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Our main objective is to predict/discover new functionality materials by means of computational materials design (CMD). In particular, the development of new high-performance permanent magnets is one of our main targets. CMD aims at to design materials and/or structures on the basis of quantum mechanics, corresponding to the inverse problem of quantum simulation. In general, solving such problems is very difficult. In CMD, we solve them by making use of the knowledge, which is obtained through “experiments performed inside computers” using quantum simulations, about underlying mechanisms realizing specific features of materials. The technique of machine learning also can be exploited on the vast data thus created. The developments of new methods of quantum simulation also are our important themes. Among them are developments of methods of accurate first-principles electronic structure calculations in general, linear response theory and first-principles non-equilibrium Green’s function both based on the KKR Green’s function method, order-N screened KKR-method for huge systems, and the methods beyond LDA.
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SOLID STATE COMMUNICATIONS (2023): 115257-115257
Hyperfine Interactionsno. 1 (2021): 1-15
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