arXiv subjects

Masahiko Ishida

Publications and source records attributed to Masahiko Ishida.

7 recordsLinked to original sources

arXiv2022

Composition-tunable magnon-polaron anomalies in spin Seebeck effects in epitaxial Bi$_x$Y$_{3-x}$Fe$_{5}$O$_{12}$ films

We have investigated hybridized magnon-phonon excitation (magnon polarons) in spin Seebeck effects (SSEs) in Bi$_x$Y$_{3-x}$Fe$_{5}$O$_{12}$ (Bi$_x$Y$_{3-x}$IG; $x=0$, $0.5$, and $0.9$) films with Pt contact. We observed sharp peak structures in the magnetic field $H$ dependence of the longitudinal SSE (LSSE) voltages, which appear when the phonon dispersions are tangential to the magnon dispersion curve in Bi$_x$Y$_{3-x}$IG. By increasing the Bi amount $x$, the peak fields in the LSSE shift toward lower $H$ values due to the reduction of the sound velocities in Bi$_x$Y$_{3-x}$IG. We also measured the SSE in a nonlocal configuration and found that magnon-polaron anomalies appear with different signs and intensities. Our result shows composition-tunability of magnon-polaron anomalies and provides a clue to further unravel the physics of magnon-polaron SSEs.

cond-mat.mtrl-sci↗

arXiv2021

Data-driven formulation of natural laws by recursive-LASSO-based symbolic regression

Discovery of new natural laws has for a long time relied on the inspiration of some genius. Recently, however, machine learning technologies, which analyze big data without human prejudice and bias, are expected to find novel natural laws. Here we demonstrate that our proposed machine learning, recursive-LASSO-based symbolic (RLS) regression, enables data-driven formulation of natural laws from noisy data. The RLS regression recurrently repeats feature generation and feature selection, eventually constructing a data-driven model with highly nonlinear features. This data-driven formulation method is quite general and thus can discover new laws in various scientific fields.

physics.data-an↗

arXiv2019

Materials development by interpretable machine learning

Machine learning technologies are expected to be great tools for scientific discoveries. In particular, materials development (which has brought a lot of innovation by finding new and better functional materials) is one of the most attractive scientific fields. To apply machine learning to actual materials development, collaboration between scientists and machine learning is becoming inevitable. However, such collaboration has been restricted so far due to black box machine learning, in which it is difficult for scientists to interpret the data-driven model from the viewpoint of material science and physics. Here, we show a material development success story that was achieved by good collaboration between scientists and one type of interpretable (explainable) machine learning called factorized asymptotic Bayesian inference hierarchical mixture of experts (FAB/HMEs). Based on material science and physics, we interpreted the data-driven model constructed by the FAB/HMEs, so that we discovered surprising correlation and knowledge about thermoelectric material. Guided by this, we carried out actual material synthesis that led to identification of a novel spin-driven thermoelectric material with the largest thermopower to date.

cond-mat.mtrl-sci↗

arXiv2019

Model-Free Cluster Analysis of Physical Property Data using Information Maximizing Self-Argument Training

We present the semi-supervised IMSAT, a versatile classification method that works without labeled data and can be tuned by little additional information. We demonstrate how semi-supervised IMSAT can classify XRD patterns and thermoelectric hysteresis curves in the same way even though their shape and dimensions are different. Our algorithm will accelerate automation of big data collection and open a way to study artificial intelligent driven material development.

physics.comp-ph↗

arXiv2018

Boosting Material Modeling Using Game Tree Search

We demonstrate a heuristic optimization algorithm based on the game tree search for multi-component materials design. The algorithm searches for the largest spin polarization of seven-component Heusler alloys. The algorithm can find the peaks quickly and is more robust against local optima than Bayesian optimization approaches using the expected improvement or upper confidence bound approaches. We also investigate Heusler alloys including anti-site disorder and show that [Fe$_{0.9}$Co$_{0.1}$]$_{2}$Cr$_{0.95}$Mn$_{0.05}$Si$_{0.3}$Ge$_{0.7}$ has the potential to be a high spin polarized material with robustness against anti-site disorder.

physics.comp-ph↗

arXiv2018

Machine-learning guided discovery of a high-performance spin-driven thermoelectric material

Thermoelectric conversion using Seebeck effect for generation of electricity is becoming an indispensable technology for energy harvesting and smart thermal management. Recently, the spin-driven thermoelectric effects (STEs), which employ emerging phenomena such as the spin-Seebeck effect (SSE) and the anomalous Nernst effect (ANE), have garnered much attention as a promising path towards low cost and versatile thermoelectric technology with easily scalable manufacturing. However, progress in development of STE devices is hindered by the lack of understanding of the mechanism and materials parameters that govern the STEs. To address this problem, we enlist machine learning modeling to establish the key physical parameters controlling SSE. Guided by these models, we have carried out a high-throughput experiment which led to the identification of a novel STE material with a thermopower an order of magnitude larger than that of the current generation STE devices.

cond-mat.mtrl-sci↗

arXiv2016

Thermoelectric generation based on spin Seebeck effects

The spin Seebeck effect (SSE) refers to the generation of a spin current as a result of a temperature gradient in magnetic materials including insulators. The SSE is applicable to thermoelectric generation because the thermally generated spin current can be converted into a charge current via spin-orbit interaction in conductive materials adjacent to the magnets. The insulator-based SSE device exhibits unconventional characteristics potentially useful for thermoelectric applications, such as simple structure, device-design flexibility, and convenient scaling capability. In this article, we review recent studies on the SSE from the viewpoint of thermoelectric applications. Firstly, we introduce the thermoelectric generation process and measurement configuration of the SSE, followed by showing fundamental characteristics of the SSE device. Secondly, a theory of the thermoelectric conversion efficiency of the SSE device is presented, which clarifies the difference between the SSE and conventional thermoelectric effects and the efficiency limit of the SSE device. Finally, we show preliminary demonstrations of the SSE in various device structures for future thermoelectric applications and discuss prospects of the SSE-based thermoelectric technologies.

cond-mat.mtrl-sci↗