[Revolutional Materials Development] Year Started : 2017

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

Professor
Graduate School of Engineering
Nagoya University

Collaborator
Yutaka OHNO Academic Researcher
Institute for Materials Research
Tohoku University
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

Professor
Institute of Innovative Research
Tokyo Institute of Technology

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

Professor
Catalysis Research Center
Hokkaido University

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

Professor
School of Materials and Chemical Technology
Tokyo Institute of Technology

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

Professor
Advanced Institute for Materials Research Device/System
Tohoku University

Collaborator
Masafumi SHIRAI Professor
Research Institute of Electrical Communication Information Devices Division
Tohoku University
Atsufumi Hirohata Professor
Department of Electronic Engineering
University of York(England)
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.

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