Material DX (Digital Transformation in Materials Science) 2026

Language:
Japanese
Product Code No:
C67121000
Issued In:
2026/01
#of Pages:
152
Publication Cycle:
  
Format:
PDF

Price

180,000 yen ($1,132.08)
(excluding consumption tax)
360,000 yen ($2,264.15)
(excluding consumption tax)
540,000 yen ($3,396.23)
(excluding consumption tax)
* Equivalent value in US$ (Today's rate : $1= 159 yen , 2026/08/21 Japan)
*Scope of Each License Type

Coverage: (Product/service)

Digital Transformation in Materials Science

Research Target:

Companies and research Institutions studying digital transformation for materials science

Research Content:

Digital Transformation in Materials Science 2026 (January 2026, Yano Research Institute; 152 pages) examines the market for digital transformation in materials science (referred to as “Material DX” in Japanese), a sector projected to surpass three trillion yen by 2030. The report offers key market intelligence, detailing vendor dynamics, current market metrics, and long-range forecasts through 2050. 

As supercomputing and cloud power advance, computational science and computer simulations are becoming increasingly practical. This enables precise modeling of material properties and drastically reduces development cycles. Looking ahead, multi-scale, multi-modal approaches that combine physical experiments, simulation, and AI analysis will drive rapid innovation across the chemical and manufacturing industries.

This report is recommended for: 

  • Strategic Leaders & Analysts: Professionals seeking to synthesize broader market dynamics, digital transformation (DX) trends in material science, and competitive intelligence into a cohesive market outlook.
  • Product Managers & Business Developers: Those responsible for defining target metrics and establishing data-driven frameworks for new product launches or developmental phases.
  • DX Specialists & Innovators: Individuals looking for actionable insights into the product landscape and a deeper understanding of competitor positioning and emerging industry shifts regarding DX in material science

 

Q: What years' performance data and forecasts are included?
A: Actual figures for 2025 and market size forecasts from 2030 to 2050.

Q: What are the key topics/keywords discussed in the report?
A: While traditional Materials Informatics (MI) primarily focused on the relationship between material composition and physical properties, PI aims to elucidate the correlation between manufacturing process parameters and final product characteristics. Digital Transformation (DX) in material science has entered a new phase, advancing the development of digital twin of entire manufacturing processes. By modeling the complex interactions between process variables and material properties using AI, technologies enable rapid identification of optimal manufacturing conditions.

Q: Can I find market player trends in this report?
A: You can explore the business trends of companies and research institutions advancing initiatives toward commercializing DX-related technologies in material science segment.

TOC:

I   Process Informatics

1 Technical Foundation of Process Informatics
2 New Phase in Digital Transformation in Materials Science (Material DX) and Digital Twin Technology
3 Material Development Platform Promoted by MEXT
3.1 Current Situations and Challenges in Material Development
3.2 Government Strategy for Enhancing Material Innovation Capabilities
3.3 Framework for Innovative Material Development Promoted by MEXT
3.3.1 Advanced Research Infrastructure for Materials and Nanotechnology in Japan (ARIM)
3.3.2 Materials Data Platform (MDPF) 
3.3.3 Data-creation and Application-oriented Materials Research and Development Project (DxMT)
3.4 Future Outlook
4 Market Size for Process Informatics (PI) in Material DX
5 Market Player Trends Regarding PI in Material DX
5.1 AIxtal
5.2 University of Tokyo
5.3 Hitachi, Ltd.
5.4 National Institute for Materials Science (NIMS)
6 Challenges and Perspectives for PI in Material DX
 

II   Computational Science and Simulation Technology

1 Computational Science and Simulation Technology are Core Technologies in Material DX
2 Methodologies 
2.1 Microscale: First-Principles Calculations & Quantum Chemistry Calculations
2.2 Mesoscale: Molecular Dynamics & Monte Carlo Methods
2.3 Macroscale: Finite Element Method & Phase-Field Method
2.4 Optimization and Search: Bayesian Optimization & Inverse Design
2.5 Integrated Approach: Multiscale Modeling & Data-Driven Design
3 Forecast of Market Size for Computational Science and Simulation Technology in Material DX
4 Market Player Trends Regarding Computational Science and Simulation Technology in Material DX
4.1 MI-6 Ltd.
4.2 Foundation for Computational Science (FOCUS)
4.3 Japan Atomic Energy Agency (JAEA)
4.4 Waseda University
5 Challenges and Perspectives for Computational Science and Simulation Technology in Material DX
 

III   Material Development Through AI/Machine Learning

1 Overview of Material Development through AI/Machine Learning in Material DX
2 Details 
2.1 Machine Learning
2.2 Neural Network
2.3 Deep Learning
2.4 Other Essential Methodologies
3 Applications for AI/Machine Learning in Material Development
3.1 Acceleration in Search for New Materials
3.2 Optimization of Catalyst Materials
3.3 Development of High-Performance Polymers
3.4 Optimization of Material Processing Conditions
4 Marke Size for Data Science in Material DX
5 Market Player Trends Regarding Data Science in Material DX
5.1 National Institute of Advanced Industrial Science and Technology （AIST）
5.2 Datachemical, Inc. 
5.3 Nara Institute of Science and Technology (NAIST)
5.4 National Institute for Materials Science (NIMS)
5.5 Mitsui Chemical 
6 Challenges and Perspectives for Data Science in Material DX
 

IV   Materials Informatics for Organic Materials

1 Overview of Material DX for Organic Materials
2 Material Development Policies in Material DX for Organic Materials
2.1 Rational Exploration of the Molecular Design Space
2.2 Advancing Performance Prediction Models
2.3 Utilizing the Inverse Design Approach
2.4 Integration with Process Design
3 Key Applications 
3.1 Electronics
3.2 Energy
3.3 Bio/Healthcare
3.4 Environment/Sustainability
3.5 Advanced Functional Materials
4 Market Size Forecast
5 Market Player Trends
5.1 Keio University 
5.2 Sekisui Chemical 
5.3 Toray Industries
5.4 University of Hyogo
5.5 Miyazaki University 
6 Challenges and Future Perspectives
 

V   Materials Informatics for Inorganic Materials

1 Overview of Material DX for Inorganic Materials
2 Material Development Policies in Material DX for Inorganic Materials
2.1 Defining Design Space and Narrowing Down
2.2 Predicting Material Properties Using Computational Science
2.3 Efficient Search and Optimization Using Machine Learning
2.4 Feedback Loop with Experiments
3 Key Applications 
3.1 Energy
3.2 Construction Materials/Heat-resistant Materials
3.3 Electronics/Semiconductors
3.4 Environmental Materials
3.5 Medicine/Bio
4 Market Size Forecast
5 Marekt Player Trends
5.1 Osaka Metropolitan University
5.2 Kwansei Gakuin University
5.3 Kyoto University
5.4 Japan Advanced Institute of Science and Technology
6 Challenges and Future Perspectives
7 Summary and Future Outlook for Material DX

Price

180,000 yen ($1,132.08)
(excluding consumption tax)
360,000 yen ($2,264.15)
(excluding consumption tax)
540,000 yen ($3,396.23)
(excluding consumption tax)
* Equivalent value in US$ (Today's rate : $1= 159 yen , 2026/08/21 Japan)
*Scope of Each License Type