재료 정보학 시장 : 세계 예측(2026-2032년)

The Material Informatics Market is projected to grow by USD 583.82 million at a CAGR of 18.34% by 2032.

Material Informatics Executive Summary

Material informatics is reshaping how advanced materials are discovered, designed, qualified, and scaled by combining materials science, computational modeling, high-throughput experimentation, laboratory automation, and machine learning. The discipline converts fragmented experimental, simulation, processing, and performance data into actionable intelligence, enabling faster identification of materials with targeted properties such as strength, conductivity, corrosion resistance, thermal stability, recyclability, and biocompatibility. Its relevance is expanding across batteries, semiconductors, polymers, catalysts, metals, ceramics, coatings, biomaterials, and sustainable materials, where development cycles have historically depended on trial-and-error experimentation. As industries face pressure to improve product performance, reduce resource intensity, comply with evolving regulations, and secure resilient supply chains, material informatics is becoming a strategic capability rather than a purely research-focused tool. The field is also gaining momentum from wider adoption of cloud computing, materials databases, digital twins, FAIR data principles, and AI-enabled simulation workflows. These capabilities help research and engineering teams connect structure-property-processing relationships more efficiently, improve reproducibility, and accelerate the transition from laboratory discovery to industrial deployment without relying on speculative assumptions.

Transformative Shifts in the Material Informatics Landscape

The material informatics landscape is undergoing a structural shift from isolated research workflows toward integrated, data-centric innovation ecosystems. Traditional materials development often separated experimental design, simulation, characterization, process engineering, and quality validation into disconnected stages. Today, organizations are increasingly linking these functions through interoperable data architectures, automated laboratories, and closed-loop optimization systems. This shift is particularly important in sectors requiring rapid qualification of high-performance materials, including energy storage, electronics, aerospace, automotive, healthcare, construction, and specialty chemicals. Another major transformation is the movement from retrospective data analysis to predictive and prescriptive materials design, where algorithms recommend promising compositions, synthesis routes, and processing conditions before extensive physical testing begins. Sustainability is also redefining priorities, with material informatics being used to identify lower-carbon substitutes, improve recyclability, minimize critical raw material dependency, and evaluate lifecycle impacts earlier in development. At the same time, growing emphasis on data provenance, standardization, cybersecurity, and regulatory traceability is changing how organizations manage experimental records and intellectual property. The result is a more connected innovation model in which materials discovery, process optimization, and qualification become faster, more transparent, and more aligned with industrial and environmental requirements.

Cumulative Impact of Artificial Intelligence on Material Informatics

Artificial intelligence is amplifying the value of material informatics by enabling faster pattern recognition across complex, multidimensional datasets that are difficult to interpret through conventional statistical approaches alone. Machine learning models are being applied to predict material properties, screen candidate chemistries, optimize formulations, detect defects, interpret microscopy and spectroscopy data, and guide autonomous experimentation. Generative AI and inverse design approaches are supporting the search for materials that meet predefined performance constraints, while natural language processing is helping extract structured knowledge from scientific literature, patents, laboratory notebooks, and technical reports. The cumulative impact is a measurable reduction in unnecessary experimental iterations, better use of historical data, and stronger links between computational predictions and physical validation. However, AI adoption in material informatics also depends on data quality, domain expertise, model interpretability, and governance. Materials datasets often remain sparse, biased toward successful experiments, or inconsistent across instruments and laboratories, making curation and metadata standards essential. Physics-informed machine learning, uncertainty quantification, and hybrid modeling approaches are therefore becoming critical to ensure that AI recommendations remain scientifically credible, reproducible, and actionable for industrial use.

Key Regional Insights for Material Informatics

Asia-Pacific is a major center for material informatics adoption because of its dense manufacturing base, strong electronics and battery ecosystems, and sustained public investment in advanced materials, clean energy, and semiconductor capabilities. The region's research institutions and industrial laboratories are increasingly applying data-driven materials discovery to energy storage, photovoltaics, display materials, specialty polymers, catalysts, and high-performance alloys. Europe has a strong policy and research foundation in sustainable materials, circular economy practices, battery value chains, hydrogen technologies, and chemicals regulation, making data traceability and lifecycle-informed material selection especially important. North America is characterized by strong integration of computational materials science, national laboratory infrastructure, university research, and industrial digital transformation programs, with emphasis on advanced manufacturing, defense technology, electrification, biomedical materials, and semiconductor resilience. Latin America is building relevance through mining, bio-based materials, energy transition applications, and sustainable construction materials, where material informatics can improve resource efficiency and value-added processing. Africa's opportunity is linked to mineral resources, renewable energy deployment, construction materials, and academic capacity building, with material informatics offering pathways to improve local value creation, responsible resource use, and materials performance in climate-specific operating conditions. The Middle East is increasingly exploring material informatics in petrochemicals, desalination, construction materials, carbon management, hydrogen, and solar energy applications as part of economic diversification and industrial modernization.

Key Group Insights Across Strategic Economic and Policy Blocs

NATO-aligned priorities create relevance for materials with defense, resilience, and security applications, including lightweight alloys, thermal protection systems, protective coatings, energetic materials, sensors, additive manufacturing feedstocks, and materials designed for reliability under extreme operating conditions. G7 countries are using material informatics to strengthen critical material resilience, advanced manufacturing, semiconductor supply chains, clean energy technologies, and high-value industrial innovation, supported by mature research systems and standards-oriented qualification practices. BRICS economies bring together large manufacturing bases, resource endowments, and expanding scientific capabilities, making material informatics relevant for energy storage, infrastructure, chemicals, aerospace, agriculture-related materials, and domestic technology development. The European Union benefits from coordinated research programs, sustainability regulation, battery initiatives, circular economy frameworks, and digital product data requirements, encouraging informatics platforms that connect composition, processing history, performance, and environmental impact. ASEAN's material informatics opportunity is closely tied to electronics manufacturing, automotive supply chains, packaging, renewable energy, and specialty chemicals, with data-driven materials workflows supporting quality improvement, process efficiency, and product localization. The GCC is aligning material informatics with diversification priorities in petrochemicals, advanced polymers, carbon capture materials, solar technologies, hydrogen infrastructure, and durable construction materials suited to extreme heat and corrosive environments. Across these groups, the common drivers are faster discovery cycles, trusted materials data, supply chain resilience, sustainability, and the need to bridge fundamental research with scalable industrial deployment.

Key Country Insights for Material Informatics Adoption

China is a prominent force in material informatics due to its scale in manufacturing, batteries, electronics, solar technologies, rare earth processing, and AI-enabled research infrastructure. The United States shows strong momentum through advanced manufacturing initiatives, national laboratory capabilities, battery and semiconductor programs, and cross-disciplinary research in AI for science. Japan has mature capabilities in electronic materials, batteries, ceramics, polymers, catalysts, and precision manufacturing, with strong emphasis on reliability and process control. India's growing focus on semiconductors, energy storage, green hydrogen, specialty chemicals, space technology, and domestic manufacturing strengthens the case for data-driven materials innovation. Germany's industrial engineering base supports material informatics in automotive, machinery, chemicals, additive manufacturing, battery systems, and high-performance materials qualification. The United Kingdom has notable capabilities in computational materials science, aerospace materials, pharmaceuticals-related materials, batteries, and digital research infrastructure. Australia's strengths in mining, critical minerals, hydrogen, solar materials, and university-led materials research create opportunities for informatics across extraction, processing, and clean energy applications. France combines strengths in aerospace, nuclear materials, specialty chemicals, energy technologies, and public research, creating demand for validated and traceable materials data. South Korea's leadership in semiconductors, displays, batteries, electronics, and advanced manufacturing makes material informatics highly relevant for process optimization, defect reduction, and next-generation material development. Italy's relevance is visible in advanced manufacturing, ceramics, polymers, coatings, biomedical materials, and design-driven industrial applications. Canada's strengths include clean energy materials, mining-related innovation, advanced alloys, polymers, and university-led computational materials research, supporting applications in resource processing and electrification. Russia's materials expertise is linked to metallurgy, nuclear technology, aerospace, energy, and defense-related applications, where modeling and simulation support high-performance requirements. Brazil is well positioned in bio-based materials, mining, energy, catalysts, and sustainable construction applications, where data-driven approaches can improve material performance and resource efficiency. Mexico's manufacturing base in automotive, electronics, and industrial goods creates opportunities for informatics-driven materials quality control, coatings, lightweight materials, and supply chain localization. Spain is advancing materials research in renewable energy, construction, nanomaterials, batteries, and circular materials, supported by European research collaboration.

Actionable Recommendations for Industry Leaders

Industry leaders should treat material informatics as an enterprise capability that connects research, engineering, manufacturing, quality, procurement, and sustainability functions. A practical first step is to establish a governed materials data infrastructure that captures experimental conditions, processing parameters, characterization results, simulation outputs, failure data, and lifecycle attributes using standardized metadata. Organizations should prioritize high-value use cases where data-driven optimization can reduce development complexity, such as battery materials, recyclable polymers, corrosion-resistant alloys, catalyst design, additive manufacturing feedstocks, electronic materials, and sustainable substitutes for critical inputs. Cross-functional teams should combine materials scientists, data engineers, AI specialists, process engineers, and domain experts to ensure that models are scientifically valid and operationally useful. Leaders should also invest in automated experimentation, digital lab notebooks, model validation protocols, and uncertainty quantification to improve reproducibility and confidence. Partnerships with universities, public research institutions, standards bodies, and supply chain partners can strengthen access to curated data and accelerate qualification. Finally, organizations should embed responsible AI practices, intellectual property safeguards, cybersecurity controls, and sustainability metrics into material informatics programs to ensure that faster discovery also supports compliance, resilience, and long-term value creation.

Research Methodology

This executive summary is developed using a structured research methodology centered on verified secondary research, domain-specific analysis, and triangulation of publicly available technical, regulatory, and institutional information. The research approach considers peer-reviewed scientific literature, public research program documentation, standards and policy references, patent and publication trends, industrial application evidence, and technology adoption indicators across advanced materials domains. Qualitative assessment is used to identify the role of material informatics in discovery, simulation, experimentation, manufacturing, and sustainability workflows, while regional and country insights are evaluated through the presence of research infrastructure, industrial capabilities, policy priorities, and application relevance. The methodology avoids unsupported market sizing, market share claims, and speculative forecasts, focusing instead on evidence-backed drivers, adoption patterns, technical enablers, barriers, and strategic implications. Particular attention is given to data governance, AI model reliability, materials qualification, lifecycle considerations, and sector-specific use cases. Insights are synthesized to provide an executive-level view that is useful for decision-makers in research, product development, operations, strategy, and innovation management.

Conclusion

Material informatics is becoming a critical enabler of faster, more reliable, and more sustainable materials innovation. By integrating AI, computational modeling, curated databases, automation, and domain expertise, the field helps organizations identify promising materials, optimize processing conditions, reduce experimental waste, and improve the traceability of materials decisions. Its impact is especially visible in energy storage, semiconductors, advanced manufacturing, polymers, catalysts, coatings, metals, ceramics, and sustainable material substitution. Regional adoption patterns differ, but the strategic direction is consistent: industries and governments are seeking shorter innovation cycles, stronger supply chain resilience, better materials performance, and improved environmental outcomes. The next stage of progress will depend on high-quality data, interoperable platforms, scientifically interpretable AI, robust validation practices, and closer collaboration between research institutions and industrial users. Organizations that build disciplined material informatics capabilities today will be better positioned to respond to technical complexity, regulatory pressure, resource constraints, and the rising demand for high-performance sustainable materials.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Definition
• 1.3. Market Segmentation & Coverage
• 1.4. Years Considered for the Study
• 1.5. Currency Considered for the Study
• 1.6. Language Considered for the Study
• 1.7. Key Stakeholders

2. Research Methodology

• 2.1. Introduction
• 2.2. Research Design
  • 2.2.1. Primary Research
  • 2.2.2. Secondary Research
• 2.3. Research Framework
  • 2.3.1. Qualitative Analysis
  • 2.3.2. Quantitative Analysis
• 2.4. Market Size Estimation
  • 2.4.1. Top-Down Approach
  • 2.4.2. Bottom-Up Approach
• 2.5. Data Triangulation
• 2.6. Research Outcomes
• 2.7. Research Assumptions
• 2.8. Research Limitations

3. Executive Summary

• 3.1. Introduction
• 3.2. CXO Perspective
• 3.3. Market Size & Growth Trends
• 3.4. New Revenue Opportunities
• 3.5. Next-Generation Business Models
• 3.6. Industry Roadmap

4. Market Overview

• 4.1. Introduction
• 4.2. Industry Ecosystem & Value Chain Analysis
  • 4.2.1. Supply-Side Analysis
  • 4.2.2. Demand-Side Analysis
  • 4.2.3. Stakeholder Analysis
• 4.3. Market Dynamics
  • 4.3.1. Key Drivers
  • 4.3.2. Key Restraints
  • 4.3.3. Key Opportunities
  • 4.3.4. Key Challenges
• 4.4. Porter's Five Forces Analysis
• 4.5. PESTLE Analysis
• 4.6. Market Outlook
  • 4.6.1. Near-Term Market Outlook (0-2 Years)
  • 4.6.2. Medium-Term Market Outlook (3-5 Years)
  • 4.6.3. Long-Term Market Outlook (5-10 Years)
• 4.7. Go-to-Market Strategy

5. Market Insights

• 5.1. Consumer Insights & End-User Perspective
• 5.2. Consumer Experience Benchmarking
• 5.3. Opportunity Mapping
• 5.4. Distribution Channel Analysis
• 5.5. Pricing Trend Analysis
• 5.6. Regulatory Compliance & Standards Framework
• 5.7. ESG & Sustainability Analysis
• 5.8. Disruption & Risk Scenarios
• 5.9. Return on Investment & Cost-Benefit Analysis

6. Cumulative Impact of Artificial Intelligence 2026

7. Material Informatics Market, by Component

• 7.1. Introduction
• 7.2. Analytical Instruments
  • 7.2.1. Microscopy Tools
    • 7.2.1.1. Atomic Force Microscopy
    • 7.2.1.2. Electron Microscopy
  • 7.2.2. Spectroscopy Devices
    • 7.2.2.1. Infrared Spectroscopy
    • 7.2.2.2. Ultraviolet-Visible Spectroscopy
• 7.3. Services
  • 7.3.1. Consulting & Implementation
  • 7.3.2. Data Curation & Annotation
  • 7.3.3. Support & Maintenance
• 7.4. Software
  • 7.4.1. Computational Platforms
  • 7.4.2. Data Analytics & Visualization Tools
  • 7.4.3. Material Discovery Platforms
  • 7.4.4. Simulation & Modeling Software

8. Material Informatics Market, by Material Type

• 8.1. Introduction
• 8.2. Biomaterials
  • 8.2.1. Biodegradable Biomaterials
  • 8.2.2. Bioinspired Materials
  • 8.2.3. Implantable Biomaterials
• 8.3. Catalysts
  • 8.3.1. Enzymatic Catalysts
  • 8.3.2. Heterogeneous Catalysts
  • 8.3.3. Homogeneous Catalysts
• 8.4. Ceramics & Glass
  • 8.4.1. Functional Ceramics
  • 8.4.2. Glass
  • 8.4.3. Structural Ceramics
• 8.5. Coatings & Surface Treatments
  • 8.5.1. Anti-Corrosion Coatings
  • 8.5.2. Anti-Fouling Coatings
  • 8.5.3. Functional Coatings
  • 8.5.4. Hard & Wear-Resistant Coatings
• 8.6. Composites
  • 8.6.1. Ceramic Matrix Composites
  • 8.6.2. Metal Matrix Composites
  • 8.6.3. Natural-Fiber Composites
  • 8.6.4. Polymer Matrix Composites
• 8.7. Metals & Alloys
  • 8.7.1. Ferrous Alloys
  • 8.7.2. High-Entropy Alloys
  • 8.7.3. Non-Ferrous Alloys
• 8.8. Nanomaterials
  • 8.8.1. MOFs & COFs
  • 8.8.2. MXenes
  • 8.8.3. Nanoparticles
  • 8.8.4. Nanotubes & Nanowires
• 8.9. Polymers
  • 8.9.1. Elastomers
  • 8.9.2. Thermoplastics
    • 8.9.2.1. Commodity Thermoplastics
    • 8.9.2.2. Engineering Thermoplastics
    • 8.9.2.3. High-Performance Thermoplastics
  • 8.9.3. Thermosets
• 8.10. Semiconductor
  • 8.10.1. Compound Semiconductors
  • 8.10.2. Elemental Semiconductors
• 8.11. Textiles & Fibers
  • 8.11.1. Natural Fibers
  • 8.11.2. Synthetic Fibers
  • 8.11.3. Technical Textiles

9. Material Informatics Market, by Technology

• 9.1. Introduction
• 9.2. Automation & Robotics
  • 9.2.1. High-Throughput Experimentation
  • 9.2.2. Robotic Synthesis
  • 9.2.3. Self-Driving Labs
• 9.3. Data Infrastructure
  • 9.3.1. Data Lakes & Warehouses
  • 9.3.2. Feature Stores
  • 9.3.3. Knowledge Graphs
• 9.4. Machine Learning & AI
  • 9.4.1. Active Learning & Bayesian Optimization
  • 9.4.2. Deep Learning
    • 9.4.2.1. Convolutional Neural Networks
    • 9.4.2.2. Graph Neural Networks
    • 9.4.2.3. Transformers & RNNs
  • 9.4.3. Generative Models
    • 9.4.3.1. Diffusion Models
    • 9.4.3.2. GANs
    • 9.4.3.3. VAEs
  • 9.4.4. Physics-Informed ML
  • 9.4.5. Reinforcement Learning
  • 9.4.6. Transfer Learning & Multi-Task Learning
• 9.5. Security & Governance
  • 9.5.1. Access Control
  • 9.5.2. Audit Trails
  • 9.5.3. Model Governance
• 9.6. Simulation & Computational Methods
  • 9.6.1. CALPHAD
  • 9.6.2. DFT & Ab Initio
  • 9.6.3. Finite Element Analysis
  • 9.6.4. Molecular Dynamics
  • 9.6.5. Phase-Field Modeling
• 9.7. Visualization & Decision Support
  • 9.7.1. Uncertainty Quantification
  • 9.7.2. Visualization Dashboards
  • 9.7.3. What-If Analysis

10. Material Informatics Market, by Data Source

• 10.1. Introduction
• 10.2. Computational Data
  • 10.2.1. DFT Databases
  • 10.2.2. Molecular Dynamics Trajectories
  • 10.2.3. Phase Diagrams
• 10.3. Experimental Data
  • 10.3.1. High-Throughput Screening
  • 10.3.2. Instrument Data
    • 10.3.2.1. Diffraction & Scattering
    • 10.3.2.2. Mechanical Testing
    • 10.3.2.3. Microscopy
    • 10.3.2.4. Spectroscopy
    • 10.3.2.5. Thermal Analysis
  • 10.3.3. LIMS & ELN
• 10.4. Proprietary & Supplier Data
• 10.5. Public Databases
  • 10.5.1. ChEMBL
  • 10.5.2. Materials Project
  • 10.5.3. NOMAD
  • 10.5.4. OQMD
  • 10.5.5. PubChem
• 10.6. Real-World Performance Data
  • 10.6.1. Field Sensors
  • 10.6.2. Warranty & Failure Logs
• 10.7. Textual & Unstructured Data
  • 10.7.1. Lab Notebooks
  • 10.7.2. Patents
  • 10.7.3. Publications
  • 10.7.4. Technical Reports

11. Material Informatics Market, by Analytics Type

• 11.1. Introduction
• 11.2. Descriptive
• 11.3. Diagnostic
• 11.4. Generative
• 11.5. Predictive
• 11.6. Prescriptive

12. Material Informatics Market, by Application

• 12.1. Introduction
• 12.2. Formulation Design
  • 12.2.1. Additives Optimization
  • 12.2.2. Multicomponent Blends
  • 12.2.3. Rheology Control
• 12.3. Knowledge Management & IP Analytics
  • 12.3.1. Knowledge Graphs
  • 12.3.2. Literature Insights
  • 12.3.3. Patent Mining
• 12.4. Lab Automation & Experiment Planning
  • 12.4.1. Autonomous Labs
  • 12.4.2. Closed-Loop Optimization
  • 12.4.3. Robotic Execution
• 12.5. Materials Discovery
  • 12.5.1. Generative Design
  • 12.5.2. Inverse Design
  • 12.5.3. Property Prediction
• 12.6. Process Development & Scale-Up
  • 12.6.1. Design of Experiments & Active Learning
  • 12.6.2. Digital Twin
  • 12.6.3. Process Parameter Optimization
• 12.7. Quality Control & Failure Analysis
  • 12.7.1. Anomaly Detection
  • 12.7.2. Predictive Quality
  • 12.7.3. Root-Cause Analysis
• 12.8. Supply Chain & Sourcing
  • 12.8.1. Compliance Screening
  • 12.8.2. Raw Material Substitution
  • 12.8.3. Supplier Risk Assessment
• 12.9. Sustainability & Circularity
  • 12.9.1. Lifecycle Assessment
  • 12.9.2. Recyclability & Circularity Modeling
  • 12.9.3. Toxicity & HSE

13. Material Informatics Market, by End-User Industry

• 13.1. Introduction
• 13.2. Academia & Research Institutes
• 13.3. Aerospace & Defense
• 13.4. Automotive
  • 13.4.1. High-Temperature Alloys
  • 13.4.2. Lightweight Composites
  • 13.4.3. Surface Treatments
• 13.5. Chemicals
  • 13.5.1. Adhesives & Sealants
  • 13.5.2. Agrochemicals
  • 13.5.3. Commodity Chemicals
  • 13.5.4. Paints & Coatings
  • 13.5.5. Petrochemicals
  • 13.5.6. Specialty Chemicals
• 13.6. Construction & Building Materials
  • 13.6.1. Cement & Concrete
  • 13.6.2. Insulation Materials
  • 13.6.3. Smart Glass & Glazing
• 13.7. Consumer Goods & Packaging
  • 13.7.1. Food-Contact Materials
  • 13.7.2. Sustainable Packaging
  • 13.7.3. Textiles & Apparel
• 13.8. Electronics
  • 13.8.1. Display Materials
  • 13.8.2. Integrated Circuit Materials
  • 13.8.3. Photonics & Optoelectronics
• 13.9. Energy & Utilities
  • 13.9.1. Batteries & Energy Storage
  • 13.9.2. Hydrogen & Fuel Cells
  • 13.9.3. Nuclear
  • 13.9.4. Oil & Gas
  • 13.9.5. Renewables
• 13.10. Healthcare & Medical Devices
  • 13.10.1. Diagnostics & Wearables
  • 13.10.2. Implants & Prosthetics
• 13.11. Mining & Metals
• 13.12. Pharmaceuticals & Life Sciences
  • 13.12.1. Advanced Therapies
  • 13.12.2. Biologics
  • 13.12.3. Drug Delivery & Excipients
  • 13.12.4. Small Molecules

14. Material Informatics Market, by Organization Size

• 14.1. Introduction
• 14.2. Large Enterprises
• 14.3. Small & Medium Enterprises

15. Material Informatics Market, by Region

• 15.1. Asia-Pacific
• 15.2. Europe
• 15.3. North America
• 15.4. Latin America
• 15.5. Africa
• 15.6. Middle East

16. Material Informatics Market, by Group

• 16.1. NATO
• 16.2. G7
• 16.3. BRICS
• 16.4. European Union
• 16.5. ASEAN
• 16.6. GCC

17. Material Informatics Market, by Country

• 17.1. China
• 17.2. United States
• 17.3. Japan
• 17.4. India
• 17.5. Germany
• 17.6. United Kingdom
• 17.7. Australia
• 17.8. France
• 17.9. South Korea
• 17.10. Italy
• 17.11. Canada
• 17.12. Russia
• 17.13. Brazil
• 17.14. Mexico
• 17.15. Spain

18. Competitive Landscape

• 18.1. Market Share Analysis, 2025
• 18.2. FPNV Positioning Matrix, 2025
• 18.3. Market Concentration Analysis, 2025
  • 18.3.1. Concentration Ratio (CR)
  • 18.3.2. Herfindahl Hirschman Index (HHI)
• 18.4. Recent Developments & Impact Analysis, 2025
• 18.5. Product Portfolio Analysis, 2025
• 18.6. Benchmarking Analysis, 2025

19. Company Profiles

• 19.1. Alchemy Cloud, Inc.
• 19.2. BASF SE
• 19.3. Citrine Informatics
• 19.4. Dassault Systemes SE
• 19.5. DeepMaterials, Inc.
• 19.6. Dow, Inc.
• 19.7. Elix, Inc.
• 19.8. ENEOS Corporation
• 19.9. Exabyte Inc.
• 19.10. ExoMatter GmbH
• 19.11. Exponential Technologies Ltd.
• 19.12. Hexagon AB
• 19.13. Hitachi, ltd.
• 19.14. Innophore GmbH
• 19.15. Intellegens Limited
• 19.16. Kebotix, Inc.
• 19.17. Materials Design, Inc.
• 19.18. Materials.Zone Technologies Ltd.
• 19.19. Noble Artificial Intelligence, Inc.
• 19.20. OntoChem GmbH by DS Digital Science GmbH
• 19.21. Optibrium Ltd.
• 19.22. Phaseshift Technologies Inc.
• 19.23. Polymerize Private Limited
• 19.24. Preferred Networks, Inc.
• 19.25. QuesTek Innovations LLC
• 19.26. Revvity Signals Software, Inc.
• 19.27. Schrodinger, Inc.
• 19.28. Simreka
• 19.29. Synopsys, Inc.
• 19.30. TDK Corporation
• 19.31. Thermo Fisher Scientific, Inc.
• 19.32. Tilde Materials Informatics
• 19.33. Uncountable Inc.