Innovate the materials research and development with integrating data

2021.06.30 Update
Japanese government is promoting the “Society 5.0” concept in order to realize the future society which is characterized by the sophisticated integration of cyberspace with physical space (“the real world”).
Following this, the Research and Services Division of Materials Data and Integrated System (MaDIS) was newly established in NIMS in April 2017 with the aim of enhancing the research and development (R&D) of “Integrated materials development system”, which will be an important system to connect the ICT (cyber) and the Materials (physical), and the materials data platform supporting this system.
This division sets out to offer a paradigm for the materials R&D which leads the significant acceleration of R&D with integrating data science, computational science, theory and experiment.
The “SIP-MI Lab.” that develops technologies for materials integration as a part of the “Structural Materials for Innovation” program which is leaded by the Cabinet Office since FY 2014 and the “Center for Materials research by Information Integration” that was established in 2015 were incorporated in this division. The “MOP-MI Lab.” that utilizes materials informatics at the Materials Open Platform (MOP), where NIMS promotes open innovation with industries, has been launched. Along with this, the “Materials Data Platform Center” that was newly established in this division constructs the world’s largest and highly functional materials data platform as a primary effort to support the integrated materials development system.



News

Recycling system of epoxy resin with peptide solution.
2021.07.29 Update Press Release

Simple Thermoset Plastic Recycling Using a Peptide Solution

—New Strategy for Promoting the Reuse of Carbon Fiber Reinforced Plastics (CFRP)—

2021.06.21 Update Press Release

Digitalization and Visualization of Odors Using an Odor Sensor and Machine Learning

—Technique Is Capable of Selecting “Quasi-Primary Odors” Out of a Dozen Odors—

2020.11.30 Update Press Release

Achieving Cost-Efficient Superalloy Powder Manufacturing Using Machine Learning

—Technique Efficiently Optimizes Complex Manufacturing Processes and May Reduce Aircraft Engine Component Production Costs—


Research Organization

Energy Materials Design Group
Data-driven Polymer Design Group
Data-driven Structural Materials Group
Device Materials Informatics Group
Data-driven Inorganic Materials Group
SIP-MI Laboratory 3D Additive Manufacturing Team
Non-equilibrium Alloy Design Team
Powder Processing MI Team
Heat Resistant Alloy MI Team
CFRP-MI Team
Materials Integration System Team
Structural Materials Database Team
Materials Data Platform Center Materials Database Group
Materials Data Analysis Group
Data System Group
Data Service Team
Publishing Team
Library Team


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Administrative Management Office,
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