Abstract
Automatic exhaustive exploration of a large material space by high-performance supercomputers is crucial for developing new functional materials. We demonstrated the efficiency of high-throughput calculations using the all-electron Korringa-Kohn-Rostoker coherent potential approximation method with the density functional theory for the large material space consisting of quaternary high entropy alloys, which are nonstoichiometric and substitutionally disordered materials. The exhaustive calculations were performed for 147 630 systems based on the AkaiKKR program package and supercomputer Fugaku, where the numerical parameters and self-consistent convergence are automatically controlled. The large material database including the total energies, magnetization, Curie temperature, and residual resistivity was constructed by our calculations. We used frequent itemset mining to identify the characteristics of parcels in magnetization and Curie temperature space. We also identified the elements that enhance the magnetization and Curie temperature and clarified the rough dependence of the elements through regression modeling of the residual resistivity.
Highlights
First-principles calculations, which can accurately investigate the electronic structures, magnetic properties, and transport properties of materials without any empirical parameters, are effective tools for designing new functional materials
We demonstrated the efficiency of high-throughput calculations using the all-electron Korringa-Kohn-Rostoker coherent potential approximation method with the density functional theory for the large material space consisting of quaternary high entropy alloys, which are nonstoichiometric and substitutionally disordered materials
We demonstrate the efficiency of highthroughput calculations for equiatomic quaternary high entropy alloys which are nonstoichiometric and configurational disordered systems, on the basis of the all-electron KorringaKohn-Rostoker (KKR) Green’s function method [14,15] with the coherent potential approximation (CPA) [16,17]
Summary
First-principles calculations, which can accurately investigate the electronic structures, magnetic properties, and transport properties of materials without any empirical parameters, are effective tools for designing new functional materials. With the development of computer hardware and numerical algorithms, material databases have been actively constructed from high-throughput calculations, i.e., exhaustive calculations, based on first-principles approaches. By applying machine learning techniques to the constructed material databases, one can elucidate the mechanisms behind the physical and chemical properties in the target and accelerate the discovery of new materials. The databases containing a wide variety of material data are required to efficiently perform machine learning. Performing exhaustive calculations with high speed and high accuracy for such large material space is not easy. The validity of material-dependent numerical parameters should always be considered.
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