Atsuto Seko 교수

Compounds of low lattice thermal conductivity (LTC) are essential for seeking thermoelectric materials with high conversion efficiency. Some strategies have been used to decrease LTC. However, such trials have yielded successes only within a limited exploration space. Here, we report the virtual screening of a library containing 54,779 compounds. Our strategy is to search the library through Bayesian optimization using for the initial data the LTC obtained from first-principles anharmonic lattic

The representations of a compound, called ``descriptors'' or ``features'', play an essential role in constructing a machine-learning model of its physical properties. In this study, we adopt a procedure for generating a set of descriptors from simple elemental and structural representations. First, it is applied to a large data set composed of the cohesive energy for about 18 000 compounds computed by density functional theory calculation. As a result, we obtain a kernel ridge prediction model w

A combination of systematic density-functional theory (DFT) calculations and machine learning techniques has a wide range of potential applications. This study presents an application of the combination of systematic DFT calculations and regression techniques to the prediction of the melting temperature for single and binary compounds. Here we adopt the ordinary least-squares regression, partial least-squares regression, support vector regression, and Gaussian process regression. Among the four

The structures and stabilities of a series of nonstoichiometric SnO2-x compounds, which are yet unknown experimentally, are predicted using the cluster expansion technique combined with first-principles calculations. A homologous series of Sn(n+1)O(2n) in which oxygen vacancies are layered on (101) planes of the rutile lattice is discovered. The homologous crystals are composed of divalent and quadrivalent Sn atoms. No trivalent Sn atoms are formed.

The cluster expansion (CE) method has been used to evaluate configurational properties in multicomponent systems based on the density-functional theory (DFT) calculations. Appropriate selections of not only clusters but also structures for DFT calculations (DFT structures) are crucial for the accuracy and the efficiency of the CE. In a conventional procedure to construct the CE, the CE error is reduced mainly through an appropriate selection of clusters. In the present paper, we propose an impro

Ground-state structures of six II-III spinel oxides are predicted by combining the cluster expansion method and first principles calculations. The ground states of $\mathrm{Mg}{\mathrm{Ga}}_{2}{\mathrm{O}}_{4}$ and $\mathrm{Mg}{\mathrm{In}}_{2}{\mathrm{O}}_{4}$ are found to be inverse spinels with a tetragonal lattice, whereas those of $\mathrm{Mg}{\mathrm{Al}}_{2}{\mathrm{O}}_{4}$, $\mathrm{Zn}{\mathrm{Al}}_{2}{\mathrm{O}}_{4}$, $\mathrm{Zn}{\mathrm{Ga}}_{2}{\mathrm{O}}_{4}$, and $\mathrm{Zn}{\

The pressure-induced phase transition from wurtzite to rocksalt in ZnO was investigated using first-principles lattice dynamics calculations within the quasi-harmonic approximation. Structural and thermodynamical properties at finite temperatures were well reproduced for both phases. The transition pressure shows negative temperature dependence inconsistent with previous experimental observations. This can be attributed to a greater increase in vibrational entropy of the rocksalt phase with temp

We propose a simple scheme to estimate the potential energy surface (PES) for which the accuracy can be easily controlled and improved. It is based on model selection within the framework of linear regression using the least absolute shrinkage and selection operator (LASSO) technique. Basis functions are selected from a systematic large set of candidate functions. The sparsity of the PES significantly reduces the computational cost of evaluating the energy and force in molecular dynamics simulat

Interatomic potentials have been widely used in atomistic simulations such as molecular dynamics. Recently, frameworks to construct accurate interatomic potentials that combine a set of density functional theory (DFT) calculations with machine learning techniques have been proposed. One of these methods is to use compressed sensing to derive a sparse representation for the interatomic potential. This facilitates the control of the accuracy of interatomic potentials. In this study, we demonstrate

Descriptors, which are representations of compounds, play an essential role in machine learning of materials data. Although many representations of elements and structures of compounds are known, these representations are difficult to use as descriptors in their unchanged forms. This chapter shows how compounds in a dataset can be represented as descriptors and applied to machine-learning models for materials datasets.

The recent discovery of free oligosaccharides typical for the complex type of glycan chains terminating with a free di-N-acetylchitobiosyl structure in certain fish eggs and early embryos (Ishii, K., Iwasaki, M., Inoue, S., Kenny, P. T. M., Komura, H., and Inoue, Y. (1989) J. Biol. Chem. 264, 1623-1630; Seko, A., Kitajima, K., Iwasaki, M., Inoue, S., and Inoue, Y. (1989) J. Biol. Chem. 264, 15922-15929; Inoue, S., Iwasaki, M., Ishii, K., Kitajima, K., and Inoue, Y. (1989) J. Biol. Chem. 264, 185

Chemically relevant compositions (CRCs) and atomic arrangements of inorganic compounds have been collected as inorganic crystal structure databases. Machine learning is a unique approach to search for currently unknown CRCs from vast candidates. Herein we propose matrix- and tensor-based recommender system approaches to predict currently unknown CRCs from database entries of CRCs. Firstly, the performance of the recommender system approaches to discover currently unknown CRCs is examined. A Tuck

The temperature dependence of cationic disorder in $\mathrm{Mg}{\mathrm{Al}}_{2}{\mathrm{O}}_{4}$ spinel is investigated using a combination of first-principles total-energy calculations, a cluster expansion, and canonical Monte Carlo simulations. The formation energies of the possible cation-disordered structures within the spinel unit cell are predicted to be all positive, suggesting that the ground state is the normal spinel in consistency with a widely accepted view. The temperature dependen

Many rotational invariants for crystal structure representations have been used to describe the structure-property relationship by machine learning. The machine learning interatomic potential (MLIP) is one of the applications of rotational invariants, which provides the relationship between the energy and the crystal structure. Therefore, the enumeration of rotational invariants should be useful for constructing MLIPs with the desired accuracy. In this study, we introduce high-order linearly ind

New glycoproteins of 100-120 kDa were isolated from the unfertilized eggs of flounder, Paralichthys olivaceus. Compositionally indistinguishable glycopeptides of 6 kDa were also purified from the activated or fertilized eggs. These high and low molecular mass glycoproteins are characterized by high (about 85%) carbohydrate content. Although some heterogeneities exist in the amino acid sequences, the 6-kDa glycopeptides (decapeptides with single large N-linked glycan chains), isolated from the fe