage showing the superconducting critical temperature of 122 K at 300 GPa and GaAsH6 showing 98 K at 180 GPa.

【 授权许可】

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