Materials informatics provider revenue to exceed US$700 million by 2034, transforming R&D.
Werkstoffinformatik 2024-2034: Märkte, Strategien, Akteure
Datenzentrierte Ansätze für Design und Entdeckung in den Materialwissenschaften. Bemerkenswerte Fortschritte in den Bereichen Dateninfrastrukturen, maschinelles Lernen und KI. Spielerprofile, technologischer Fortschritt, Marktausblick, strategische Ansätze.
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Materials Informatics represents an R&D paradigm shift by fundamentally accelerating the time from innovation to market. It represents the digital transformation of the materials industry, with AI driving material development. There are multiple strategic approaches and many notable success stories; missing this transition will be costly.
This report provides key insights into the market, forecasting its growth to 2034. Readers will get a detailed understanding of the players, business models, technology, and the application areas. Over 30 company profiles are included, giving primary insights into this fast-evolving industry.
Materials informatics (MI) involves applying data-centric approaches for materials science R&D, including machine learning. There are multiple strategic approaches and many notable success stories; adoption is accelerating and missing this transition will be costly.
This report provides key insights and commercial outlooks for this emerging field. Built upon technical primary interviews with 27 players, readers will get a detailed understanding of the players, business models, technology, and strategies in this industry. The revenue of firms offering MI services is forecast to 2034, with 11.5% CAGR expected until then. The impact of the ongoing AI boom is considered and numerous relevant projects across materials science are covered. Analysis of the underlying technologies demystifies this fast-growing area of digital transformation in R&D.
What is materials informatics?
Image: The major classes of project in materials informatics. Source: IDTechEx.
Materials informatics is the use of data-centric approaches for the advancement of materials science. This can take numerous forms and influence all parts of R&D (hypothesis - data handling & acquisition - data analysis - knowledge extraction).
Primarily, MI is based on using data infrastructures and leveraging machine learning solutions for the design of new materials, discovery of materials for a given application, and optimization of how they are processed.
MI can accelerate the "forward" direction of innovation (properties are realized for an input material) but the idealized solution is to enable the "inverse" direction (materials are designed given desired properties).
This is not straightforward and is emerging from its nascent stage. In many cases, the data infrastructure is not comprehensive and MI algorithms are often too immature for the given experimental data. The challenge is not the same as in other AI-led areas (such as autonomous cars or social media), the players are often dealing with sparse, high-dimensional, biased, and noisy data; leveraging domain knowledge is an essential part of most approaches.
Contrary to what some may believe, this is not something that will displace research scientists. If integrated correctly, MI will become a set of enabling technologies accelerating scientists' R&D processes whilst making use of their domain expertise. For many, the dream end-goal is for humans to oversee an autonomous self-driving laboratory; although still at an early-stage there have been key improvements, spin-out companies formed, and success stories all facilitated by MI developments.
Why now?
This is not a new approach; many sectors have adopted similar design approaches for decades. But there are three main reasons why this transformative technology is impacting the materials science space right now:
- Improvements in AI-driven solutions leveraged from other sectors. This includes the impact of large language models in simplifying materials informatics.
- Improvements in data infrastructures, from open-access data repositories to cloud-based research platforms.
- Awareness, education, and a need to keep up with the underlying pace of innovation. The AI boom has only accelerated this need.
IDTechEx has identified three repeated advantages to employing advanced machine learning techniques into the R&D process: enhanced screening of candidates & scoping research areas, reducing the number of experiments to develop a new material (and therefore time to market), and finding new materials or relationships. The training data can be based on internal experimental, computational simulation and/or from external data repositories; enhanced laboratory informatics and high throughput experimentation or computation can be integral to many projects.
This report looks at the key progressions in machine learning for MI, the success stories, and how end-users are actively engaging with this.
What are the strategic approaches?
Ignoring this R&D transition is a major oversight for any company that designs materials or designs with materials: awareness of the potential significant missed opportunities in the mid- to long-term is growing rapidly. This could be when bringing competitive products to market, developing versatility in the supply chain, finding next-generation opportunities, or generating the ability to diversify a business unit or material portfolio.
Numerous players have already begun this adoption with three core approaches: operate fully in-house, work with an external company, or join forces as part of a consortium. Each of these approaches is appraised in detail in the report; choosing to start the adoption of MI is important, choosing the right path is essential.
The external MI players can come from numerous starting points, as outlined in the figure below. There is also the option for MI players to become a licensing company with a strong advanced material portfolio and also for end-users to offer MI as a service. Geographically, many of the end-users embracing this technology are Japanese companies, many of the emerging external companies are from USA, and the most notable consortia and academic labs are split across Japan and the USA.
Image: Categorizing materials informatics industry players. Source: IDTechEx
Interview based profiles of many key companies are included within this IDTechEx report.
What will I learn from the report?
This market report is released at a point in time where the 10-year outlook is prime for rapid adoption, with the market for external MI players expected to exceed US$700M by 2034. This report goes far beyond what is available on the internet, providing key commercial outlooks based on primary interviews coupled with expertise on both this topic and numerous of the relevant application areas.
In recent years there has been significant progression in external companies providing MI solutions, more key partnerships and end-user engagements, new consortia and academic advancements, and new companies emerging. All of this is tracked, explained and analyzed throughout this industry leading report on the topic.
Market forecasts, player profiles, investments, roadmaps, and comprehensive company lists are all provided, making this essential reading for anyone wanting to get ahead in this field.
Key aspects
This report provides the following information:
Technology Trends:
- Analysis of the key components to enabling a materials informatics strategy.
- Status, developments, and limitations of AI-driven approaches for materials science R&D, including the impact of large language models.
- Key academic and industrial progressions highlighted.
- Analysis of the impact of the AI boom included.
Company Analysis:
- Comprehensive list, details, and differentiating features of MI companies.
- Over 30 interview-based company profiles.
- Partnerships, funding, and business models evaluated .
- Strategic options critically evaluated.
- End-user engagement assessed.
- National and International consortia and initiatives analyzed.
Applications and Market Outlook
- 10-year market forecast for external MI companies.
- Roadmap for adoption and application.
- Key project types categorized, and relevant examples included.
| Report Metrics | Details |
|---|---|
| CAGR | The global market for external provision of materials informatics will reach US$714 million by 2034, representing 11.5% CAGR vs. 2024. |
| Forecast Period | 2024 - 2034 |
| Forecast Units | USD |
| Regions Covered | Worldwide |
| Segments Covered | Global market forecast for provision of external materials informatics services. |
Analyst access from IDTechEx
All report purchases include up to 30 minutes telephone time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.
Further information
If you have any questions about this report, please do not hesitate to contact our report team at research@IDTechEx.com or call one of our sales managers:
ASIA: +82 10 3896 6219
| 1. | EXECUTIVE SUMMARY |
| 1.1. | What is materials informatics? |
| 1.2. | AI opportunities at every stage of materials design and development |
| 1.3. | Problems with materials science data |
| 1.4. | Types of MI algorithms - Supervised vs unsupervised |
| 1.5. | Key areas of algorithm advancements in MI |
| 1.6. | Simulation data is an important input to MI processes |
| 1.7. | Large Language Models and MI: what are the possibilities? (I) |
| 1.8. | Large Language Models and MI: what are the possibilities? (II) |
| 1.9. | Materials informatics players - categories |
| 1.10. | Conclusions and outlook for strategic approaches: approaches for end-users (I) |
| 1.11. | Conclusions and outlook for strategic approaches: approaches for end-users (II) |
| 1.12. | For MI end-users, there is no one-size-fits-all approach |
| 1.13. | Key Partners and Customers of External Providers |
| 1.14. | Main industry players (I): Established leaders |
| 1.15. | Main industry players (II): Promising challengers |
| 1.16. | Notable MI consortia |
| 1.17. | Materials Informatics: the state of the industry in 2024 |
| 1.18. | Project categories in MI |
| 1.19. | Market forecast: external materials informatics players |
| 1.20. | Market outlook for external MI companies |
| 1.21. | Materials informatics - Market penetration by maturity |
| 1.22. | Materials informatics roadmap |
| 2. | INTRODUCTION |
| 2.1. | What is materials informatics? |
| 2.2. | Materials informatics - Why now? |
| 2.3. | What can ML/AI do in materials science? |
| 2.4. | Materials Informatics - Category definitions |
| 2.5. | The broader informatics space in science and engineering |
| 2.6. | Key challenges for MI across the full materials spectrum |
| 2.7. | Closing the loop on traditional synthetic approaches |
| 2.8. | High Throughput Virtual Screening (HTVS) |
| 2.9. | Advantages of ML for chemistry and materials science - Acceleration |
| 2.10. | Advantages of ML for chemistry and materials science - Scoping and screening |
| 2.11. | Advantages of ML for chemistry and materials science - Scoping and screening (2) |
| 2.12. | Advantages of ML for chemistry and materials science - New species and relationships |
| 2.13. | Data infrastructures for chemistry and materials science |
| 2.14. | ELN/LIMS Software and Materials Informatics |
| 3. | TECHNOLOGY ASSESSMENT |
| 3.1. | Overview |
| 3.1.1. | Inputs and outputs of materials informatics algorithms |
| 3.1.2. | What is needed for materials informatics? |
| 3.1.3. | Summary of technology approaches |
| 3.1.4. | Uncertainty in experimental data undermines analysis |
| 3.1.5. | QSAR and QSPR: relating structure to properties |
| 3.2. | MI algorithms |
| 3.2.1. | Overview of MI algorithms |
| 3.2.2. | Problems with materials science data |
| 3.2.3. | Descriptors and training a model |
| 3.2.4. | Describing materials to a computer (I) |
| 3.2.5. | Describing materials to a computer (II) |
| 3.2.6. | Types of MI algorithms - Supervised vs unsupervised |
| 3.2.7. | Problem classes in supervised and unsupervised learning |
| 3.2.8. | Reinforcement learning: Learning by trial and error |
| 3.2.9. | Automated feature selection |
| 3.2.10. | Exploitation vs exploration: Use what you know or look into new areas? |
| 3.2.11. | Pure exploitation vs epsilon-greedy policies in materials informatics |
| 3.2.12. | Active learning and MI: Choosing experiments to maximize improvement |
| 3.2.13. | Supervised learning models: "More sophisticated" is not always better |
| 3.2.14. | Bayesian optimization: A versatile tool in machine learning |
| 3.2.15. | Genetic algorithms: Mimicking natural selection |
| 3.2.16. | Unsupervised learning case study - Mapping phases |
| 3.2.17. | Deep learning: Imitating the brain |
| 3.2.18. | Deep learning: Types of neural network |
| 3.2.19. | Generative vs discriminative algorithms - Explaining vs labelling |
| 3.2.20. | Transformer models are at the core of the AI boom |
| 3.2.21. | Generative algorithms in materials informatics: case study |
| 3.2.22. | Deep learning: An example in MI |
| 3.2.23. | Generative models for inorganic compounds (I) |
| 3.2.24. | Generative models for inorganic compounds (II): Generative adversarial networks |
| 3.2.25. | How to work with small material datasets |
| 3.2.26. | Deep learning with small material datasets: examples (I) |
| 3.2.27. | Deep learning with small material datasets: examples (II) |
| 3.2.28. | Large Language Models (LLMs) and Materials R&D |
| 3.2.29. | Capabilities of LLMs in science |
| 3.2.30. | Summary: Key areas of algorithm advancements in MI |
| 3.3. | Establishing a data infrastructure |
| 3.3.1. | A data infrastructure is critical for MI |
| 3.3.2. | Developments targeted for chemical and materials science |
| 3.3.3. | ELN/LIMS, materials informatics and managing R&D processes |
| 3.4. | External databases |
| 3.4.1. | Data repositories - Organizations |
| 3.4.2. | Leveraging data repositories |
| 3.4.3. | Text extraction and analysis |
| 3.4.4. | ChemDataExtractor V1.0: Data mining publications and patents |
| 3.4.5. | ChemDataExtractor V2.0: Mining relational data |
| 3.4.6. | Annotating and extracting the relevant information: The commercial space |
| 3.5. | MI with physical experiments and characterization |
| 3.5.1. | Achieving high-volumes of physical experimental data |
| 3.5.2. | Achieving high-volumes of physical experimental data (2) |
| 3.5.3. | High-throughput spectroscopy |
| 3.5.4. | In-situ spectroscopy developments |
| 3.6. | MI with computational materials science |
| 3.6.1. | Simulations for chemistry and materials science R&D |
| 3.6.2. | Density functional theory (DFT) - Quantum mechanical modelling for CAMD |
| 3.6.3. | Surrogate models reduce the computational expense of atomistic simulation |
| 3.6.4. | Simulating matter across the length scale continuum: multiscale modelling |
| 3.6.5. | ICME and the role of machine learning |
| 3.6.6. | ICME: Why is it important? |
| 3.6.7. | QuesTek Innovations and ICME: from service to SaaS |
| 3.6.8. | Thermo-Calc and CompuTherm: ICME software provision and QuesTek collaboration |
| 3.6.9. | Generating and using the largest computational materials science database |
| 3.6.10. | Explorative design utilizing cloud-based simulation |
| 3.6.11. | The potential in leveraging quantum computing |
| 3.6.12. | Computation autonomy for materials discovery |
| 3.6.13. | Summary: simulation data is an important input to MI processes |
| 3.7. | Autonomous labs |
| 3.7.1. | The future - fully autonomous labs |
| 3.7.2. | The future - "Chemputer" |
| 3.7.3. | DeepMatter and the Chemputer |
| 3.7.4. | Workflow management for laboratory automation |
| 3.7.5. | Autonomous high throughput experimentation |
| 3.7.6. | Commercial self-driving-laboratories |
| 3.7.7. | Gearu: attempting to commercialize mobile autonomous robotic scientists |
| 3.7.8. | Retrosynthesis through to robot execution |
| 3.7.9. | Technology pillars for chemical autonomy |
| 4. | INDUSTRY ANALYSIS |
| 4.1. | Overview |
| 4.1.1. | Materials Informatics: the state of the industry in 2024 |
| 4.2. | Strategic approaches to MI |
| 4.2.1. | Materials informatics players - categories |
| 4.2.2. | Conclusions and outlook for strategic approaches: approaches for end-users (I) |
| 4.2.3. | Conclusions and outlook for strategic approaches: approaches for end-users (II) |
| 4.2.4. | Conclusions and outlook for strategic approaches; approaches for external materials informatics companies (I) |
| 4.2.5. | Conclusions and outlook for strategic approaches; approaches for external materials informatics companies (II) |
| 4.3. | Player analysis |
| 4.3.1. | Materials informatics players - overview |
| 4.3.2. | Key Partners and Customers of External Providers |
| 4.3.3. | Partnerships with engineering simulation software |
| 4.3.4. | Funding raised by private companies (I): in-house development leads to high capital requirements |
| 4.3.5. | Funding raised by private companies (II): the AI boom may be raising interest in MI |
| 4.3.6. | NobleAI: MI, Microsoft, the AI boom and cloud marketplaces |
| 4.3.7. | Main industry players (I): Established leaders |
| 4.3.8. | Main industry players (II): Promising challengers |
| 4.3.9. | Major MI players: on a path to profitability? |
| 4.3.10. | What are the barriers to profitability for MI SaaS players? |
| 4.3.11. | Taking materials informatics in-house |
| 4.3.12. | Offering in-housed operations as a service |
| 4.3.13. | Taking the operation in-house: What needs to happen first? |
| 4.3.14. | Enthought: Digital transformation in scientific/engineering R&D |
| 4.3.15. | Resonac/Showa Denko - from external engagements to in-housed MI strategy? |
| 4.3.16. | Retrosynthesis prediction: "Can I make this compound?" |
| 4.3.17. | Commercial retrosynthesis predictors |
| 4.3.18. | Notable MI consortia (1) - NIMS and Materials Open Platforms |
| 4.3.19. | Notable MI consortia (2) - AIST Data-Driven Consortium |
| 4.3.20. | Notable MI consortia (3) - Toyota Research Institute and university collaboration |
| 4.3.21. | Notable MI consortia (4) - The Global Acceleration Network |
| 4.3.22. | Notable past MI consortia (1) - IBM collaborations |
| 4.3.23. | Notable past MI consortia (2): CHiMaD and the CMD Network |
| 4.3.24. | Public-private collaborations |
| 4.3.25. | The Open Catalyst Project: Crowdsourcing MI |
| 4.3.26. | Materials Genome Initiative (MGI) |
| 4.3.27. | Materials Genome Engineering (MGE) or National Materials Genome Project (China) |
| 4.3.28. | Additional key initiatives and research centers around the world (1) |
| 4.3.29. | Additional key initiatives and research centers around the world (2) |
| 4.3.30. | Conclusion: for MI end-users, there is no one-size-fits-all approach |
| 4.4. | Applications of materials informatics |
| 4.4.1. | Project categories in MI |
| 4.4.2. | Application Progression |
| 4.4.3. | Materials informatics roadmap |
| 4.5. | Market forecast and outlook |
| 4.5.1. | Signs of growth in the MI industry |
| 4.5.2. | Market forecast: external materials informatics players |
| 4.5.3. | Forecast data and market outlook |
| 4.6. | MI industry player data |
| 4.6.1. | Lists of MI players |
| 4.6.2. | Full player list - Commercial companies (confirmed operational) (1) |
| 4.6.3. | Full player list - Commercial companies (confirmed operational) (2) |
| 4.6.4. | Full player list - Commercial companies (confirmed operational) (3) |
| 4.6.5. | Full player list - Commercial companies (confirmed operational) (4) |
| 4.6.6. | Full player list - Commercial companies (confirmed operational) (5) |
| 4.6.7. | Full player list - Commercial companies (confirmed operational) (6) |
| 4.6.8. | Full player list - Commercial companies (confirmed operational) (7) |
| 4.6.9. | Full player list - Commercial companies (confirmed operational) (8) |
| 4.6.10. | Full player list - Commercial companies (confirmed operational) (9) |
| 4.6.11. | Full player list - Commercial companies (likely industry leavers in 2023) |
| 4.6.12. | Player list - Public organizations (I) |
| 4.6.13. | Player list - Public organizations (II) |
| 5. | COMPANY PROFILES |
| 5.1. | Albert Invent |
| 5.2. | Alchemy Cloud |
| 5.3. | Ansatz AI |
| 5.4. | Citrine Informatics (2020) |
| 5.5. | Citrine Informatics (2022) |
| 5.6. | Citrine Informatics (2023/4 update) |
| 5.7. | Copernic Catalysts |
| 5.8. | Cynora |
| 5.9. | Elix, Inc |
| 5.10. | Enthought |
| 5.11. | Exomatter |
| 5.12. | Exponential Technologies |
| 5.13. | FEHRMANN MaterialsX |
| 5.14. | Fluence Analytics |
| 5.15. | Intellegens |
| 5.16. | Kebotix (2020) |
| 5.17. | Kebotix (2022) |
| 5.18. | Kyulux |
| 5.19. | MaterialsIn |
| 5.20. | Materials Zone (2020) |
| 5.21. | Materials Zone (2022) |
| 5.22. | Matmerize |
| 5.23. | META |
| 5.24. | OTI Lumionics |
| 5.25. | Phaseshift Technologies |
| 5.26. | Polymerize |
| 5.27. | Preferred Computational Chemistry/Matlantis |
| 5.28. | QuesTek Innovations LLC |
| 5.29. | Schrödinger |
| 5.30. | Stoicheia |
| 5.31. | Uncountable (2020) |
| 5.32. | Uncountable (2022) |
| 5.33. | Xinterra |
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Werkstoffinformatik 2024-2034: Märkte, Strategien, Akteure
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Report Statistics
| Slides | 186 |
|---|---|
| Companies | 33 |
| Forecasts to | 2034 |
| Published | Feb 2024 |
| ISBN | 9781835700198 |
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