308 citations · 345 across the 8 of their papers we have counts for
9 papers
Composition-tunable magnon-polaron anomalies in spin Seebeck effects in epitaxial BiYFeO films
Takashi Kikkawa, Koichi Oyanagi, Tomosato Hioki +5
We have investigated hybridized magnon-phonon excitation (magnon polarons) in spin Seebeck effects (SSEs) in BiYFeO (BiYIG; , , and $…
Data-driven formulation of natural laws by recursive-LASSO-based symbolic regression
Yuma Iwasaki, Masahiko Ishida
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 p…
Materials development by interpretable machine learning
Yuma Iwasaki, Ryoto Sawada, Valentin Stanev +7
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…
Model-Free Cluster Analysis of Physical Property Data using Information Maximizing Self-Argument Training
Ryohto Sawada, Yuma Iwasaki, Masahiko Ishida
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-s…
Machine-learning guided discovery of a high-performance spin-driven thermoelectric material
Yuma Iwasaki, Ichiro Takeuchi, Valentin Stanev +10
Thermoelectric conversion using Seebeck effect for generation of electricity is becoming an indispensable technology for energy harvesting and smart thermal management. Recently, t…
Boosting Material Modeling Using Game Tree Search
Ryohto Sawada, Yuma Iwasaki, Masahiko Ishida
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 se…