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Articles by Atsuto Seko
Predictive power of polynomial machine learning potentials for liquid states in 22 elemental systems
The polynomial machine learning potentials (MLPs) described by polynomial rotational invariants have been systematically developed for various systems and used in diverse applications in crystalline states.
Structure search method for atomic clusters based on the dividing rectangles algorithm
The Dividing Rectangles (DIRECT) algorithm is a deterministic optimization method to explore optimal solutions by repeatedly dividing a given hyperrectangle search space into subhyperrectangles. Herein, we propose a structure search method for atomic clusters based on the DIRECT algorithm in combination with a gradient-based local optimizer to enable an efficient structure search in high-dimensional search spaces.
Fast material search of lithium ion conducting oxides using a recommender system
Fast material search of lithium ion conducting oxides using a recommender system† Kota Suzuki, abc Kosei Ohura,a Atsuto Seko,bdef Yudai Iwamizu,a Guowei Zhao,c Masaaki Hirayama,ac Isao Tanakadefg and Ryoji Kanno*ac Author affiliations * Corresponding authors a Department of Chemical Science and Engineering, School of Materials and Chemical Technology, Tokyo Institute of Technology, 4259 Nagatsuta, Midori, Yokohama 226-8502, Japan E-mail: kanno@echem.titech.ac.jp b Japan Science and...
Prediction of perovskite-related structures in $A{\mathrm{CuO}}_{3\ensuremath{-}x}$ ($A$ = Ca, Sr, Ba, Sc, Y, La) using density functional theory and Bayesian optimization
Abstract Oxygen vacancy ordering in perovskite-type transition-metal oxides plays an important role in the emergence of exotic electronic properties, as typified by superconducting cuprates. In this study, we predict the stability of oxygen-deficient perovskite structures in ACuO3−x (A = Ca, Sr, Ba, Sc, Y, La) by density functional theory calculation.
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