1. | EXECUTIVE SUMMARY |
1.1. | What is materials informatics? |
1.2. | Materials Informatics: the state of the industry in 2025 |
1.3. | AI opportunities at every stage of materials design and development |
1.4. | Problems with materials science data |
1.5. | Exploratory machine learning workflow |
1.6. | Types of MI algorithms - Supervised vs unsupervised |
1.7. | Foundation models and materials informatics |
1.8. | Capabilities of LLMs in science |
1.9. | Algorithmic approaches in MI are diverse |
1.10. | Materials informatics players - categories |
1.11. | Conclusions and outlook for strategic approaches: approaches for end-users (I) |
1.12. | Conclusions and outlook for strategic approaches: approaches for end-users (II) |
1.13. | For MI end-users, there is no one-size-fits-all approach |
1.14. | Key Partners and Customers of Selected External Providers |
1.15. | Funding raised by private companies (I): in-house development leads to high capital requirements |
1.16. | Funding raised by private companies (II): is faith in SaaS business models waning? |
1.17. | Lila Sciences: the largest funding raise in MI to date |
1.18. | Main industry players (I): Established leaders |
1.19. | Main industry players (II): Promising challengers |
1.20. | Major MI players: on a path to profitability? |
1.21. | Market outlook for external MI companies |
1.22. | Notable MI consortia |
1.23. | Project categories in MI |
1.24. | Application Progression |
1.25. | Materials informatics roadmap |
1.26. | Market forecast: external materials informatics players |
2. | INTRODUCTION |
2.1. | What is materials informatics? |
2.2. | Materials informatics - Why now? |
2.3. | Materials Informatics - Category definitions |
2.4. | The broader informatics space in science and engineering |
2.5. | Key challenges for MI across the full materials spectrum |
2.6. | Closing the loop on traditional synthetic approaches |
2.7. | High Throughput Virtual Screening (HTVS) |
2.8. | Advantages of ML for chemistry and materials science - Acceleration |
2.9. | Advantages of ML for chemistry and materials science - Scoping and screening |
2.10. | Advantages of ML for chemistry and materials science - Scoping and screening (2) |
2.11. | Advantages of ML for chemistry and materials science - New species and relationships |
2.12. | Data infrastructures for chemistry and materials science |
2.13. | ELN/LIMS Software and Materials Informatics |
2.14. | Proving the value of materials informatics |
3. | TECHNOLOGY ASSESSMENT |
3.1. | Introduction |
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. | Exploratory machine learning workflow |
3.2.4. | Descriptors and training a model |
3.2.5. | Describing materials to a computer (I) |
3.2.6. | Describing materials to a computer (II) |
3.2.7. | Types of MI algorithms - Supervised vs unsupervised |
3.2.8. | Problem classes in supervised and unsupervised learning |
3.2.9. | Reinforcement learning: Learning by trial and error |
3.2.10. | Automated feature selection |
3.2.11. | Exploitation vs exploration: Use what you know or look into new areas? |
3.2.12. | Pure exploitation vs epsilon-greedy policies in materials informatics |
3.2.13. | Active learning and MI: Choosing experiments to maximize improvement |
3.2.14. | Supervised learning models: "More sophisticated" is not always better |
3.2.15. | Bayesian optimization: A versatile tool in machine learning |
3.2.16. | Genetic algorithms: Mimicking natural selection |
3.2.17. | Unsupervised learning case study - Mapping phases |
3.2.18. | Deep learning: Imitating the brain |
3.2.19. | Deep learning: Types of neural network |
3.2.20. | Characterizing and computing neural networks |
3.2.21. | Generative vs discriminative algorithms - Explaining vs labelling |
3.2.22. | "Generative AI" is distinct from generative algorithms |
3.2.23. | Generative algorithms in materials informatics: case study |
3.2.24. | Deep learning: An example in MI |
3.2.25. | Physics-Informed Neural Networks (PINNs) in material development |
3.2.26. | PINN applications in materials informatics |
3.2.27. | Generative models for inorganic compounds (I) |
3.2.28. | Generative models for inorganic compounds (II): Generative adversarial networks |
3.2.29. | Transformer models are at the core of the AI boom |
3.2.30. | Foundation models and materials informatics |
3.2.31. | Foundation models in materials informatics: experimental data |
3.2.32. | Data availability and computational expense hold back foundation model deployment |
3.2.33. | Large Language Models (LLMs) and Materials R&D |
3.2.34. | Capabilities of LLMs in science |
3.2.35. | Task-based machine learning is harder to avoid in materials science than many areas of machine learning interest |
3.2.36. | How to work with small material datasets |
3.2.37. | Deep learning with small material datasets: examples (I) |
3.2.38. | Deep learning with small material datasets: examples (II) |
3.2.39. | AutoML: democratizing machine learning |
3.2.40. | Multi-model ensembles: combining multiple predictive methodologies |
3.2.41. | Summary: Algorithmic approaches in MI are diverse |
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. | ChemDataExtractor V1.0: Data mining publications and patents |
3.4.4. | ChemDataExtractor V2.0: Mining relational data |
3.4.5. | Annotating and extracting the relevant information: The commercial space |
3.4.6. | LLMs expand material data mining capabilities |
3.5. | MI with computational materials science |
3.5.1. | Simulations for chemistry and materials science R&D |
3.5.2. | Density functional theory (DFT) - Quantum mechanical modelling for CAMD |
3.5.3. | Surrogate models reduce the computational expense of atomistic simulation |
3.5.4. | Simulating matter across the length scale continuum: multiscale modelling |
3.5.5. | ICME and the role of machine learning |
3.5.6. | ICME: Why is it important? |
3.5.7. | QuesTek Innovations and ICME: from service to SaaS |
3.5.8. | Thermo-Calc and CompuTherm: ICME software provision and QuesTek collaboration |
3.5.9. | Explorative design utilizing cloud-based simulation |
3.5.10. | The potential in leveraging quantum computing |
3.5.11. | Computation autonomy for materials discovery |
3.5.12. | Big Tech, computational materials science and materials informatics |
3.5.13. | Summary: simulation data is an important input to MI processes |
3.6. | MI with physical experiments and characterization |
3.6.1. | Achieving high-volumes of physical experimental data |
3.6.2. | Achieving high-volumes of physical experimental data (2) |
3.6.3. | In-situ spectroscopy developments |
3.6.4. | Why is high throughput screening in materials tougher than other areas of science? |
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. | Materials Informatics: the state of the industry in 2025 |
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 Selected 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): is faith in SaaS business models waning? |
4.3.6. | Lila Sciences: the largest funding raise in MI to date |
4.3.7. | NobleAI: MI, Microsoft, the AI boom and cloud marketplaces |
4.3.8. | Main industry players (I): Established leaders |
4.3.9. | Main industry players (II): Promising challengers |
4.3.10. | Major MI players: on a path to profitability? |
4.3.11. | Pricing MI SaaS platforms |
4.3.12. | Risks for SaaS business models in MI |
4.3.13. | What are the barriers to profitability for MI SaaS players? |
4.3.14. | Microsoft's Azure Quantum Elements: stiff competition for smaller MI players |
4.3.15. | Applications of Azure Quantum Elements |
4.3.16. | Taking materials informatics in-house |
4.3.17. | Offering in-housed operations as a service |
4.3.18. | Taking the operation in-house: What needs to happen first? |
4.3.19. | Enthought: Digital transformation in scientific/engineering R&D |
4.3.20. | Resonac/Showa Denko - from external engagements to in-housed MI strategy? |
4.3.21. | Retrosynthesis prediction: "Can I make this compound?" |
4.3.22. | Commercial retrosynthesis predictors |
4.3.23. | Notable MI consortia (1) - NIMS and Materials Open Platforms |
4.3.24. | Notable MI consortia (2) - AIST Data-Driven Consortium |
4.3.25. | Notable MI consortia (3) - Toyota Research Institute and university collaboration |
4.3.26. | Notable MI consortia (4) - The Global Acceleration Network |
4.3.27. | Notable past MI consortia (1) - IBM collaborations |
4.3.28. | Notable past MI consortia (2): CHiMaD and the CMD Network |
4.3.29. | Public-private collaborations |
4.3.30. | The Open Catalyst Project: Crowdsourcing MI |
4.3.31. | Materials Genome Initiative (MGI) |
4.3.32. | Materials Genome Engineering (MGE) or National Materials Genome Project (China) |
4.3.33. | Additional key initiatives and research centers around the world (1) |
4.3.34. | Additional key initiatives and research centers around the world (2) |
4.3.35. | 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. | Market forecast: external materials informatics players |
4.5.2. | 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 - Industry leavers (likely and confirmed) |
4.6.10. | Player list - Public organizations (I) |
4.6.11. | Player list - Public organizations (II) |
5. | COMPANY PROFILES |
5.1. | Albert Invent |
5.2. | Alchemy Cloud |
5.3. | Ansatz AI |
5.4. | Citrine Informatics |
5.5. | Citrine Informatics |
5.6. | Citrine Informatics: Update |
5.7. | Copernic Catalysts |
5.8. | Cynora |
5.9. | Dunia Innovations |
5.10. | Elix, Inc. |
5.11. | ExoMatter |
5.12. | Exponential Technologies |
5.13. | FEHRMANN MaterialsX |
5.14. | Fluence Analytics |
5.15. | Intellegens |
5.16. | Kebotix |
5.17. | Kebotix |
5.18. | Kyulux |
5.19. | LG AI Research |
5.20. | Materials Zone |
5.21. | Materials Zone |
5.22. | materialsIn |
5.23. | MaterialsZone |
5.24. | Matmerize |
5.25. | Metamaterial Technologies |
5.26. | NobleAI |
5.27. | OTI Lumionics |
5.28. | Phaseshift Technologies |
5.29. | Polymerize |
5.30. | Preferred Computational Chemistry (PFCC)/Matlantis |
5.31. | Preferred Computational Chemistry (PFCC)/MATLANTIS: Early 2025 Update |
5.32. | QuesTek Innovations LLC |
5.33. | Schrödinger |
5.34. | Stoicheia |
5.35. | Uncountable |
5.36. | Uncountable |
5.37. | Xinterra |