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JST Support program for starting up innovationhub, Information Integration, new scientific approach for materials research

Center for Materials research by Information Integration of NIMS (CMI2) breaks into an unexplored field of Information Integration known as “Data-driven” science.

CMI2 is the place where the new materials science is accelerated through integrating data science with conventional materials science. CMI2 drives forward "Materials research by Information Integration " Initiative (MI2I), which not only develops and constructs useful database but also builds up reinforced government-industry-academia collaboration in wide range of fields from materials science to information and mathematical sciences. CMI2 aims at constructing open-innovation hub, where more extensive business firms can participate in the new scientific activities.

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Specific challenges are focused into development of new materials for batteries, magnets, and thermal control materials, and the achievements will be worked toward practical use in the society. At the same time, new approaches and tools for materials science will be developed, and experienced tools will be integrated into a system of materials research tools for Information Integration. The system will support the specific challenges, and will be applied to other materials development in various fields or products.

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CMI2 will also challenge AI-based technologies. Whole activities of CMI2 can evolve discipline of the new scientific approaches, as well as can develop human resources. CMI2 tries to survive sustainably and expand its activities. 

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"Significant Increase in Power Output of Thermoelectric Materials Using Machine Learning" Image
2019.03.19 Update Press Release

Significant Increase in Power Output of Thermoelectric Materials Using Machine Learning

—New Approach to Chemical Composition Optimization May Expedite Practical Use of Thermoelectric Materials Composed of Abundant Elements—

2019.01.23 Update Press Release

Design and Validation of World-Class Multilayered Thermal Emitter Using Machine Learning

—Selection of an Optimum Structure from Approximately Eight Billion Candidates May Make Society More Energy Efficient—

2018.07.04 Update Press Release

Development of an Inorganic Composite Material with the Lowest Thermal Conductivity Using MI

—Study Sets a New Benchmark for Combining Data Science and Experimental Research to Develop Materials—


"Satoshi ITOH" Image

Satoshi ITOH

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