cover
- Published: May 2025
- Pages: 225
- Tables: 16
- Figures: 10
The materials informatics (MI) market represents a rapidly developing sector where data science, artificial intelligence, and materials science converge to accelerate discovery and optimization of new materials. The core value proposition driving this growth is the dramatic reduction in materials development timelines. Traditional approaches typically require 10-20 years from concept to commercialization, whereas MI-enabled methods can potentially compress this to 2-5 years. This acceleration delivers significant competitive advantages in industries where material innovation directly impacts product performance and market differentiation.
Several distinct business models have emerged within the MI ecosystem. Software-as-a-Service (SaaS) platforms from companies like Citrine Informatics, Kebotix, and Materials Design provide specialized tools for materials scientists with limited data science expertise. These platforms typically employ subscription models with tiered pricing based on functionality and user numbers. Meanwhile, MI consultancies like NobleAI offer project-based engagements focusing on specific material development challenges. Major corporations including BASF, Toyota, and Samsung have also established substantial in-house MI capabilities, representing a third pathway to market adoption.
Recent market activity has been characterized by significant venture capital investment, with several MI startups securing funding rounds exceeding $50 million. Simultaneously, large technology companies have entered the space, most notably Microsoft with its Azure Quantum Elements platform, potentially disrupting smaller players' market positions. Strategic partnerships between MI providers and traditional materials simulation software companies have also increased, creating more comprehensive integrated solutions. By application sector, battery materials currently represent the largest segment (approximately 30% of market value), followed by advanced polymers (20%), catalysts (15%), and alloys (12%). The strongest growth is projected in pharmaceutical materials discovery and renewable energy applications.
Key challenges facing the market include data quality and standardization issues, the high expertise barrier combining materials science and data science, and questions about return on investment given the significant upfront costs of MI implementation. Despite these challenges, the market is expected to continue rapid expansion as successful case studies demonstrate clear competitive advantages for early adopters, creating pressure across industries to implement MI approaches or risk falling behind in materials innovation capabilities.
The Global Materials Informatics Market 2025-20 provides an in-depth analysis of the rapidly evolving materials informatics (MI) industry, examining current technologies, market dynamics, key players, and future growth trajectories through 2035. As materials discovery and optimization increasingly leverage artificial intelligence and data science approaches, this report offers essential strategic insights for stakeholders across the materials value chain.
Report Contents include:
- Historical development of materials informatics within data science evolution
- Analysis of key motivating factors driving MI adoption, including time-to-market acceleration and cost reduction
- Detailed examination of AI integration opportunities in materials science
- Comparative analysis of MI with parallel informatics fields (bioinformatics, cheminformatics, etc.)
- Assessment of primary challenges facing widespread MI implementation
- Evaluation of machine learning advantages specific to materials development workflows
- Technology Analysis
- Detailed examination of MI workflows from scoping to implementation
- Comprehensive analysis of core technology approaches including data mining, ML/AI, high-throughput computation
- In-depth assessment of MI algorithm types, capabilities, and application scenarios
- Evaluation of data infrastructure requirements and implementation strategies
- Analysis of database integration approaches and big data challenges in materials science
- Examination of small data strategies for materials development environments
- Assessment of physical experimentation integration with MI workflows
- Detailed overview of computational materials science applications
- Evaluation of autonomous experimentation technologies and implementation roadmaps
- Applications of Materials Informatics
- Detailed case studies across 21 material categories including:
- Alloy design optimization with specific focus on high-entropy, aluminum, titanium, and superalloys
- Pharmaceutical and drug discovery applications
- Specialty materials (intermetallics, organometallics, ionic liquids)
- Electronic materials including organic electronics and 2D materials
- Energy materials with focus on batteries, hydrogen technologies, and thermoelectrics
- Structural materials including polymers, nanomaterials, and construction applications
- Sustainable materials development for circular economy applications
- Detailed case studies across 21 material categories including:
- Market Analysis
- Comprehensive competitive landscape assessment of major players and emerging competitors
- Detailed funding analysis for MI companies with investment trends through 2025
- Strategic approaches analysis for both MI providers and end-users
- Examination of key consortia, corporate initiatives, and strategic partnerships
- Analysis of global MI initiatives and government-backed programs
- Research center and academic activity assessment
- Detailed company profiles of 42 MI technology providers and end-users. Companies profiled include Albert Invent, Alchemy Cloud, Ansatz AI, Citrine Informatics, Copernic Catalysts, Cynora, Dunia Innovations, Elix Inc., Enthought, Exomatter GmbH, Exponential Technologies Ltd., FEHRMANN MaterialsX, Fluence Analytics, Genie TechBio, Hitachi High-Tech, Innophore, Intellegens, Kebotix, Kyulux, LG AI Research, materialsIn, Materials Zone, Matmerize Inc., Mat3ra, META and more.....
- Market size forecasts with segmentation by:
- Technology type and application area
- Geographic region and industry vertical
- Business model (SaaS, consulting, in-house)
- End-user type and company size
- Future Outlook and Economic Impact
- Assessment of emerging technologies including quantum machine learning and neuromorphic computing
- Analysis of economic impacts including R&D cost savings and time-to-market acceleration
- Evaluation of MI's role in sustainable development and circular economy initiatives
- Global market forecasts from 2025-2035 with detailed growth analysis
- Strategic recommendations for MI providers, end-users, and investors
This comprehensive analysis includes company overviews, proprietary technology assessments, business models, key partnerships, target markets, funding history, and strategic positioning within the materials informatics ecosystem. The report provides both established industry leaders and emerging start-ups with actionable intelligence to navigate this rapidly evolving market landscape through 2035.
1 EXECUTIVE SUMMARY 10
- 1.1 What is Materials Informatics? 10
- 1.2 Issues with Materials Science Data 11
- 1.3 Dealing with little or sparse data 12
- 1.4 Key Technologies Driving Materials Informatics 12
- 1.5 Importance in Modern Materials Science and Engineering 14
- 1.6 Market Challenges and Restraints 16
- 1.7 Recent Industry Developments 17
- 1.8 Market Players 19
- 1.9 Future Markets Outlook and Opportunities 21
- 1.9.1 Integration of AI and Robotics in Materials Labs 21
- 1.9.2 Quantum Machine Learning for Materials Discovery 22
- 1.9.3 Blockchain for Materials Data Management 23
- 1.9.4 Edge Computing in Materials Informatics 24
- 1.9.5 Augmented and Virtual Reality in Materials Design 25
- 1.9.6 Neuromorphic Computing for Materials Modeling 25
- 1.9.7 Materials Informatics as a Service (MIaaS) 27
- 1.9.8 Integration with Internet of Things (IoT) 27
- 1.9.9 Green Technology and Circular Economy Applications 27
- 1.10 MI Roadmap 27
- 1.11 Economic Impact Analysis 29
- 1.11.1 Cost Savings in Materials R&D 29
- 1.11.2 Accelerated Time-to-Market for New Materials 30
- 1.11.3 Job Creation and Skill Development 31
- 1.11.4 Impact on Traditional Materials Industries 32
- 1.12 Sustainability and Environmental 33
- 1.12.1 Role of Materials Informatics in Sustainable Development 33
- 1.12.2 Reducing Environmental Impact of Materials Production 34
- 1.12.3 Design for Recyclability and Circular Economy 35
- 1.12.4 Bio-inspired Materials Discovery 36
- 1.13 Global Market Forecasts 36
2 INTRODUCTION 39
- 2.1 Advent of the data science era 39
- 2.2 Background to the emergence of MI 40
- 2.3 Motivation for Materials Informatics Development 41
- 2.3.1 Accelerating Discovery 41
- 2.3.2 Cost Reduction 42
- 2.3.3 Addressing Global Challenges 43
- 2.3.4 Maximizing Data Value 44
- 2.3.5 Handling Complexity 45
- 2.3.6 Enabling Targeted Design 46
- 2.3.7 Improving Reproducibility 47
- 2.3.8 Integrating Multidisciplinary Knowledge 48
- 2.3.9 Supporting Sustainability 49
- 2.3.10 Competitive Advantage 50
- 2.4 Integration of Artificial Intelligence (AI) into materials science and engineering 50
- 2.4.1 AI Opportunities 51
- 2.5 Problems with Materials Science Data 52
- 2.6 Algorithm Advancements 53
- 2.7 Materials Informatics Categories 54
- 2.8 Trend towards data-driven approaches in science and engineering 55
- 2.8.1 Bioinformatics 55
- 2.8.2 Cheminformatics 57
- 2.8.3 Geoinformatics 58
- 2.8.4 Health Informatics 59
- 2.8.5 Environmental Informatics 60
- 2.8.6 Astroinformatics 60
- 2.8.7 Neuroinformatics 61
- 2.8.8 Engineering Informatics 61
- 2.8.9 Energy Informatics 62
- 2.8.10 Quantum Informatics 63
- 2.9 Challenges 64
- 2.10 Advantages of Machine Learning 65
3 TECHNOLOGY ANALYSIS 75
- 3.1 Overview 76
- 3.1.1 Scoping and Screening 76
- 3.1.2 New Species and Relationships 76
- 3.1.3 Closing the Loop on Traditional Synthetic Approaches 77
- 3.1.4 High Throughput Virtual Screening (HTVS) 78
- 3.1.5 Inputs and outputs of materials informatics algorithms 80
- 3.2 Technology approaches 82
- 3.2.1 Data Mining 83
- 3.2.2 Machine Learning and AI 84
- 3.2.3 High-Throughput Computation 85
- 3.2.4 Data Infrastructure 86
- 3.2.5 Visualization Tools 87
- 3.2.6 Reinforcement Learning 87
- 3.2.7 Natural Language Processing 88
- 3.2.8 Automated Experimentation 89
- 3.2.9 Workflow Management 90
- 3.2.10 Quantum Computing 91
- 3.2.11 QSAR and QSPR 92
- 3.3 MI algorithms 93
- 3.3.1 Types of MI Algorithms 94
- 3.3.2 Automated feature selection 96
- 3.3.3 Supervised learning models 97
- 3.3.3.1 Supervised Learning Algorithms 98
- 3.3.3.2 Unsupervised Learning Algorithms 99
- 3.3.4 Bayesian optimization 100
- 3.3.5 Genetic algorithms 101
- 3.3.6 Generative vs discriminative algorithms 102
- 3.3.7 Deep learning 102
- 3.3.8 Large Language Models (LLMs) and Materials R&D 104
- 3.4 Data infrastructure 105
- 3.5 Databases 107
- 3.6 Databases to big data 108
- 3.6.1 Rapid data generation and collection 108
- 3.6.2 Integrated use of materials databases 109
- 3.6.3 Data reliability 110
- 3.7 Small data strategies in materials informatics 111
- 3.7.1 Utilizing data correlations 112
- 3.7.2 Selecting descriptors based on theory and experience 113
- 3.8 MI with Physical Experiments and Characterization 114
- 3.8.1 High-Throughput Experimentation (HTE) 114
- 3.8.2 In-situ and Operando Characterization 116
- 3.8.3 Advanced Imaging and Spectroscopy 118
- 3.9 Computational Materials Science 120
- 3.9.1 Integrated Computational Materials Engineering (ICME) 121
- 3.9.2 Quantum Computing 123
- 3.10 Autonomous Experimentation and Labs 125
- 3.10.1 Fully autonomous labs 125
- 3.11 Multi-modal Data Integration 129
- 3.12 Inverse Problems in Materials Characterization 130
- 3.13 Data-driven Experimental Design 131
- 3.14 Automated Data Analysis and Interpretation 131
- 3.15 Robotics and Automation in Materials Research 133
4 APPLICATIONS OF MATERIALS INFORMATICS 135
- 4.1 Alloy Design and Optimization 135
- 4.1.1 High-Entropy Alloy Design 135
- 4.1.2 Aluminum and titanium alloys 136
- 4.1.3 Metallic glass alloys 137
- 4.1.4 Nickel-base superalloys 137
- 4.2 Drug Discovery and Development 139
- 4.2.1 AI-Driven Drug Design 139
- 4.3 Intermetallics 140
- 4.4 Organometallics 141
- 4.5 Organic Electronics 142
- 4.6 Coatings 144
- 4.7 Catalysts 145
- 4.8 Ionic liquids 146
- 4.9 Battery Materials 148
- 4.9.1 Lithium-ion batteries 148
- 4.9.2 Accelerated Battery Material Discovery 149
- 4.10 High-density Heat Storage Materials 149
- 4.11 Hydrogen-based Superconductors 150
- 4.12 Polymer Informatics 152
- 4.12.1 Optimizing Additive Manufacturing Materials 152
- 4.12.2 Sustainable Polymer Development 153
- 4.13 Rubber processing 155
- 4.14 Nanomaterials 156
- 4.15 2D materials 158
- 4.16 Metamaterials 159
- 4.17 Lubricants 160
- 4.18 Thermoelectric Materials 161
- 4.19 Photovoltaics 162
- 4.20 Construction Materials 164
- 4.21 Biomaterials 165
5 MARKET PLAYERS 166
- 5.1 Main Players 166
- 5.2 Funding 168
- 5.3 Market Strategies 170
- 5.4 MI Consortia 172
- 5.5 Corporate Initiatives in MI 173
- 5.6 Strategic Collaborations and Agreements 174
- 5.7 Global Initiatives 176
- 5.8 Research Centre and Academic Activity 177
6 COMPANY PROFILES 178 (42 company profiles)
7 RESEARCH METHODOLOGY 219
8 REFERENCES 220
List of Tables
- Table 1. Issues with materials science data. 12
- Table 2. Key Technologies Driving Materials Informatics. 13
- Table 3. Market Challenges and Restraint in Materials Informatics. 16
- Table 4. Materials informatics industry developments 2022-2025. 17
- Table 5. Market players in materials informatics-comparative analysis. 19
- Table 6. Global materials informatics market size 2023-2035 (Millions USD). 37
- Table 7. Key areas of algorithm advancements in materials informatics 53
- Table 8. Main categories within Materials Informatics. 54
- Table 9. Key challenges for MI in materials-by type. 64
- Table 10. Technology approaches. 82
- Table 11. Types of MI Algorithms. 94
- Table 12. Generative vs discriminative algorithms. 102
- Table 13. Types of neural network. 103
- Table 14. Materials informatics investment funding. 168
- Table 15. Corporate Initiatives in MI. 173
- Table 16. MI Strategic Collaborations and Agreements. 174
List of Figures
- Figure 1. Comparison of Conventional Materials Development and Materials Informatics. 11
- Figure 2. Materials Informatics (MI) Roadmap. 28
- Figure 3. Global materials informatics market size 2023-2035 (Millions USD). 38
- Figure 4. Incorporating Machine Learning into Established Bioinformatics Frameworks. 56
- Figure 5. Example of CI Utilization. 57
- Figure 6. Molecular design methodology based on QSPR/QSAR. 92
- Figure 7. Overview of the ICME process integration and optimization workflow. 121
- Figure 8. Chemputer. 126
- Figure 9. Citrine Platform Overview. 184
- Figure 10. Hitachi High-Tech Chemicals Informatics and Materials Informatics proof of concept. 195
Payment methods: Visa, Mastercard, American Express, Paypal, Bank Transfer. To order by Bank Transfer (Invoice) select this option from the payment methods menu after adding to cart, or contact info@futuremarketsinc.com
