Noritaka USAMI

Multicrystalline informatics toward establishment of general grain boundary physics and realization of high-quality silicon ingot with ideal microstructures

Research Director

Noritaka USAMI

Collaborator

Hiroaki KUDO	Associate Professor Graduate School of Informatics Nagoya University
Tatsuya Yokoi	Lecturer Graduate School of Engineering Nagoya University

Outline

We attempt to pioneer “multicrystalline informatics” to establish general grain boundary physics through collaboration of data collection from large quantities of practical multicrystalline silicon wafers, machine learning and computation. We will show universal guideline to improve performance of multicrystalline materials in spite of their complexity in microstructures, and demonstrate the usefulness of the methodology by realization of high-quality silicon ingot for solar cells with ideal microstructures.

Fumiyasu OBA

Accelerated development of novel semiconductors and dielectrics based on data-driven materials exploration

Research Director

Fumiyasu OBA

Collaborator

Hiroki Taniguchi	Associate Professor Graduate Schcol of Science Nagoya University
Ryo Tamura	Principal Researcher International Center for Materials Nanoarchitectonics National Institute for Materials Science
Yoshitaro NOSE	Associate Professor Graduate School of Engineering Kyoto University
Hidenori Hiramatsu	Professor Institute of Innovative Research Tokyo Institute of Technology

Outline

Conventional approaches to materials exploration solely using experiments require a fair amount of physical and human resources, resulting in a bottleneck in the development of novel materials. The aim of this project is to accelerate materials development by a combination of in silico high-throughput screening, which is based on cutting-edge computational and data science, and advanced experiments including synthesis, characterization, and device fabrication. Accelerated materials discovery will be demonstrated via case studies of semiconductors and dielectrics.

Ken-ichi SHIMIZU

Experimental, theoretical, and data science studies for catalyst informatics

Research Director

Ken-ichi SHIMIZU

Collaborator

Takashi Kamachi	Professor Faculty of Engineering Fukuoka Institute of Technology
Nobutsugu Hamamoto	Assistant professor Faculty of engineering Sanyo-Onoda City University
Yoyo Hinuma	Senior Researcher Department of Energy and Environment National Institute of Advanced Industrial Science and Technology
Zen Maeno	Associate Professor School of Advanced Engineering Kogakuin University

Outline

Catalytic data from experiments and literature (objective variable) is combined with explanatory variable obtained from experiments, theoretical calculation and the literature to establish a catalyst design support system driven by data scientific methods. This system is used for solving essential problems in heterogeneous catalysis and for supporting catalyst development in companies. If artificial intelligence finds a pattern in a big data, we can design a new catalytic material that cannot be conceived by researchers having common sense and limited knowledge.

Ken NAKAJIMA

Topology control of dynamic network on thermoplastic elastomers

Research Director

Ken NAKAJIMA

Collaborator

Ken Kojio	Associate Professor Institute for Materials Chemistry and Engineering Kyushu University
Motoko Kotani	Professor AIMR Tohoku University
Koya Shimokawa	Professor Faculty of Core Research Ochanomizu University
Hiroshi MORITA	Team Leader Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and Technology

Outline

Thermoplastic elastomer (TPE) is a class of copolymers or a polymer blend which exhibits both thermoplastic and elastomeric properties. We aim to realize a tough TPE material which can be an alternative to rubbers. We pursuit its nano-scale structural change during tensile deformation by state-of-the-art experimental techniques, incorporated with data-assimilation simulation which visualize a stress network structure. The visualized network is analyzed by mathematics such as topology. Finally, we propose a mathematical model describing a hierarchic network which emerges multi-functional properties by dynamical structural rearrangement in response to environmental changes.

Shigemi Mizukami

Development of magnetoresistive switching device materials using computational science

Research Director

Shigemi Mizukami

Collaborator

Masafumi SHIRAI

Professor Research Institute of Electrical Communication Information Devices Division Tohoku University

Outline

Magnetic tunnel junction devices consist of a nanometer thick insulating barrier sandwiched between two magnetic layers, which exhibit a large change in electrical resistance with changing direction of a magnetic pole of magnetic layer. This device has been mainly developed in Japan for application to a hard disk drive reading head and non-volatile memory. In this project, novel device materials are explored by the computational physics and data science for the hetero-interface, and we develop innovative magnetic tunnel junction devices much superior than the conventional ones for application to artificial intelligence.