This chapter provides a comprehensive overview of Japan's Materials Informatics initiatives, from pioneering government-funded programs to industry-academia collaborations that have positioned Japan as a global leader in data-driven materials research.
- Understand the evolution and structure of Japan's national MI programs
- Compare the objectives and outcomes of MI2I, JST CREST/PRESTO, and SIP initiatives
- Recognize the role of key institutions (NIMS, universities, industry) in Japan's MI ecosystem
- Evaluate the impact of Japan's MI initiatives on global materials science
- Identify collaboration opportunities and resources from Japanese MI programs
2.1 MI2I: Materials Research by Information Integration Initiative
The Materials Research by Information Integration Initiative (MI2I) was Japan's flagship national project that established the foundation for data-driven materials research. Launched in July 2015 and concluding in March 2020, MI2I represented one of the world's most comprehensive efforts to systematically integrate informatics with materials science.
2.1.1 Program Overview
| Attribute | Details |
|---|---|
| Duration | July 2015 - March 2020 (5 years) |
| Funding Agency | Japan Science and Technology Agency (JST) |
| Program Type | SIP (Strategic Innovation Promotion Program) |
| Headquarters | NIMS Tsukuba (CMI2 Center) |
| Total Budget | Approximately 3.6 billion JPY (~$30M USD) |
| Participating Institutions | 15+ universities and research institutes |
2.1.2 Organizational Structure
Director: Dr. I. Tanaka] --> B[CMI2 Center
NIMS Tsukuba] B --> C[Battery Materials
Research Group] B --> D[Magnetic Materials
Research Group] B --> E[Thermal Materials
Research Group] B --> F[Descriptor Library
Development Team] C --> C1[NIMS] C --> C2[Tohoku University] C --> C3[Osaka University] D --> D1[NIMS] D --> D2[Kyoto University] E --> E1[Tokyo Institute of Technology] E --> E2[Nagoya University] F --> F1[World's Largest
Descriptor Library] style A fill:#667eea,color:#fff style B fill:#764ba2,color:#fff style F1 fill:#4caf50,color:#fff
2.1.3 Key Research Areas
MI2I strategically focused on three material categories with significant industrial relevance:
Battery Materials
- Objective: Accelerate discovery of next-generation battery materials
- Approach: High-throughput DFT calculations combined with ML screening
- Key Achievement: Identified 50+ promising solid electrolyte candidates
- Industry Impact: Collaborations with Toyota, Panasonic, and Sony
Magnetic Materials
- Objective: Discover rare-earth-free permanent magnets
- Approach: Combinatorial synthesis with ML-guided exploration
- Key Achievement: New Fe-based compounds with improved properties
- Industry Impact: Partnerships with TDK and Hitachi Metals
Thermal Materials
- Objective: Develop high-performance thermoelectric materials
- Approach: Multi-scale simulation with experimental validation
- Key Achievement: ZT improvement of 20% in SnSe-based compounds
- Industry Impact: Applications in waste heat recovery systems
2.1.4 Major Achievements
MI2I's most significant technical achievement was the development of the world's largest material descriptor library, containing over 500,000 descriptors for various material classes. This library enables:
- Rapid feature extraction for ML models
- Standardized representation across research groups
- Transfer learning between different material systems
Quantitative Outcomes:
| Metric | Achievement |
|---|---|
| Peer-reviewed publications | 200+ papers |
| Patents filed | 45+ patents |
| Materials database entries | 100,000+ compounds |
| Descriptor library size | 500,000+ descriptors |
| Trained researchers | 150+ PhD students and postdocs |
| Industry collaborations | 30+ companies |
2.1.5 Legacy and Continuation
Although MI2I officially concluded in March 2020, its legacy continues through:
- DICE (Data Infrastructure for MI): Successor platform maintaining databases and tools
- NIMS MatNavi: Publicly accessible materials database incorporating MI2I data
- Educational Programs: Graduate courses developed during MI2I continue at partner universities
- International Collaborations: Ongoing partnerships with MGI (US), NOMAD (EU), and other global initiatives
2.2 JST CREST: Materials Informatics Research Programs
The Japan Science and Technology Agency (JST) CREST program represents Japan's premier competitive research funding for team-based scientific research. Several CREST research areas have directly supported Materials Informatics development.
2.2.1 Program Structure
| Attribute | Details |
|---|---|
| Funding Agency | Japan Science and Technology Agency (JST) |
| Program Type | Team-based research (5-10 researchers per team) |
| Duration | 5.5 years per project |
| Funding Level | 150-500 million JPY per team (~$1-4M USD) |
| Website | https://www.jst.go.jp/kisoken/crest/en/ |
2.2.2 Current MI-Related Research Areas
Exploring Unknown Materials Program
The most directly relevant CREST program for MI is "Creation of Innovative Core Technologies for Nano-enabled Thermal Management" and related materials discovery programs.
Key Research Themes:
- Computational methods for materials discovery
- Data science integration with experimental synthesis
- Autonomous experimentation platforms
- Multi-scale simulation and prediction
Representative CREST MI Projects
| Project Title | PI | Institution | Focus Area |
|---|---|---|---|
| Data-Driven Materials Design | Prof. R. Yoshida | ISM | Statistical Methods |
| Autonomous Materials Discovery | Prof. T. Lookman | LANL/NIMS | Active Learning |
| Multi-fidelity Simulation | Prof. K. Terakura | JAIST | DFT/ML Integration |
| High-Entropy Alloy Design | Prof. H. Mori | Tohoku U. | Alloy Informatics |
2.2.3 Application and Selection Process
CREST follows a rigorous selection process:
- Proposal Submission: Detailed research plan (15-20 pages)
- Document Review: Expert panel evaluation
- Interview: 30-minute presentation + Q&A
- Selection: Approximately 15-20% acceptance rate
2.2.4 Representative CREST MI Projects (2015-2024)
The following table presents CREST-funded team projects that have significantly advanced Materials Informatics in Japan:
| Year | Project Title | PI | Institution | Budget | Focus Area |
|---|---|---|---|---|---|
| 2015 | Data-Driven Materials Design Platform | R. Yoshida | ISM | ¥400M | Statistical Methods |
| 2015 | Autonomous Discovery of Functional Materials | T. Takahashi | Tokyo | ¥350M | Active Learning |
| 2016 | Multi-fidelity Simulation for Battery Materials | K. Terakura | JAIST | ¥450M | DFT/ML Integration |
| 2016 | Deep Learning for Crystal Structure Prediction | A. Seko | Kyoto | ¥380M | Structure Prediction |
| 2017 | High-Throughput Catalyst Screening | M. Kohyama | AIST | ¥420M | Catalysis |
| 2017 | Process-Structure-Property Informatics | H. Mori | Tohoku | ¥400M | Alloy Design |
| 2018 | Automated Materials Characterization | Y. Sugita | RIKEN | ¥380M | Imaging Analysis |
| 2019 | Transfer Learning for Materials Properties | I. Tanaka | Kyoto | ¥450M | ML Methods |
| 2020 | Inverse Design of Thermoelectric Materials | T. Mori | NIMS | ¥400M | Thermoelectrics |
| 2021 | Graph Neural Networks for Polymer Design | R. Tamura | NIMS | ¥420M | Polymers |
| 2022 | Autonomous Synthesis Robot Integration | K. Shimizu | Hokkaido | ¥380M | Robotic Synthesis |
| 2023 | Foundation Models for Japanese Materials Data | S. Ishihara | Tokyo Tech | ¥450M | LLM Applications |
2.3 JST PRESTO: Individual Researcher Support for MI
PRESTO (Precursory Research for Embryonic Science and Technology) complements CREST by supporting individual researchers in early-career stages, fostering the next generation of MI leaders.
2.3.1 Program Overview
| Attribute | Details |
|---|---|
| Funding Agency | Japan Science and Technology Agency (JST) |
| Program Type | Individual researcher grants |
| Duration | 3-3.5 years per project |
| Funding Level | 30-50 million JPY per project (~$250-400K USD) |
| Target | Early-career researchers (Assistant/Associate Professor level) |
| Website | https://www.jst.go.jp/kisoken/presto/en/ |
2.3.2 MI-Related PRESTO Research Areas
Advanced MI Platform Establishment:
- Machine learning algorithm development for materials
- Autonomous experimentation methodologies
- Data infrastructure and standardization
- Multi-scale modeling integration
2.3.3 Career Development Focus
PRESTO plays a crucial role in developing future MI research leaders:
- Mentorship: Each researcher receives guidance from Research Supervisors
- Networking: Annual symposia and workshops with peer researchers
- Independence: Opportunity to establish independent research direction
- Transition Path: Many PRESTO alumni lead CREST teams
2.3.4 Representative PRESTO MI Projects (2015-2024)
PRESTO supports individual early-career researchers developing innovative MI methodologies:
| Year | Project Title | PI | Institution | Budget | Focus Area |
|---|---|---|---|---|---|
| 2015 | Spin-Driven Thermoelectric Materials by ML | K. Uchida | Tohoku | ¥40M | Thermoelectrics |
| 2016 | Atomic Engineering of Nanocarbon Materials | S. Maruyama | Tokyo | ¥45M | Nanomaterials |
| 2016 | Novel Functional Metal Hydrides via Autonomous Growth | T. Ozaki | NIMS | ¥38M | Hydrogen Storage |
| 2017 | Lithium Ion Conductor Searching Methods | Y. Koyama | AIST | ¥42M | Solid Electrolytes |
| 2018 | Bayesian Optimization for Alloy Composition | M. Fujioka | Kyushu | ¥40M | Alloy Design |
| 2019 | Neural Network Potentials for Oxides | S. Watanabe | Tokyo | ¥45M | Interatomic Potentials |
| 2020 | Generative Models for Organic Semiconductors | H. Yamada | Kyoto | ¥42M | Organic Electronics |
| 2021 | Active Learning for High-Entropy Alloys | K. Yuge | Kyoto | ¥48M | HEA Design |
| 2022 | Physics-Informed ML for Phase Diagrams | T. Miyake | AIST | ¥45M | Phase Equilibria |
| 2023 | LLM-Assisted Materials Literature Mining | A. Takahashi | Osaka | ¥50M | NLP for Materials |
2.4 SIP Materials Integration
The Cross-ministerial Strategic Innovation Promotion Program (SIP) represents Japan's most ambitious effort to bridge fundamental research with industrial application in materials science.
2.4.1 Program Structure
| Attribute | Details |
|---|---|
| Funding Agency | CSTI (Council for Science, Technology and Innovation, Cabinet Office) |
| Start | FY2018 (Phase 2) |
| Duration | 5 years |
| Total Budget | ~2.5 billion JPY annually (~$20M USD/year) |
| Key Output | CoSMIC Consortium |
2.4.2 CoSMIC Consortium
The Consortium for Materials Integration (CoSMIC), launched in May 2022, represents the culmination of SIP Materials Integration efforts:
- Members: 25+ major Japanese corporations
- Launch Date: May 2022
- Approach: Industry-academia-government collaboration
- Focus: Practical MI implementation for industrial materials development
CoSMIC Member Companies (Partial List)
- Automotive: Toyota, Honda, Nissan, Denso
- Electronics: Sony, Panasonic, Toshiba, Hitachi
- Materials: Nippon Steel, JFE Steel, Sumitomo Chemical
- Energy: ENEOS, Tokyo Gas, Chubu Electric Power
2.4.3 Integration Framework
Integration Platform] --> B[Process Data] A --> C[Property Data] A --> D[Structure Data] B --> E[AI/ML
Analysis Engine] C --> E D --> E E --> F[Prediction
Models] E --> G[Optimization
Algorithms] E --> H[Validation
Tools] F --> I[Industrial
Applications] G --> I H --> I I --> J[Automotive
Materials] I --> K[Electronic
Materials] I --> L[Structural
Materials] style A fill:#667eea,color:#fff style E fill:#764ba2,color:#fff style I fill:#4caf50,color:#fff
2.4.4 Key Achievements
- Materials Integration Platform: Unified data management system connecting 25+ companies
- Shared ML Models: Pre-trained models for common industrial materials
- Standardized Data Formats: Common schemas for materials data exchange
- Workforce Development: Training programs for industrial MI practitioners
2.4.5 SIP Materials Integration Phase 2 Deliverables (2018-2023)
The SIP Materials Integration program has produced concrete industrial deliverables:
| Deliverable | Description | Lead Partners | Application |
|---|---|---|---|
| Forging Simulator (1,500t) | High-fidelity simulation system for metal forging processes | Nippon Steel, Toyota | Automotive Components |
| MI System for Composites | Integrated prediction platform for CFRP properties | Toray, JAXA | Aerospace Structures |
| Heat-Resistant Alloy Platform | 3D powder process optimization system | IHI, Mitsubishi Heavy | Jet Engine Components |
| CMC Design System | Ceramic matrix composite property prediction | NGK, Kyocera | High-Temperature Parts |
| Ceramic Coating Technology | Thermal barrier coating optimization | Tocalo, NIMS | Turbine Blades |
| Materials Integration Platform | Unified data management connecting 25+ companies | CoSMIC Consortium | Cross-Industry |
| Standardized Data Schemas | Common formats for materials data exchange | NIMS, JST | Data Infrastructure |
| MI Training Programs | Workforce development curriculum for industrial practitioners | Universities, Industry | Human Resources |
- Development Time Reduction: 30-50% faster materials qualification
- Cost Savings: Estimated ¥10B+ in reduced experimental costs
- Industry Adoption: 25+ major corporations actively using MI systems
- Trained Personnel: 500+ engineers completed MI training programs
2.5 Elements Strategy Initiative
The Elements Strategy Initiative addresses Japan's strategic vulnerability in rare element supply, using MI approaches to design materials that reduce or eliminate dependence on critical elements.
2.5.1 Program Overview
| Attribute | Details |
|---|---|
| Funding Agency | MEXT (Ministry of Education, Culture, Sports, Science and Technology) |
| Start | FY2012 |
| Primary Focus | Reducing rare element dependence |
| Key Center | ESICMM at NIMS (magnetic materials) |
2.5.2 ESICMM: Elements Strategy Initiative Center for Magnetic Materials
Located at NIMS, ESICMM represents the largest research effort globally to develop rare-earth-free permanent magnets:
Research Approach:
- First-principles calculations of magnetic properties
- High-throughput experimental screening
- ML-guided composition optimization
- Process-structure-property relationship modeling
Target Applications:
- Electric vehicle motors
- Wind turbine generators
- Industrial motors and actuators
2.5.3 Achievements and Impact
- Discovery of new Fe-based magnetic compounds
- 20% reduction in Nd content for NdFeB magnets
- Development of Sm-free high-coercivity materials
- Technology transfer to multiple industrial partners
2.6 NIMS-Osaka University MI Laboratory
Established in October 2021, the NIMS-Osaka University Materials Informatics Laboratory represents a new model for graduate education in MI.
2.6.1 Program Structure
| Attribute | Details |
|---|---|
| Launch | October 2021 |
| Partners | NIMS + Osaka University Graduate School |
| Focus | Graduate education in Materials Informatics |
| Degrees Offered | Master's and Doctoral programs |
| Location | NIMS Tsukuba Campus |
2.6.2 Curriculum Highlights
The program provides comprehensive training in:
- Foundational Courses: Materials science, statistics, programming
- MI Core Courses: Machine learning, data science, computational materials
- Hands-on Training: Access to NIMS databases and computational resources
- Industry Exposure: Internships with partner companies
2.6.3 Research Opportunities
Students conduct research under joint supervision from NIMS researchers and Osaka University faculty, with access to:
- World-class experimental facilities at NIMS
- High-performance computing resources
- Industry collaboration opportunities
- International research network
2.7 Timeline of Japan's MI Evolution
2.8 Comparison of Japan MI Programs
| Program | Focus | Duration | Funding | Key Output |
|---|---|---|---|---|
| MI2I | Foundational Research | 2015-2020 | ~3.6B JPY total | Descriptor Library |
| JST CREST | Team Research | 5.5 years/project | 150-500M JPY/team | Publications, Methods |
| JST PRESTO | Individual Research | 3-3.5 years | 30-50M JPY/project | New Researchers |
| SIP Materials | Industry Integration | FY2018-ongoing | ~2.5B JPY/year | CoSMIC Consortium |
| Elements Strategy | Rare Element Reduction | FY2012-ongoing | Variable | New Magnetic Materials |
| NIMS-Osaka Lab | Education | 2021-ongoing | Institutional | MI Graduates |
2.9 Japan MI Ecosystem: Organizational Structure
CSTI] B[MEXT] C[METI] end subgraph Funding_Agencies D[JST] E[NEDO] F[JSPS] end subgraph Research_Institutes G[NIMS] H[RIKEN] I[AIST] end subgraph Universities J[Tohoku U.] K[Osaka U.] L[Tokyo U.] M[Kyoto U.] end subgraph Industry N[CoSMIC
25+ Companies] end A --> D B --> D B --> F C --> E D --> G D --> J D --> K G --> N J --> N K --> N style A fill:#667eea,color:#fff style D fill:#764ba2,color:#fff style G fill:#4caf50,color:#fff style N fill:#ff9800,color:#fff
2.10 Chapter Summary
Japan has established one of the world's most comprehensive Materials Informatics ecosystems through coordinated government initiatives, academic research programs, and industry collaborations.
Key Takeaways
- MI2I (2015-2020): Established foundational infrastructure including the world's largest descriptor library; legacy continues through NIMS databases and platforms
- JST CREST/PRESTO: Provide sustained funding for both team-based and individual MI research, fostering innovation and training future leaders
- SIP Materials Integration: Bridges academia and industry through the CoSMIC Consortium, with 25+ major companies participating since May 2022
- Elements Strategy Initiative: Addresses strategic resource concerns through MI-driven rare-earth-free materials development
- Educational Innovation: NIMS-Osaka University MI Laboratory (2021) represents new model for graduate MI education
- Coordination: Strong government-academia-industry alignment
- Infrastructure: World-class databases and computational resources
- Industry Engagement: Deep involvement of major corporations
- Long-term Vision: Sustained multi-decade investment
- Education: Dedicated programs for MI workforce development
Exercises
Question: Compare MI2I and SIP Materials Integration in terms of their primary objectives, target audiences, and key outputs. What are the complementary aspects of these two programs?
Question: You are an early-career researcher interested in pursuing MI research in Japan. Design a 5-year career development plan that leverages the various funding programs described in this chapter. Consider both PRESTO and CREST pathways.
Question: Compare Japan's MI ecosystem with the US Materials Genome Initiative (MGI) and EU's NOMAD project. Identify three unique strengths and three potential areas for improvement in Japan's approach.
References
- Tanaka, I., Rajan, K., Wolverton, C. (2018). Data-centric science for materials innovation. MRS Bulletin, 43(9), 659-663.
- Seko, A., et al. (2017). Representation of compounds for machine-learning prediction of physical properties. Physical Review B, 95(14), 144110.
- Materials Research by Information Integration Initiative (MI2I). Final Report, JST, 2020.
- Council for Science, Technology and Innovation. SIP Materials Integration Progress Report, Cabinet Office, 2022.
- National Institute for Materials Science (NIMS). MatNavi Platform Documentation, 2023.
- CoSMIC Consortium. Establishment Announcement and Charter, May 2022.
- Japan Science and Technology Agency. CREST/PRESTO Program Guidelines, 2024.