Materials Informatics 2025-2035: Markets, Strategies, Players

Data-centric approaches for design and discovery within materials science. Notable advancements in data infrastructures, machine learning, and AI. Player profiles, technology progression, market outlook, strategic approaches.

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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 primary interviews with 25 companies in the field, 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 2035, with 9.0% 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?
 
 
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, despite the scale of the current AI industrial revolution, is still 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, with potential future impact from foundation models for materials and chemistry.
  • 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 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 USA, and the most notable consortia and academic labs are split across Japan and the USA.
 
 
Image caption: Categorizing materials informatics industry players. Source: IDTechEx
 
Interview based profiles of many key companies are included within this IDTechEx report.
 
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 MetricsDetails
CAGRThe global market for external provision of materials informatics will reach US$725 million by 2034, representing 9.0% CAGR vs. 2025.
Forecast Period2025 - 2035
Forecast UnitsUS$
Regions CoveredWorldwide
Segments CoveredGlobal 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
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AMERICAS (USA): +1 617 577 7890
ASIA (Japan): +81 3 3216 7209
EUROPE (UK) +44 1223 812300
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
 

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Slides 205
Companies Over 35
Forecasts to 2035
Published May 2025
 

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