Global Materials Informatics Market Size, Share, Trends, And Forecast Analysis - 2026 To 2036 Generative AI And Self-Driving Labs Drive 20.5% CAGR And A USD 1.61 Billion Opportunity

(MENAFN- GlobeNewsWire - Nasdaq) Boost ROI by using AI-driven modeling, virtual testing and intelligent screening to cut R&D costs, shorten development cycles and accelerate materials commercialization.

Dublin, Nov. 06, 2026 (GLOBE NEWSWIRE) -- "Materials Informatics Market Size, Share, Trends & Forecast Analysis - Global Forecast to 2036" has been added to ResearchAndMarkets.com's offering.

The global materials informatics market is estimated at USD 0.25 billion in 2026 and is projected to reach USD 1.61 billion by 2036, expanding at a CAGR of 20.5% during the forecast period. The report examines market trends, technological innovation, competitive developments, regional performance, and growth opportunities across the materials research and product development value chain.

Artificial intelligence, machine learning, high-throughput computing, and advanced data analytics are accelerating materials discovery, design, testing, and commercialization. Organizations in chemicals, pharmaceuticals, electronics, energy, automotive, aerospace, and advanced manufacturing are adopting materials informatics platforms to shorten research timelines, reduce development costs, improve product performance, and support faster commercialization.

Materials Informatics Market Drivers and Challenges

Demand for accelerated materials discovery and shorter product development cycles is a major market driver. Companies face increasing pressure to create high-performance materials that satisfy requirements for sustainability, durability, efficiency, safety, and regulatory compliance. Materials informatics platforms support predictive modeling, virtual experimentation, intelligent screening, formulation optimization, and data-driven decision-making while reducing dependence on costly trial-and-error research.

Continued investment in digital research and development, Industry 4.0 programs, cloud infrastructure, and artificial intelligence is strengthening market expansion. Machine learning algorithms, deep learning models, generative AI, digital twins, high-throughput computational methods, and cloud-based materials databases are being integrated into scientific workflows to improve prediction accuracy and research productivity.

These capabilities are supporting the discovery of novel compounds and next-generation materials for batteries, semiconductors, pharmaceuticals, polymers, specialty chemicals, and other advanced applications. Collaboration among technology companies, research institutions, academic organizations, and industrial manufacturers is also helping move materials informatics technologies from research environments into commercial operations.

Market adoption remains affected by limited access to standardized, high-quality datasets and the complexity of integrating heterogeneous experimental and computational data. Data security, intellectual property protection, implementation costs, and shortages of professionals with expertise in both materials science and artificial intelligence present additional challenges, particularly for small and medium-sized enterprises.

Segment Analysis

The report analyzes the materials informatics market by offering, technology, material type, application, end user, and geography. The offering segment includes software and services. Software accounts for the largest share, supported by growing deployment of AI-powered discovery platforms, simulation software, predictive analytics tools, data management solutions, and cloud-based research environments.

Demand for consulting, implementation, integration, model development, technical support, and training services is also rising. Organizations increasingly require specialist expertise to connect materials informatics platforms with existing research and development systems and workflows.

Technologies evaluated include machine learning, deep learning, generative AI, high-throughput computing, and other emerging solutions. Machine learning represents a significant share of adoption because of its use in property prediction, materials screening, and formulation optimization. Generative AI is expected to record the fastest growth as companies adopt AI-driven molecular design, automated hypothesis generation, and intelligent research tools.

Material categories covered include polymers, chemicals, metals & alloys, semiconductors, pharmaceuticals, energy materials, and other advanced materials. Key applications include material discovery, product development, manufacturing optimization, quality control, and related research activities.

The chemical industry is a major contributor to market revenue due to sustained investment in advanced materials research and digital innovation. Pharmaceutical, electronics, automotive, energy, aerospace & defense, and other industrial sectors are expected to increase adoption as AI-enabled platforms improve research efficiency and accelerate product commercialization.

Regional Materials Informatics Market Outlook

North America holds the largest share of the global materials informatics market. Regional growth is supported by advanced research infrastructure, substantial artificial intelligence investment, established software developers, strong manufacturing capabilities, and extensive academic-industry collaboration.

Europe is demonstrating robust growth through sustainable materials research, digital transformation programs, collaborative innovation initiatives, and broader use of computational materials engineering. Asia-Pacific is expected to register the fastest growth during the forecast period, driven by industrialization, expanding research and development spending, government support for advanced manufacturing and semiconductor production, and rising artificial intelligence adoption in China, Japan, South Korea, and India.

Latin America and the Middle East & Africa offer emerging opportunities as research capabilities, industrial investment, and digital innovation initiatives expand.

Competitive Landscape

The competitive assessment examines product portfolios, artificial intelligence capabilities, computational modeling expertise, materials databases, partnerships, acquisitions, research collaborations, geographic expansion, and commercialization strategies. Leading companies are investing in machine learning, cloud computing, and advanced analytics to strengthen digital research offerings and support next-generation materials innovation.

Companies profiled include Citrine Informatics, Exabyte Inc., Kebotix, MaterialsZone Ltd., Phaseshift Technologies Inc., Schrodinger, Inc., Dassault Systemes SE, ANSYS, Inc., Thermo Fisher Scientific Inc., Intellegens Ltd., and other emerging participants.

Strategic Value of the Report

• Provides market size estimates and long-term forecasts through 2036.
• Assesses artificial intelligence and computational technology adoption in materials research.
• Identifies high-growth opportunities by offering, technology, material type, application, end user, and region.
• Evaluates market drivers, restraints, challenges, investment trends, and commercialization opportunities.
• Benchmarks leading companies based on technology capabilities, portfolios, partnerships, and competitive positioning.
• Supports market entry, product development, investment planning, partnership evaluation, and business expansion.

Growing investment in sustainable materials, advanced batteries, semiconductor research, digital laboratories, and AI-enabled product development is expected to create substantial long-term opportunities. As data-driven research becomes central to industrial innovation, materials informatics will play an increasingly important role in improving productivity, optimizing manufacturing, and accelerating the development of commercially viable advanced materials.

Key Topics Covered:
1. Introduction
1.1. Market Definition
1.2. Currency & Limitations
2. Research Methodology
2.1. Research Approach
2.2. Data Collection & Validation
2.2.1. Secondary Research
2.2.2. Primary Research
2.3. Market Assessment
2.3.1. Market Size Estimation
2.3.2. Bottom-Up Approach
2.3.3. Top-Down Approach
2.3.4. Growth Forecast
2.4. Assumptions for the Study
3. Executive Summary
4. Market Insights
4.1. Overview
4.2. Factors Affecting Market Growth
4.2.1. Drivers
4.2.1.1. Growing Need to Accelerate and Reduce the Cost of Materials Research and Development
4.2.1.2. Rising Demand for Advanced and Sustainable Materials Across Batteries, Semiconductors, and Chemicals
4.2.1.3. Growing Government and Corporate Investment in AI-Driven Materials Research
4.2.2. Restraints
4.2.2.1. Shortage of Skilled Talent and High Cost of Platforms and Services Limiting Adoption
4.2.3. Opportunities
4.2.3.1. Generative AI and Large Language Models Creating New Opportunities in Materials Design
4.2.3.2. Growth in Battery, Semiconductor, and Sustainable Materials Development
4.2.4. Challenges
4.2.4.1. Insufficient Data Volume and Quality Expected to Remain a Major Challenge
4.3. Key Trends
4.3.1. Rise of Generative AI Models for Materials Design
4.3.2. Integration with Lab Automation for Closed-Loop, Self-Driving Labs
4.4. Vendor Selection Criteria/Factors Influencing Purchase Decisions
4.5. Use Cases
4.6. Porter's Five Forces Analysis
4.6.1. Bargaining Power of Buyers: Moderate
4.6.2. Bargaining Power of Suppliers: Moderate
4.6.3. Threat of Substitutes: Low to Moderate
4.6.4. Threat of New Entrants: Moderate
4.6.5. Degree of Competition: High
4.7. Value Chain Analysis
4.8. Pricing Analysis
4.9. Technology Analysis
4.10. PESTEL Analysis
5. Materials Informatics Market Assessment - By Offering
5.1. Overview
5.2. Software & Platforms
5.3. Services
5.3.1. Consulting
5.3.2. Integration & Deployment
5.3.3. Support & Maintenance
6. Materials Informatics Market Assessment - By Deployment Mode
6.1. Overview
6.2. Cloud
6.3. On-premise
7. Materials Informatics Market Assessment - By Material Type
7.1. Overview
7.2. Chemicals & Polymers
7.3. Metals & Alloys
7.4. Composites
7.5. Semiconductor & Electronic Materials
7.6. Nanomaterials
7.7. Other Materials
8. Materials Informatics Market Assessment - By Technique
8.1. Overview
8.2. Machine Learning
8.3. Deep Learning
8.4. Generative Models
8.5. Statistical Analysis
8.6. Genetic Algorithm & Others
9. Materials Informatics Market Assessment - By Application
9.1. Overview
9.2. Materials Discovery
9.3. Property Prediction
9.4. Product Development & Optimization
9.5. Synthesis Route Prediction
10. Materials Informatics Market Assessment - By End User
10.1. Overview
10.2. Chemicals & Petrochemicals
10.3. Pharmaceuticals & Biotechnology
10.4. Electronics & Semiconductors
10.5. Energy & Batteries
10.6. Automotive & Aerospace
10.7. Food & Consumer Goods
10.8. Other End Users
11. Materials Informatics Market Assessment - By Geography
11.1. Overview
11.2. North America
11.2.1. United States
11.2.2. Canada
11.3. Europe
11.3.1. Germany
11.3.2. United Kingdom
11.3.3. France
11.3.4. Italy
11.3.5. Spain
11.3.6. Rest of Europe
11.4. Asia Pacific
11.4.1. China
11.4.2. Japan
11.4.3. South Korea
11.4.4. India
11.4.5. Australia & New Zealand
11.4.6. Rest of Asia Pacific
11.5. Latin America
11.5.1. Brazil
11.5.2. Mexico
11.5.3. Rest of Latin America
11.6. Middle East & Africa
11.6.1. Israel
11.6.2. Saudi Arabia
11.6.3. United Arab Emirates
11.6.4. Rest of Middle East & Africa
12. Competitive Landscape
12.1. Introduction
12.2. Key Growth Strategies
12.3. Competitive Benchmarking
12.4. Competitive Dashboard
12.4.1. Industry Leaders
12.4.2. Market Differentiators
12.4.3. Vanguards
12.4.4. Emerging Companies
12.5. Market Share/Position Analysis
13. Company Profiles (Company Overview, Financial Overview, Product Portfolio, Strategic Developments)
13.1. Schrodinger, Inc. (U.S.)
13.2. Dassault Systemes SE (BIOVIA) (France)
13.3. Citrine Informatics, Inc. (U.S.)
13.4. International Business Machines Corporation (IBM) (U.S.)
13.5. Microsoft Corporation (U.S.)
13.6. Materials Zone Ltd. (Israel)
13.7. SandboxAQ, Inc. (U.S.)
13.8. Kebotix, Inc. (U.S.)
13.9. Phaseshift Technologies Inc. (Canada)
13.10. Mat3ra, Inc. (formerly Exabyte.io) (U.S.)
13.11. Orbital Materials Ltd. (U.K.)
13.12. Siemens AG (Germany)
13.13. Ansys, Inc. (Granta MI) (U.S.)
13.14. Noble.AI, Inc. (U.S.)
13.15. Intellegens Ltd. (U.K.)
13.16. Others
14. Appendix
14.1. Available Customization
14.2. Related Reports
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