Tomoyasu Taniyama

Interfacial multiferroic materials

Research Director

Tomoyasu Taniyama

Collaborator

Takashi Kimura	Professor Graduate School of Science Kyushu University
Yoshihiro Gohda	Associate Professor School of Materials and Chemical Technology Tokyo Institute of Technology
Kohei Hamaya	Professor Graduate School of Engineering Science Osaka University

Outline

A novel approach to efficiently designing interfacial multiferroic materials with gigantic magneto-electric correlation is developed by solving inverse problems with inputs from experiments, high-throughput first principles calculations, and Bayesian optimization, which allows of the screening of the materials and interfaces. The preeminence of interfacial multiferroics in materials will be demonstrated by verifying its potential for a variety of innovative device applications such as electric field contol of magnetization, gating technology of thermal rectification, and microwave technology.

Tatsuo Hasegawa

Development of applicable electronic materials by fusion of experimental, computational, and data sciences

Research Director

Tatsuo Hasegawa

Collaborator

Reiji Kumai	Professor Institute of Materials Structure Science High Energy Accelerator Research Organization
Sachio Horiuchi	Chief Senior Researcher Research Institute for Advanced Electronics and Photonics National Institute of Advanced Science and Technology
Hiroyuki Matsui	Professor Graduate School of Organic Materials Science Yamagata University
Koji Yonekura	Group Director SPring-8 Center RIKEN

Outline

This project aims to develop a novel methodology for searching and exploring liquid-form-applicable electronic materials exhaustively and systematically, based on a fusion of experimental, computational, and data sciences. On this basis, we study to develop and sophisticate the materials and their device processing toward industrial application of the printed electronics technology. The following topics are investigated: development of a technique to explore and design molecular materials with use of crystal structure database and machine learning; prediction of precise molecular packing structures and electronic functionalities with use of computational science; synthesis, structure analyses, and evaluation of actual materials and their uses for verifying the computational predictions; development and optimization of techniques for thin-film processing and electronic device fabrications.

Yoshihiro Yamazaki

Development of novel proton-conducting inorganic compounds based on materials science and machine learning

Research Director

Yoshihiro Yamazaki

Collaborator

Yuji Okuyama	Professor Faculty of Engineering University of Miyazaki
Akihide Kuwabara	Chief Researcher Nanostructure Research Laboratory Japan Fine Ceramics Center

Outline

Intermediate-temperature operation of solid oxide fuel cells (SOFCs) would mitigate their cost: it does not need expensive noble metal catalysts and high-temperature structural materials in the system. The intermediate temperature operation, however, has not yet been realized since there is no appropriate electrolyte with proton conductivity higher than 0.01 S/cm at the intermediate temperatures and high chemical stability in a long operation. In this project, we aim to develop such inorganic compounds based on high-proton conduction. As the developing tools, we will combine materials science experiment, machine learning, and theoretical calculations.

Akiyasu Yamamoto

Development of polycrystalline superconducting materials and magnets based on superconducting materials informatics

Research Director

Akiyasu Yamamoto

Collaborator

Kazumasa Iida	Professor College of Industrial Technology Nihon University
Yusuke Shimada	Associate Professor Faculty of Engineering Sciences Kyushu University
Satoshi Hata	Professor Department of Advanced Material Science and Engineering Kyushu University
Akinori Yamanaka	Professor Graduate School of Engneering Tokyo University of Agriculture and Technology

Outline

Transport properties in superconducting polycrystalline materials have been major issues in both materials science and applications. In particular, the transport of macroscopic superconducting current is influenced by the presence of grain boundaries, microstructure and anisotropy in addition to the intrinsic physical properties within the grains. In this project, we will develop a model for predicting the transport properties of such complicated polycrystalline materials. By establishing control methodologies of the intra-grain superconducting property upper limit based on experiment, theory and calculation, and the microstructure based on advanced materials processing, grain boundaries formation simulation and process machine learning, we will propose a new style of materials science to develop a superconducting material as a strong magnet at high speed.