This report has been updated. Click here to view latest edition.
If you have previously purchased the archived report below then please use the download links on the right to download the files.
Informatica dei materiali 2023-2033
Approcci incentrati sui dati per la progettazione e la scoperta nell'ambito della ricerca e sviluppo della scienza dei materiali. Notevoli progressi nelle infrastrutture di dati e nell'apprendimento automatico. Profili dei giocatori, progressione tecnologica, prospettive di mercato, modelli di business e casi di studio
Show All
Description
Contents, Table & Figures List
FAQs
Pricing
Related Content
Materials informatics (MI) involves using data-centric approaches for materials science R&D. 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 24 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 2033, with 13.7% CAGR expected until then. Case studies in numerous applications highlight the wide range of areas in materials science where MI adds value. Analysis of the underlying technologies demystifies this fast-growing area of the R&D digital transformation.
Key areas of coverage in this report. Source: IDTechEx
What is materials informatics?
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.
- 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.
IDTechEx have classified the projects undertaken into eight main categories outlined in detail within the report. Within that, there are three repeated advantages to employing advanced machine learning techniques into your 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 the USA, and the most notable consortia and academic labs are split across Japan and the USA.
Interview based profiles of all the key companies are included within this IDTechEx report.
Categorizing materials informatics industry players. Source: IDTechEx
Where is materials informatics being applied?
Organic electronics, battery compositions, additive manufacturing alloys, polyurethane formulations, and nanomaterial development are all examples of areas that MI is having an immediate impact on. The broad range of material use-cases means industrial adoption is being seen from electronics manufacturers to chemical companies.
There are universal challenges, but each application area will have certain considerations, be it in the availability of existing data, the domain knowledge, the complexity of the structure-property relationships, and more.
The final part of this report goes into detail on a comprehensive range of application areas in turn, highlighting key developments, commercial use-cases, and notable publications. This provides end-users the opportunity to focus on case studies in their specific areas of interest and MI players to what areas to explore.
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 headcount of the average MI firm growing by 91% from 2021-22. 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 consortium 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.
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.
- Key academic and industrial progressions highlighted.
Company Analysis:
- Comprehensive list, details and differentiating features of all MI companies.
- Interview-based company profiles for 24 players.
- 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.
- Case studies and success stories across the R&D of numerous advanced materials and emerging applications.
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: +44 1223 810259
| 1. | EXECUTIVE SUMMARY AND CONCLUSIONS |
| 1.1. | What is materials informatics? |
| 1.2. | Overview of significant industry activity |
| 1.3. | Latest key news and developments |
| 1.4. | AI opportunities at every stage of materials design and development |
| 1.5. | Problems with materials science data |
| 1.6. | Key areas of algorithm advancements |
| 1.7. | Materials informatics players - Categories |
| 1.8. | Conclusions and outlook for strategic approaches |
| 1.9. | Main players |
| 1.10. | Key partners and customers of external providers |
| 1.11. | Notable MI consortia |
| 1.12. | Project categories |
| 1.13. | Market forecast |
| 1.14. | Company Profiles |
| 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 |
| 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: The role of regression analysis (1) |
| 3.1.6. | QSAR and QSPR: The role of regression analysis (2) |
| 3.2. | MI algorithms |
| 3.2.1. | Overview of MI algorithms |
| 3.2.2. | Descriptors and training a model |
| 3.2.3. | Types of MI algorithms - Supervised vs unsupervised |
| 3.2.4. | Problem classes in supervised and unsupervised learning |
| 3.2.5. | Reinforcement learning: Learning by trial and error |
| 3.2.6. | Describing materials to a computer |
| 3.2.7. | Automated feature selection |
| 3.2.8. | Exploitation vs exploration: Use what you know or look into new areas? |
| 3.2.9. | Pure exploitation vs epsilon-greedy policies in materials informatics |
| 3.2.10. | Active learning and MI: Choosing experiments to maximize improvement |
| 3.2.11. | Supervised learning models: "More sophisticated" is not always better |
| 3.2.12. | Bayesian optimization: A versatile tool in machine learning |
| 3.2.13. | Genetic algorithms: Mimicking natural selection |
| 3.2.14. | Unsupervised learning case study - Mapping phases |
| 3.2.15. | Generative vs discriminative algorithms - Explaining vs labelling |
| 3.2.16. | Generative algorithms in practice: Case study |
| 3.2.17. | Deep learning: Imitating the brain |
| 3.2.18. | Deep learning: Types of neural network |
| 3.2.19. | Deep learning: An example in MI |
| 3.2.20. | Generative models for inorganic compounds (I) |
| 3.2.21. | Generative models for inorganic compounds (II): Generative adversarial networks |
| 3.2.22. | How to work with small material datasets |
| 3.2.23. | Deep learning with small material datasets |
| 3.2.24. | Key areas of algorithm advancements |
| 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 |
| 3.4. | 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. | Simulating matter across the length scale continuum: multiscale modelling |
| 3.6.4. | ICME and the role of machine learning |
| 3.6.5. | ICME: Why is it important? |
| 3.6.6. | QuesTek Innovations and ICME: from service to SaaS |
| 3.6.7. | Thermo-Calc and CompuTherm: ICME software provision and QuesTek collaboration |
| 3.6.8. | Generating and using the largest computational materials science database |
| 3.6.9. | Explorative design utilizing cloud-based simulation |
| 3.6.10. | The potential in leveraging quantum computing |
| 3.6.11. | Computation autonomy for materials discovery |
| 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. | Mobile autonomous robots in academia |
| 3.7.8. | Gearu: commercializing mobile autonomous robotic scientists |
| 3.7.9. | Retrosynthesis through to robot execution |
| 3.7.10. | Technology pillars for chemical autonomy |
| 4. | COMPANY ANALYSIS |
| 4.1. | Overview of significant industry activity |
| 4.2. | Latest key news and developments |
| 4.3. | Materials informatics players - categories |
| 4.4. | Conclusions and outlook for strategic approaches |
| 4.5. | Conclusions and outlook for strategic approaches |
| 4.6. | Materials informatics players - overview |
| 4.7. | Key partners and customers of external providers |
| 4.8. | Partnerships with engineering simulation software |
| 4.9. | Funding raised by private companies |
| 4.10. | The MI industry is in a growth phase |
| 4.11. | Signs of growth from major players |
| 4.12. | Lists of MI players |
| 4.13. | Full player list - Commercial companies (confirmed operational) |
| 4.14. | Full player list - Commercial companies (confirmed and likely industry leavers) |
| 4.15. | Company Profiles |
| 4.16. | Main players |
| 4.17. | Full player list - Public organizations (I) |
| 4.18. | Full player list - Public organizations (II) |
| 4.19. | Taking the operation in-house |
| 4.20. | Offering in-housed operations as a service |
| 4.21. | Taking the operation in-house: What needs to happen first? |
| 4.22. | Enthought: Digital transformation in scientific/engineering R&D |
| 4.23. | Retrosynthesis prediction: "Can I make this compound?" |
| 4.24. | Commercial retrosynthesis predictors |
| 4.25. | Notable MI consortia (1) - NIMS and Materials Open Platforms |
| 4.26. | Notable MI consortia (2) - Toyota Research Institute and university collaboration |
| 4.27. | Notable MI consortia (3) - The Global Acceleration Network |
| 4.28. | Notable MI consortia (4) - IBM collaborations |
| 4.29. | Notable MI consortia (5): ChiMaD and the CMD Network |
| 4.30. | Public-private collaborations |
| 4.31. | Materials Genome Initiative (MGI) |
| 4.32. | Materials Genome Engineering (MGE) |
| 4.33. | Additional key initiatives and research centres around the world |
| 4.34. | Materials development via synthetic biology |
| 5. | APPLICATIONS AND CASE STUDIES |
| 5.1. | Case studies - Overview |
| 5.2. | Market forecast |
| 5.3. | Materials informatics roadmap |
| 5.4. | Project categories |
| 5.5. | Materials informatics - Market penetration by maturity |
| 5.6. | Microscopy: Accelerating process and synthetic uses |
| 5.7. | Improving the use of synchrotron light sources |
| 5.8. | Aluminum and titanium alloys |
| 5.9. | Metallic glass alloys |
| 5.10. | Nickel-base superalloys |
| 5.11. | High-entropy alloys |
| 5.12. | Intermetallics |
| 5.13. | Coatings |
| 5.14. | Organic electronics |
| 5.15. | Organic electronics - RFID |
| 5.16. | Organic electronics - OPV |
| 5.17. | Organic electronics - Emerging areas |
| 5.18. | Catalysts (1) |
| 5.19. | Catalysts (2) |
| 5.20. | Catalysts (3) |
| 5.21. | Ionic liquids |
| 5.22. | Superconductors |
| 5.23. | Toxic chemicals and toxicity prediction |
| 5.24. | Energy storage: Lithium-ion batteries |
| 5.25. | Polymers and composites |
| 5.26. | Polymer Informatics |
| 5.27. | Lubricants and related areas |
| 5.28. | Thermoelectrics |
| 5.29. | Organometallics |
| 5.30. | 2D materials |
| 5.31. | Nanofabrication |
| 5.32. | Quantum Dots |
| 5.33. | Other nanomaterials |
| 5.34. | Metal-insulator transition compounds |
| 5.35. | Light absorbers and solar cells |
| 5.36. | Perovskite photovoltaics |
| 5.37. | Self-assembled monolayers |
| 5.38. | Metamaterials |
| 6. | COMPANY PROFILES |
| 6.1. | Alchemy Cloud |
| 6.2. | Ansatz AI |
| 6.3. | Citrine Informatics (2020) |
| 6.4. | Citrine Informatics (2022) |
| 6.5. | Cynora |
| 6.6. | Elix, Inc |
| 6.7. | Enthought |
| 6.8. | Exponential Technologies |
| 6.9. | Fluence Analytics |
| 6.10. | Intellegens (2019) |
| 6.11. | Intellegens (2020) |
| 6.12. | Kebotix (2020) |
| 6.13. | Kebotix (2022) |
| 6.14. | Kyulux |
| 6.15. | MaterialsIn |
| 6.16. | Materials Zone (2020) |
| 6.17. | Materials Zone (2022) |
| 6.18. | Matmerize |
| 6.19. | META |
| 6.20. | OTI Lumionics |
| 6.21. | Phaseshift Technologies |
| 6.22. | Polymerize |
| 6.23. | QuesTek Innovations LLC |
| 6.24. | Schrödinger |
| 6.25. | Stoicheia |
| 6.26. | Uncountable (2020) |
| 6.27. | Uncountable (2022) |
| 6.28. | Xinterra |
| 6.29. | Zymergen |
Materials informatics provider revenue to grow at 13.7% CAGR to 2033, transforming R&D
Report Statistics
| Slides | 205 |
|---|---|
| Forecasts to | 2033 |
Customer Testimonial
"The resources produced by IDTechEx are a valuable tool... Their insights and analyses provide a strong foundation for making informed, evidence-based decisions. By using their expertise, we are better positioned to align our strategies with emerging opportunities."