We develop and use data-driven materials design (materials informatics) which is a fusion of data science and material science to explore new functional materials realizing next-generation electronics to replace the present Si-CMOS technology. Materials design is an inverse problem of general simulation and is actually very difficult task. Due to the developments of computer performances and numerical algorithms, nowadays one can not only analyze physical properties in real systems but also design hypothetical systems with novel functionalities, based on quantum mechanical electronic structure calculations. However, it is almost impossible to perform systematic exploration in an infinitely wide range of materials space. In order to overcome such problem, we need to perform materials exploration by materials informatics which can extract useful knowledges quickly from large-scale materials database. Our main purpose is to efficiently solve the inverse problem from “functionality” to “material” by the data-driven material design method. We are also developing the large-scale DFT calculation package “KKRnano”, where the full potential screened Korringa-Kohn-Rostoker (KKR) Green’s function method is optimized by a massively parallel linear scaling (order-N) all electron algorithm.

Computational materials design engine (CMD®), which consists of “quantum simulation”, “deduction of physical mechanism”, and “guess of hypothetical materials”.

Automatic high throughput screening of quaternary magnetic high entropy alloys by AkaiKKR code.

Research Subjects

Large-scale DFT calculations by order-N screened KKR Green’s function method

Design of new functional materials by materials informatics

Computational materials design

Electronic structure and magnetism in substitutional and structural disordered nano-materials