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 constructio
• Sustainable materials development for circular economy applications

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

 

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

 

 

2.1 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

 

 

3.1 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

 

 

4.1 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

 

 

5.1 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

 

 

 

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

 

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