Materials Informatics Global Market 2024-2035

1             EXECUTIVE SUMMARY            

• 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     22
  • 1.9.2    Quantum Machine Learning for Materials Discovery           23
  • 1.9.3    Blockchain for Materials Data Management             24
  • 1.9.4    Edge Computing in Materials Informatics   24
  • 1.9.5    Augmented and Virtual Reality in Materials Design              26
  • 1.9.6    Neuromorphic Computing for Materials Modeling                26
  • 1.9.7    Materials Informatics as a Service (MIaaS) 28
  • 1.9.8    Integration with Internet of Things (IoT)         28
  • 1.9.9    Green Technology and Circular Economy Applications     28
• 1.10     MI Roadmap  28
• 1.11     Economic Impact Analysis   30
  • 1.11.1 Cost Savings in Materials R&D           30
  • 1.11.2 Accelerated Time-to-Market for New Materials       31
  • 1.11.3 Job Creation and Skill Development               32
  • 1.11.4 Impact on Traditional Materials Industries 33
• 1.12     Sustainability and Environmental    34
  • 1.12.1 Role of Materials Informatics in Sustainable Development             34
  • 1.12.2 Reducing Environmental Impact of Materials Production 36
  • 1.12.3 Design for Recyclability and Circular Economy      37
  • 1.12.4 Bio-inspired Materials Discovery      37
• 1.13     Global Market Forecasts        38

2             INTRODUCTION          

• 2.1        Advent of the data science era           41
• 2.2        Background to the emergence of MI               42
• 2.3        Motivation for Materials Informatics Development               43
  • 2.3.1    Accelerating Discovery            43
  • 2.3.2    Cost Reduction            44
  • 2.3.3    Addressing Global Challenges           45
  • 2.3.4    Maximizing Data Value            46
  • 2.3.5    Handling Complexity 47
  • 2.3.6    Enabling Targeted Design      48
  • 2.3.7    Improving Reproducibility      49
  • 2.3.8    Integrating Multidisciplinary Knowledge      50
  • 2.3.9    Supporting Sustainability      51
  • 2.3.10 Competitive Advantage          52
• 2.4        Integration of Artificial Intelligence (AI) into materials science and engineering                53
  • 2.4.1    AI Opportunities          54
• 2.5        Problems with Materials Science Data         55
• 2.6        Algorithm Advancements      56
• 2.7        Materials Informatics Categories      58
• 2.8        Trend towards data-driven approaches in science and engineering          59
  • 2.8.1    Bioinformatics              59
  • 2.8.2    Cheminformatics        61
  • 2.8.3    Geoinformatics            62
  • 2.8.4    Health Informatics     63
  • 2.8.5    Environmental Informatics   64
  • 2.8.6    Astroinformatics         65
  • 2.8.7    Neuroinformatics        65
  • 2.8.8    Engineering Informatics          66
  • 2.8.9    Energy Informatics     67
  • 2.8.10 Quantum Informatics               67
• 2.9        Challenges      69
• 2.10     Advantages of Machine Learning      70

3             TECHNOLOGY ANALYSIS       

• 3.1        Overview           82
• 3.2        Technology approaches         89
  • 3.2.1    Data Mining    89
  • 3.2.2    Machine Learning and AI        90
  • 3.2.3    High-Throughput Computation          91
  • 3.2.4    Data Infrastructure     92
  • 3.2.5    Visualization Tools      93
  • 3.2.6    Reinforcement Learning         94
  • 3.2.7    Natural Language Processing             95
  • 3.2.8    Automated Experimentation                96
  • 3.2.9    Workflow Management           96
  • 3.2.10 Quantum Computing               98
  • 3.2.11 QSAR and QSPR          98
• 3.3        MI algorithms 100
  • 3.3.1    Types of MI Algorithms            100
  • 3.3.2    Automated feature selection               103
  • 3.3.3    Supervised learning models 104
    • 3.3.3.1 Supervised Learning Algorithms       105
    • 3.3.3.2 Unsupervised Learning Algorithms 106
  • 3.3.4    Bayesian optimization             107
  • 3.3.5    Genetic algorithms    108
  • 3.3.6    Generative vs discriminative algorithms      109
  • 3.3.7    Deep learning 110
  • 3.3.8    Large Language Models (LLMs) and Materials R&D              112
• 3.4        Data infrastructure     113
• 3.5        Databases       115
• 3.6        Databases to big data              115
  • 3.6.1    Rapid data generation and collection            116
  • 3.6.2    Integrated use of materials databases          117
  • 3.6.3    Data reliability               118
• 3.7        Small data strategies in materials informatics        119
  • 3.7.1    Utilizing data correlations      120
  • 3.7.2    Selecting descriptors based on theory and experience      121
• 3.8        MI with Physical Experiments and Characterization            122
  • 3.8.1    High-Throughput Experimentation (HTE)     122
  • 3.8.2    In-situ and Operando Characterization        124
  • 3.8.3    Advanced Imaging and Spectroscopy            126
• 3.9        Computational Materials Science    128
  • 3.9.1    Integrated Computational Materials Engineering (ICME)  129
  • 3.9.2    Quantum Computing               131
• 3.10     Autonomous Experimentation and Labs     134
• 3.11     Multi-modal Data Integration              138
• 3.12     Inverse Problems in Materials Characterization     139
• 3.13     Data-driven Experimental Design    140
• 3.14     Automated Data Analysis and Interpretation            141
• 3.15     Robotics and Automation in Materials Research   142

4             APPLICATIONS OF MATERIALS INFORMATICS        

• 4.1        Alloy Design and Optimization           144
  • 4.1.1    High-Entropy Alloy Design     145
  • 4.1.2    Aluminum and titanium alloys           145
  • 4.1.3    Metallic glass alloys  146
  • 4.1.4    Nickel-base superalloys         147
• 4.2        Drug Discovery and Development    148
  • 4.2.1    AI-Driven Drug Design              149
• 4.3        Intermetallics 150
• 4.4        Organometallics         151
• 4.5        Organic Electronics   152
• 4.6        Coatings            154
• 4.7        Catalysts          155
• 4.8        Ionic liquids    157
• 4.9        Battery Materials         158
  • 4.9.1    Lithium-ion batteries 158
  • 4.9.2    Accelerated Battery Material Discovery       159
• 4.10     High-density Heat Storage Materials              160
• 4.11     Hydrogen-based Superconductors 161
• 4.12     Polymer Informatics  162
  • 4.12.1 Optimizing Additive Manufacturing Materials          163
  • 4.12.2 Sustainable Polymer Development 164
• 4.13     Rubber processing     166
• 4.14     Nanomaterials              167
• 4.15     2D materials   169
• 4.16     Metamaterials               170
• 4.17     Lubricants       171
• 4.18     Thermoelectric Materials       172
• 4.19     Photovoltaics 174
• 4.20     Construction Materials           176
• 4.21     Biomaterials   177

5             MARKET PLAYERS      

• 5.1        Main Players   178
• 5.2        Funding             180
• 5.3        Market Strategies        182
• 5.4        MI Consortia  184
• 5.5        Corporate Initiatives in MI      185
• 5.6        Strategic Collaborations and Agreements  186
• 5.7        Global Initiatives          188
• 5.8        Research Centre and Academic Activity      190

6             COMPANY PROFILES (35 company profiles)               

7             RESEARCH METHODOLOGY              227

8             REFERENCES 228

List of Tables

• Table 1. Issues with materials science data.             11
• Table 2. Key Technologies Driving Materials Informatics. 12
• Table 3. Market Challenges and Restraint in Materials Informatics.          16
• Table 4. Materials informatics industry developments 2022-2024.           17
• Table 5. Market players in materials informatics-comparative analysis. 20
• Table 6. Global materials informatics market size 2023-2035 (Millions USD).   38
• Table 7. Key areas of algorithm advancements in materials informatics                56
• Table 8. Main categories within Materials Informatics.      58
• Table 9. Key challenges for MI in materials-by type.             69
• Table 10. Types of MI Algorithms.     100
• Table 11. Generative vs discriminative algorithms.              109
• Table 12. Types of neural network.   110
• Table 13. Materials informatics investment funding.           180
• Table 14. Corporate Initiatives in MI.              185
• Table 15. MI Strategic Collaborations and Agreements.    186

List of Figures

• Figure 1. Comparison of Conventional Materials Development and Materials Informatics.      10
• Figure 2. Materials Informatics (MI) Roadmap.       29
• Figure 3. Global materials informatics market size 2023-2035 (Millions USD). 40
• Figure 4. Incorporating Machine Learning into Established Bioinformatics Frameworks.           60
• Figure 5. Example of CI Utilization.  61
• Figure 6. Molecular design methodology based on QSPR/QSAR.               99
• Figure 7. Overview of the ICME process integration and optimization workflow.              129
• Figure 8. Chemputer. 135
• Figure 9. Citrine Platform Overview.                196
• Figure 10. Hitachi High-Tech Chemicals Informatics and Materials Informatics proof of concept.       204

Materials Informatics Global Market 2024-2035

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Materials Informatics Global Market 2024-2035

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