arXiv subjects

Yuma Iwasaki

Publications and source records attributed to Yuma Iwasaki.

7 recordsLinked to original sources

arXiv2026

Inverse-design of two-dimensional magnonic crystals via topology optimization with frequency-domain micromagnetics

Magnonic crystals (MCs) are emerging spintronic metamaterials capable of manipulating transmission properties of magnons, the quanta of spin waves. Due to the complex relationship between lattice geometry and magnonic band dispersion, it remains challenging to establish general design strategies for optimizing targeted properties in MCs. In this study, we demonstrated an inverse-design framework for two-dimensional MCs to explore unconventional lattice structures with large magnonic band gaps. We employed genetic algorithms to enable global exploration of structures with a complete band gap as the objective property, and used frequency-domain micromagnetic simulations for computationally efficient band gap evaluation. Our established inverse-design method successfully discovered several previously unreported designs of MCs, whose performance was validated using time-domain micromagnetic simulations. Furthermore, we observed that the design landscape becomes increasingly non-convex at high-order bands, suggesting the existence of multiple design solutions. The overall inverse-design framework is expected to be broadly applicable to experimentally accessible material systems and device dimensions, facilitating the formulation of design rules for MCs.

cond-mat.mtrl-sci↗

arXiv2024

Autonomous materials search using machine learning and ab initio calculations for L10-FePt-based quaternary alloys

The efficient exploration of expansive material spaces remains a significant challenge in materials science. To address this issue, autonomous material search methods that combine machine learning with ab initio calculations have emerged as a promising solution. These approaches offer a systematic and rapid means of discovering new materials, particularly when the material space is too large. This requirement is particularly important in the development of L10-structured alloys as magnetic recording media. These materials require a high magnetic moment (M) and magnetocrystalline anisotropy energy (EMCA) to satisfy the demands of next-generation data storage technologies. Although autonomous search methods have been successfully applied to various material systems, quaternary L10 alloys with optimized magnetic properties remain an open and underexplored frontier. In this study, we present a simulation-based autonomous search method aimed at identifying quaternary L10 alloys with enhanced M and EMCA values. Over a continuous 100-day search, our system suggested the FeMnPtEr alloy system as a promising candidate, exhibiting superior values for both M and EMCA. Although further experimental validation is required, this study underscores the potential of autonomous search methods to accelerate the discovery of advanced materials. Keywords: L10, FePt, machine learning, ab initio calculations, Bayesian optimization

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↗