Ryo Tamura 교수

The cap of a carbon nanotube is characterized by six five-membered rings called disclinations. Its electronic structure is studied by the simple tight-binding models for a monolayer nanotube. The method of the development map is used to systematically define the atomistic structures of the cap. The topological feature of the bond network is determined by the configuration of the six five-membered rings. The effects of such topological features on electronic structure are elucidated by the model

The local density of states (LDOS) of a single disclination and a disclination pair of various configurations in the monolayer graphite is calculated by the recursion method introduced by Haydock [J. Phys. C 5, 2845 (1972)]. The LDOS shows the existence of some resonant states near the Fermi energy. At the Fermi level, the value of the LDOS vanishes for a single disclination of five- and seven-membered rings, and remains a finite value for four- and eight-membered rings. On going away from the d

The conductance of junctions connecting two different metallic carbon nanotubes is calculated by Landauer's formula with a simple tight-binding model. The structures of the junctions are characterized by the relative positions of a pair of disclinations, i.e., a five-membered ring and a seven-membered ring. Conductances of about six thousand kinds of junctions are obtained. The conductance is determined only by the ratio ${\mathrm{R}}_{2}$/${\mathrm{R}}_{1}$ where ${\mathrm{R}}_{1}$ is the the c

The process parameters in powder manufacturing must be optimized to produce high-quality powders with desired sizes depending on the use. Machine learning-driven optimization was applied to determine promising gas atomization process parameters for the manufacture of Ni-Co based superalloy powders for turbine-disk applications. Using a Bayesian optimization without expert assistance, starting from just three sets of data, three optimization cycles were used to determine the gas atomization proce

A sensing signal obtained by measuring an odor usually contains varied information that reflects an origin of the odor itself, while an effective approach is required to reasonably analyze informative data to derive the desired information. Herein, we demonstrate that quantitative odor analysis was achieved through systematic material design-based nanomechanical sensing combined with machine learning. A ternary mixture consisting of water, ethanol, and methanol was selected as a model system whe

Remarkable features of local density of states (LDOS) at a single $n$-membered ring defect $(n=4,5,7,8)$ in monolayered graphite are described. Based on the $n$-fold rotational symmetry at the center of the $n$-membered ring, the wave functions and the spectra of these systems are classified into $n$ kinds of components according to the character of the rotational symmetry group. Based on the analysis using this symmetry, the LDOS can be considered as broadened energy levels of the corresponding

The electron transport through the nanotube junctions that connect different metallic nanotubes by a pair consisting of a pentagonal defect and a heptagonal defect is investigated with Landauer's formula and the effective-mass approximation. From our previous calculations based on the tight-binding model, it is known that the conductance is determined almost only by two parameters, i.e., the energy units of the onset energy of more than two channels and the ratio of the radii of the two nanotube

Knowledge of phase diagrams is essential for material design as it helps in understanding microstructure evolution during processing. The determination of phase diagrams is thus one of the central tasks in materials science. When exploring new materials for which the phase diagram is unknown, experimentalists often try to determine the key experiments that should be performed by referencing known phase diagrams of similar systems. To enhance this practical strategy, we attempted to estimate unkn

An efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique. A key idea of the proposed method is to use extreme values of acquisition functions by Gaussian processes for the next training phase, which should be located near a local maximum or a global maximum of the probability distribution. Our Bayesian optimization technique is applied to the posterior distribution in the effective phys

We develop a method to estimate the spin-spin interactions in the Hamiltonian from the observed magnetization curve by machine learning based on Bayesian inference. In our method, plausible spin-spin interactions are determined by maximizing the posterior distribution, which is the conditional probability of the spin-spin interactions in the Hamiltonian for a given magnetization curve with observation noise. The conditional probability is obtained with the Markov chain Monte Carlo simulations co

We have investigated the relation between magnetic ordered structure and magnetic refrigeration efficiency in the Ising model on a simple cubic lattice using Monte Carlo simulations. The magnetic entropy behaviors indicate that the protocol, which was first proposed in [Tamura et al., Appl. Phys. Lett. 104, 052415 (2014)], can produce the maximum isothermal magnetic entropy change and the maximum adiabatic temperature change in antiferromagnets. Furthermore, the total amount of heat transfer und

The relations between the mechanical properties, heat treatment, and compositions of elements in aluminum alloys are extracted by a materials informatics technique. In our strategy, a machine learning model is first trained by a prepared database to predict the properties of materials. The dependence of the predicted properties on explanatory variables, that is, the type of heat treatment and element composition, is searched using a Markov chain Monte Carlo method. From the dependencies, a facto

NIMS-OS (NIMS Orchestration System) is a Python library created to realize a closed loop of robotic experiments and artificial intelligence (AI) without human intervention for automated materials exploration. It uses various combinations of modules to operate autonomously. Each module acts as an AI for materials exploration or a controller for a robotic experiments. As AI techniques, optimization tools for PHYSics based on Bayesian Optimization (PHYSBO), BoundLess Objective-free eXploration (BLO

A drastically efficient method for identifying electrocatalysts with desirable functionality is a pressing necessity for making a breakthrough in advanced water-electrolyzers toward large-scale green hydrogen production and addressing the significant challenge of carbon neutrality. Despite extensive investigations over the last several centuries, it remains a time-consuming task to identify even one promising affordable electrocatalyst without platinum-group-metal (PGM) for one electrochemical r