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Materials Informatics Market

The Global Materials Informatics Market 2026-2036

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  • Published: May 2026
  • Pages: 190
  • Tables: 31
  • Figures: 20

 

Materials informatics (MI) has emerged as one of the most consequential transformations in industrial R&D since the digitalisation of design itself. Built on the convergence of materials science, data science, and artificial intelligence, MI applies machine learning, high-throughput computation, generative models, and large language models to compress the time and cost of discovering and optimising new materials. Industry practitioners now routinely report 50–70% reductions in the number of physical experiments required to develop a new material, with corresponding time-to-market acceleration measured in years rather than months. What once required decades of iterative trial-and-error can increasingly be completed in two-to-five-year programmes guided by data-driven workflows.

The market has moved from an early-adopter phase between 2014 and 2018, through a growth phase between 2019 and 2023, into the AI-boom acceleration phase that began in 2024 and now defines the industry. Three forces shape the 2026 landscape. First, foundation models, transformer architectures, generative diffusion models, and universal machine-learning interatomic potentials originally developed for language and vision have crossed over decisively into materials science.  Second, big technology firms — Microsoft, Google DeepMind, Meta FAIR, IBM Research, and NVIDIA — have entered the field as direct competitors and infrastructure providers, reshaping competitive economics for the dedicated MI vendor category. Third, mega-funding rounds have arrived, with Lila Sciences alone raising approximately US$550 million cumulatively by Q1 2026 to build fully autonomous labs for life, chemical, and materials sciences.

Adoption is now mainstream. Virtually every major materials player has engaged with MI through external service providers, consortia membership, or in-house programmes. Executive-level mandates to demonstrate AI impact across the business have become as common as bottom-up scientist-led pilots. Sustainability-driven applications — catalysts for green hydrogen, sorbents for carbon capture, low-embodied-carbon cement, recyclable polymers, PFAS replacements, energy-transition battery and fuel-cell materials — represent the largest single application driver, accounting for an increasing share of programme spend through 2036.

The Global Materials Informatics Market 2026–2036 provides a comprehensive analysis of the materials informatics industry at its most transformative inflection point to date. Building on the methodology established in earlier editions and informed by primary interviews conducted with industry players through 2025–2026, this revised edition captures the structural reshaping of the field driven by foundation models, big-tech entry, and the commercialisation of self-driving laboratories. The report forecasts the market through 2036 with both a narrower external MI provider revenue segment and a broader total MI software and services market segment that captures big-tech cloud platform revenue, project-based services, and addressable in-house spend.

The report examines the technologies, business models, applications, and players that define the modern MI industry. New for 2026 is dedicated treatment of foundation models for materials science; the strategic implications of big-tech entry; the autonomous-laboratory revolution; the sharp bifurcation in the funding landscape between mega-rounds for integrated AI-and-experimentation platforms and headwinds facing first-generation MI SaaS; and the geopolitical context.

Report Contents

  • Executive summary including 2026 industry state, AI-boom impact, and global market forecasts
  • Introduction covering motivations, AI integration, and parallel informatics fields
  • Technology analysis: algorithms, foundation models, generative AI, LLMs, agentic AI scientists
  • Data infrastructure, databases (Materials Project, AFLOW, NOMAD, OMat24, GNoME), small-data strategies
  • Computational materials science: DFT, ICME, universal MLIPs, quantum computing
  • Autonomous experimentation and self-driving laboratories
  • Twenty-eight application areas including alloys, drug discovery, batteries, catalysts, polymers, photovoltaics, carbon capture, PFAS replacement, critical minerals
  • Industry analysis: strategic approaches, player categories, funding, SaaS economics, big-tech competition
  • MI consortia and public-private initiatives globally
  • Market forecasts with bull, base, and bear scenarios
  • 53 company profiles
  • Research methodology and references

 

Companies Profiled include Aionics, Albert Invent, Alchemy Cloud, Ansatz AI, Asahi Kasei, Atomic Tessellator, Citrine Informatics, Copernic Catalysts, Cynora, DeepVerse, Dunia Innovations, Elix Inc, Enthought, Exomatter GmbH, Exponential Technologies Ltd, FEHRMANN MaterialsX, fibclick, Genie TechBio, Google DeepMind GNoME, Hitachi High-Tech, IBM Research Materials, Innophore, Intellegens, Kebotix, Kyulux, LG AI Research, Lila Sciences, MaterialsZone, Matmerize Inc, Mat3ra, META, Microsoft, N-ERGY, Noble.AI, Novyte Materials and more......

 

 

 

1             EXECUTIVE SUMMARY            14

  • 1.1        What is Materials Informatics?           14
  • 1.2        Materials Informatics: State of the Industry in 2026            15
  • 1.3        Issues with Materials Science Data 16
  • 1.4        Dealing with Little or Sparse Data    17
  • 1.5        Key Technologies Driving Materials Informatics      18
  • 1.6        Importance in Modern Materials Science and Engineering             19
  • 1.7        Market Challenges and Restraints   19
  • 1.8        Recent Industry Developments         20
  • 1.9        The AI Boom and Its Impact on Materials Informatics         22
  • 1.10     Foundation Models, Generative AI and Materials Discovery           23
  • 1.11     Big Tech Entry into Materials Informatics    25
  • 1.12     Market Players               26
  • 1.13     Funding Landscape: Mega-Rounds and SaaS Headwinds              27
  • 1.14     Future Markets Outlook and Opportunities               28
    • 1.14.1 Integration of AI and Robotics in Materials Labs     28
    • 1.14.2 Self-Driving Laboratories and Autonomous Science Platforms    28
    • 1.14.3 Quantum Machine Learning for Materials Discovery           28
    • 1.14.4 Blockchain for Materials Data Provenance 28
    • 1.14.5 Edge Computing in Materials Informatics   29
    • 1.14.6 Augmented and Virtual Reality in Materials Design              29
    • 1.14.7 Neuromorphic Computing for Materials Modeling                29
    • 1.14.8 Materials Informatics as a Service (MIaaS) 29
    • 1.14.9 Integration with Internet of Things (IoT)         29
    • 1.14.10              Green Technology and Circular Economy Applications     29
    • 1.14.11              Agentic AI Scientists 30
  • 1.15     MI Roadmap  30
  • 1.16     Economic Impact Analysis   31
    • 1.16.1 Cost Savings in Materials R&D           31
    • 1.16.2 Accelerated Time-to-Market for New Materials       31
    • 1.16.3 Job Creation and Skill Development               32
    • 1.16.4 Impact on Traditional Materials Industries 32
  • 1.17     Sustainability and Environmental    32
    • 1.17.1 Role of Materials Informatics in Sustainable Development             32
    • 1.17.2 Reducing Environmental Impact of Materials Production 32
    • 1.17.3 Design for Recyclability and Circular Economy      32
    • 1.17.4 Bio-inspired Materials Discovery      33
    • 1.17.5 Materials for Energy Transition           33
  • 1.18     Geopolitical Considerations: U.S., EU, China, Japan, Korea          33
  • 1.19     Global Market Forecasts        34

 

2             INTRODUCTION          35

  • 2.1        Advent of the Data Science Era         35
  • 2.2        Background to the Emergence of MI               36
  • 2.3        Motivation for Materials Informatics Development               36
    • 2.3.1    Accelerating Discovery            37
    • 2.3.2    Cost Reduction            37
    • 2.3.3    Addressing Global Challenges           37
    • 2.3.4    Maximizing Data Value            37
    • 2.3.5    Handling Complexity 37
    • 2.3.6    Enabling Targeted Design (Inverse Design) 37
    • 2.3.7    Improving Reproducibility      37
    • 2.3.8    Integrating Multidisciplinary Knowledge      38
    • 2.3.9    Supporting Sustainability      38
    • 2.3.10 Competitive Advantage          38
  • 2.4        Integration of Artificial Intelligence (AI) into materials science and engineering                38
    • 2.4.1    AI Opportunities at Every Stage of Materials Design and Development   38
    • 2.4.2    The Transition from Predictive AI to Generative AI in Materials      39
    • 2.4.3    Physical AI: Models that Understand Physics and Chemistry        39
  • 2.5        Problems with Materials Science Data         39
  • 2.6        Algorithm Advancements      39
  • 2.7        Materials Informatics Categories      40
  • 2.8        Trend towards data-driven approaches in science and engineering          41
    • 2.8.1    Bioinformatics              41
    • 2.8.2    Cheminformatics        42
    • 2.8.3    Geoinformatics            43
    • 2.8.4    Health Informatics     43
    • 2.8.5    Environmental Informatics   43
    • 2.8.6    Astroinformatics         43
    • 2.8.7    Neuroinformatics        44
    • 2.8.8    Engineering Informatics          44
    • 2.8.9    Energy Informatics     44
    • 2.8.10 Quantum Informatics               44
  • 2.9        Challenges      44
  • 2.10     Advantages of Machine Learning      45
    • 2.10.1 Acceleration    45
    • 2.10.2 Scoping and Screening            46
    • 2.10.3 New Species and Relationships        46
    • 2.10.4 Closing the Loop on Traditional Synthetic Approaches     46
    • 2.10.5 High-Throughput Virtual Screening (HTVS) 46
  • 2.11     Data Infrastructures for Chemistry and Materials Science              46
  • 2.12     ELN/LIMS Software and Materials Informatics        47
  • 2.13     Proving the Value of Materials Informatics: Case Studies 47

 

3             TECHNOLOGY ANALYSIS       48

  • 3.1        Overview           48
    • 3.1.1    Inputs and Outputs of Materials Informatics Algorithms  49
    • 3.1.2    What is Needed for Materials Informatics?                49
  • 3.2        Technology approaches         49
    • 3.2.1    Summary of Technology Approaches            49
    • 3.2.2    Uncertainty in Experimental Data    49
    • 3.2.3    Data Mining    49
    • 3.2.4    Machine Learning and AI        49
    • 3.2.5    High-Throughput Computation          50
    • 3.2.6    Data Infrastructure     50
    • 3.2.7    Visualization Tools      50
    • 3.2.8    Reinforcement Learning         50
    • 3.2.9    Natural Language Processing             50
    • 3.2.10 Automated Experimentation                50
    • 3.2.11 Workflow Management           51
    • 3.2.12 Quantum Computing               51
    • 3.2.13 QSAR and QSPR          51
    • 3.2.14 Automated feature selection               52
    • 3.2.15 Exploitation vs exploration    53
    • 3.2.16 Pure exploitation vs epsilon-greedy policies in materials informatics      53
    • 3.2.17 Active learning and MI: Choosing experiments to maximize improvement           54
  • 3.3        MI Algorithms 56
    • 3.3.1    Overview of MI Algorithms    56
    • 3.3.2    Types of MI Algorithms            56
    • 3.3.3    Descriptors and Training a Model     57
    • 3.3.4    Supervised vs. Unsupervised Learning         58
    • 3.3.5    Automated Feature Selection             58
    • 3.3.6    Exploitation vs. Exploration; Active Learning            58
    • 3.3.7    Bayesian Optimization            58
    • 3.3.8    Genetic Algorithms    58
    • 3.3.9    Generative vs. Discriminative Algorithms   58
    • 3.3.10 Deep Learning and Neural Network Types  59
    • 3.3.11 Physics-Informed Neural Networks (PINNs)             60
    • 3.3.12 Graph Neural Networks (GNNs) for Materials          60
    • 3.3.13 Transformer Models and the AI Boom           60
    • 3.3.14 Foundation Models for Materials      60
      • 3.3.14.1            Definition and Architecture   60
      • 3.3.14.2            Foundation Models for Computational Data             61
      • 3.3.14.3            Foundation Models for Experimental Data 61
      • 3.3.14.4            Limitations: Data Availability and Compute Cost  61
    • 3.3.15 Generative Models for Inorganic Compounds         62
      • 3.3.15.1            Variational Autoencoders and GANs              62
      • 3.3.15.2            Diffusion Models for Crystal Generation      62
    • 3.3.16 Large Language Models (LLMs) and Materials R&D              63
      • 3.3.16.1            Capabilities of LLMs in Science         63
      • 3.3.16.2            LLM-Powered Material Data Mining 63
      • 3.3.16.3            Agentic LLMs and Autonomous Research  63
    • 3.3.17 AutoML: Democratizing Machine Learning 63
    • 3.3.18 Multi-Model Ensembles          63
    • 3.3.19 How to Work with Small Material Datasets 63
    • 3.3.20 Algorithmic Approaches in MI Are Diverse — Summary    64
  • 3.4        Data infrastructure     64
    • 3.4.1    Overview           64
    • 3.4.2    Developments Targeted for Chemical and Materials Science        64
    • 3.4.3    ELN/LIMS Integration with MI Workflows     64
  • 3.5        Databases and External Repositories           64
    • 3.5.1    Data Repositories — Organizations 64
    • 3.5.2    Leveraging Data Repositories             65
    • 3.5.3    The Materials Project, AFLOW, NOMAD, OQMD     66
    • 3.5.4    Meta's Open Materials 2024 (OMat24) Dataset      66
    • 3.5.5    GNoME Dataset and DeepMind's Contributions to the Materials Project              66
    • 3.5.6    Text Extraction and Analysis 66
    • 3.5.7    ChemDataExtractor V1.0 and V2.0 67
    • 3.5.8    LLMs Expand Material Data Mining Capabilities    67
  • 3.6        Databases to Big Data             67
    • 3.6.1    Rapid data generation and collection            67
    • 3.6.2    Integrated use of materials databases          67
    • 3.6.3    Data reliability               67
  • 3.7        Small Data Strategies in Materials Informatics        67
    • 3.7.1    Utilizing data correlations            68
    • 3.7.2    Selecting descriptors based on theory and experience          68
  • 3.8        MI with Physical Experiments and Characterization            68
    • 3.8.1    High-Throughput Experimentation (HTE)     68
    • 3.8.2    In-situ and Operando Characterisation        68
    • 3.8.3    Advanced Imaging and Spectroscopy            68
    • 3.8.4    Why High-Throughput Screening in Materials is Tougher Than in Other Fields    68
  • 3.9        Computational Materials Science    69
    • 3.9.1    Simulations for Chemistry and Materials Science R&D     69
    • 3.9.2    Density Functional Theory (DFT) — Quantum Mechanical Modeling        69
    • 3.9.3    Surrogate Models for Atomistic Simulation               69
    • 3.9.4    Universal ML Interatomic Potentials (CHGNet, MACE, M3GNet, MatterSim)      69
    • 3.9.5    Multiscale Modelling 70
    • 3.9.6    Integrated Computational Materials Engineering (ICME)  70
    • 3.9.7    ICME and the Role of Machine Learning      71
    • 3.9.8    QuesTek Innovations and ICME: From Service to SaaS      71
    • 3.9.9    Thermo-Calc, CompuTherm and the ICME Software Ecosystem                71
    • 3.9.10 Cloud-Based Simulation Platforms 71
    • 3.9.11 The Potential in Leveraging Quantum Computing 72
    • 3.9.12 Big Tech, Computational Materials Science and MI             72
  • 3.10     Autonomous Experimentation and Self-Driving Labs          72
    • 3.10.1 The Vision: Fully Autonomous Labs 72
    • 3.10.2 The Chemputer             72
    • 3.10.3 Workflow Management for Laboratory Automation              73
    • 3.10.4 A-Lab (Lawrence Berkeley): Closed-Loop Synthesis Validation   73
    • 3.10.5 Lila Sciences AI Science Factory      74
    • 3.10.6 Dunia Innovations: Physics-Informed ML + Lab Automation          75
    • 3.10.7 Google DeepMind's Gemini-Powered Autonomous Lab   75
    • 3.10.8 Commercial Self-Driving Laboratories          75
    • 3.10.9 Mobile Autonomous Robots in Academia  75
    • 3.10.10              Retrosynthesis Through to Robot Execution              76
    • 3.10.11              Technology Pillars for Chemical Autonomy               76
  • 3.11     Multi-modal Data Integration              76
  • 3.12     Inverse Problems in Materials Characterization     76
  • 3.13     Data-driven Experimental Design    76
  • 3.14     Automated Data Analysis and Interpretation            77
  • 3.15     Robotics and Automation in Materials Research   77
  • 3.16     Digital Twins for Materials and Process Engineering            77

 

4             APPLICATIONS OF MATERIALS INFORMATICS        78

  • 4.1        Alloy Design and Optimization           78
    • 4.1.1    High-Entropy Alloy Design     78
    • 4.1.2    Aluminum and titanium alloys           79
    • 4.1.3    Metallic glass alloys  79
    • 4.1.4    Nickel-base superalloys         79
    • 4.1.5    Steels for Extreme Environments     80
  • 4.2        Drug Discovery and Development    80
    • 4.2.1    AI-Driven Drug Design              80
  • 4.3        Intermetallics 80
  • 4.4        Organometallics         81
  • 4.5        Organic Electronics   81
    • 4.5.1    RFID     81
    • 4.5.2    OPV      81
    • 4.5.3    OLEDs 82
    • 4.5.4    Emerging Areas            82
  • 4.6        Coatings and Paints  82
  • 4.7        Catalysts          83
    • 4.7.1    Heterogeneous Catalysts      83
    • 4.7.2    Catalysts for Green Hydrogen Production  83
    • 4.7.3    Open Catalyst Project (Meta)              84
  • 4.8        Ionic liquids    84
  • 4.9        Battery Materials         84
    • 4.9.1    Lithium-ion batteries 84
    • 4.9.2    Solid-State Batteries 85
    • 4.9.3    Lithium-Sulfur and Beyond-Li Batteries        85
    • 4.9.4    Accelerated Battery Material Discovery       86
  • 4.10     High-density Heat Storage Materials              86
  • 4.11     Hydrogen-based Superconductors 86
  • 4.12     Sorbents for Carbon Capture              86
  • 4.13     Polymer Informatics  87
    • 4.13.1 Optimizing Additive Manufacturing Materials          87
    • 4.13.2 Sustainable Polymer Development 87
    • 4.13.3 Large Engineering Models for Polymer Processing 88
  • 4.14     Rubber processing     88
  • 4.15     Nanomaterials              88
    • 4.15.1 Nanofabrication          88
    • 4.15.2 Quantum Dots              88
    • 4.15.3 Other Nanomaterials               89
  • 4.16     2D materials   89
  • 4.17     Metamaterials               89
  • 4.18     Lubricants       90
  • 4.19     Thermoelectric Materials       90
  • 4.20     Photovoltaics 90
    • 4.20.1 Light Absorbers and Solar Cells        90
    • 4.20.2 Perovskite Photovoltaics        90
    • 4.20.3 Tandem Cells 91
  • 4.21     Metal-insulator transition compounds         91
  • 4.22     Self-assembled monolayers                91
  • 4.23     Construction Materials and Cement              92
  • 4.24     Biomaterials   92
  • 4.25     Materials for Quantum Technologies             92
  • 4.26     Materials for Defence and Extreme Environments 93
  • 4.27     PFAS Replacement Materials              93
  • 4.28     Critical Minerals and Rare-Earth Substitution         93

 

5             INDUSTRY ANALYSIS 95

  • 5.1        Materials Informatics: State of the Industry in 2026            95
  • 5.2        Strategic Approaches to MI  95
    • 5.2.1    Materials Informatics Players              96
    • 5.2.2    SaaS Platforms             96
    • 5.2.3    Project-Based Consultancies             97
    • 5.2.4    In-house Development by Materials Corporates    97
    • 5.2.5    Big Tech Cloud Platforms       97
    • 5.2.6    Conclusions for End-Users   98
    • 5.2.7    Conclusions for External MI Companies     98
  • 5.3        Player Analysis             98
    • 5.3.1    Materials Informatics Players — Overview 98
    • 5.3.2    Key Partners and Customers of Selected External Providers          99
    • 5.3.3    Partnerships with Engineering Simulation Software             99
    • 5.3.4    Funding Raised by Private Companies (I): In-House Development Drives Capital Requirements                99
    • 5.3.5    Funding Raised by Private Companies (II): The State of SaaS Business Models 101
    • 5.3.6    Pricing MI SaaS Platforms     104
      • 5.3.6.1 Risks for SaaS Business Models in MI            104
    • 5.3.7    Barriers to Profitability for MI SaaS Players 104
    • 5.3.8    Microsoft's Azure Quantum Elements: Competition for Dedicated MI Players   105
    • 5.3.9    Applications of Azure Quantum Elements  105
    • 5.3.10 Google DeepMind's GNoME and the Vertical Integration Play       105
    • 5.3.11 Meta's FAIR, OMat24 and the Open Catalyst Project           105
    • 5.3.12 Taking Materials Informatics In-House          106
    • 5.3.13 Offering In-Housed Operations as a Service             106
    • 5.3.14 Retrosynthesis Prediction     107
    • 5.3.15 Commercial Retrosynthesis Predictors        107
  • 5.4        MI Consortia and Public-Private Initiatives 107
    • 5.4.1    NIMS and Materials Open Platforms (Japan)            108
    • 5.4.2    AIST Data-Driven Consortium (Japan)           108
    • 5.4.3    Toyota Research Institute and University Collaboration    108
    • 5.4.4    The Global Acceleration Network     109
    • 5.4.5    IBM Collaborations    109
    • 5.4.6    ChiMaD and the CMD Network          109
    • 5.4.7    The Open Catalyst Project: Crowdsourcing MI        109
    • 5.4.8    Materials Genome Initiative (MGI) — U.S.   109
    • 5.4.9    Materials Genome Engineering / National Materials Genome Project (China)   109
    • 5.4.10 Horizon Europe Materials Initiatives               110
    • 5.4.11 K-Moonshot    110
    • 5.4.12 Additional Initiatives and Research Centers              110
  • 5.5        Corporate Initiatives in MI      110
  • 5.6        Strategic Collaborations and Agreements 2024–2026       111
  • 5.7        Geopolitics, Export Controls and MI               112
  • 5.8        Applications of Materials Informatics            113
    • 5.8.1    Project Categories in MI          113
    • 5.8.2    Application Progression         113
    • 5.8.3    Materials Informatics Roadmap 2026–2036             113
  • 5.9        Market Forecast and Outlook             114
    • 5.9.1    Market Forecast: External Materials Informatics Players (Provider Revenue)      114
    • 5.9.2    Market Forecast: Total MI Software & Services Market       114
    • 5.9.3    Forecast Data and Market Outlook 115
    • 5.9.4    Sensitivity Analysis: Bull, Base, and Bear Scenarios            115
  • 5.10     MI Industry Player Data           116
    • 5.10.1 Lists of MI Players       116
    • 5.10.2 Full Player List — Commercial Companies (Confirmed Operational)       116
    • 5.10.3 Industry Leavers (Likely and Confirmed)     119

 

6             COMPANY PROFILES                121 (53 company profiles)

 

7             RESEARCH METHODOLOGY              176

 

8             REFERENCES 180

 

List of Tables

  • Table 1. Issues with materials science data.             17
  • Table 2. Key Technologies Driving Materials Informatics. 18
  • Table 3. Market Challenges and Restraint in Materials Informatics.          19
  • Table 4. Materials informatics industry developments 2024-2026.           20
  • Table 5. Foundation models for materials science: comparison 23
  • Table 6. Big Tech entrants in materials informatics: capabilities and strategy     25
  • Table 7. Market players in materials informatics-comparative analysis. 26
  • Table 8. Global materials informatics market size 2025–2036 (USD millions)    34
  • Table 9. Key areas of algorithm advancements in materials informatics                39
  • Table 10. Main categories within Materials Informatics.    41
  • Table 11. Key challenges for MI in materials-by type.           44
  • Table 12. Generative vs. discriminative algorithms               58
  • Table 13. Types of neural network     59
  • Table 14. Materials data repositories: open-source and commercial (new)         64
  • Table 15. Universal ML interatomic potentials — comparison      69
  • Table 16. Mega-rounds in MI 2024–2026 (new)       102
  • Table 17. Pricing models for MI SaaS platforms      104
  • Table 18. National MI initiatives by country                107
  • Table 19.Corporate initiatives in MI 110
  • Table 20. MI strategic collaborations and agreements 2024–2026            111
  • Table 21. External MI provider revenue forecast 2025–2036          114
  • Table 22. Global materials informatics market size 2025–2036 (US$M) 114
  • Table 23. Bull, base, and bear case forecasts to 2036 (US$M, total MI software and services market)                115
  • Table 24. Dedicated MI SaaS Platforms        116
  • Table 25. Project-Based Consultancies       117
  • Table 26. Physics-Based Incumbents with AI Capabilities              118
  • Table 27. Autonomous-Laboratory and Integrated AI-Plus-Experimentation Platforms                118
  • Table 28. Big-Tech Cloud Platforms and Open-Model Providers   118
  • Table 29. Materials-Specialty MI Players (Single-Domain Focus) 119
  • Table 30. Major Materials Corporates with In-House MI Capability            119
  • Table 31. Industry leavers and consolidations 2023–2026              119

 

List of Figures

  • Figure 1. Comparison of Conventional Materials Development and Materials Informatics.      15
  • Figure 2. Materials informatics maturity curve 2014–2026             16
  • Figure 3. The shift from predictive AI to generative AI in materials              24
  • Figure 4. Materials informatics roadmap 2026–2036          31
  • Figure 5. Global materials informatics market size 2025–2036 (USD millions)  35
  • Figure 6. Incorporating Machine Learning into Established Bioinformatics Frameworks.           42
  • Figure 7. Example of cheminformatics utilization  43
  • Figure 8. Molecular design methodology based on QSPR/QSAR.               51
  • Figure 9. Foundation model architecture for materials science   61
  • Figure 10. Diffusion model schematic for crystal generation (MatterGen)             62
  • Figure 11. Growth of stable known crystals               66
  • Figure 12. Overview of the ICME process integration and optimization workflow             71
  • Figure 13. Chemputer.             73
  • Figure 14. A-Lab autonomous synthesis workflow (Lawrence Berkeley) 74
  • Figure 15. Lila Sciences AI Science Factory architecture  75
  • Figure 16. Classes of players in materials informatics (updated) 96
  • Figure 17. Funding raised by major MI private companies cumulative to 2026  101
  • Figure 18. External MI provider revenue forecast 2025–2036        116
  • Figure 19. Citrine Platform Overview.             128
  • Figure 20. Hitachi High-Tech Chemicals Informatics and Materials Informatics proof of concept.       141

 

 

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  • Comprehensive Excel spreadsheet of all data.
  • Mid-year Update

 

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Additional Print Edition Supplementary to PDF.
Price: £100.00

 

Payment methods: Visa, Mastercard, American Express, 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

 

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