Dublin, May 20, 2026 (GLOBE NEWSWIRE) -- The "The Global Materials Informatics Market 2026-2036" report has been added to ResearchAndMarkets.com's offering.
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.
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 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 Highlights
- 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
Materials Informatics: State of the Industry in 2026
- Issues with Materials Science Data
- Dealing with Little or Sparse Data
- Key Technologies Driving Materials Informatics
- Importance in Modern Materials Science and Engineering
- Market Challenges and Restraints
- Recent Industry Developments
- The AI Boom and Its Impact on Materials Informatics
- Foundation Models, Generative AI and Materials Discovery
- Big Tech Entry into Materials Informatics
- Market Players
- Funding Landscape: Mega-Rounds and SaaS Headwinds
Future Markets Outlook and Opportunities
- Integration of AI and Robotics in Materials Labs
- Self-Driving Laboratories and Autonomous Science Platforms
- Quantum Machine Learning for Materials Discovery
- Blockchain for Materials Data Provenance
- Edge Computing in Materials Informatics
- Augmented and Virtual Reality in Materials Design
- Neuromorphic Computing for Materials Modeling
- Materials Informatics as a Service (MIaaS)
- Integration with Internet of Things (IoT)
- Green Technology and Circular Economy Applications
- Agentic AI Scientists
Economic Impact Analysis
- Cost Savings in Materials R&D
- Accelerated Time-to-Market for New Materials
- Job Creation and Skill Development
- Impact on Traditional Materials Industries
Companies Featured
- 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
- Mat3ra
- MaterialsZone
- Matmerize Inc.
- META
- Microsoft
- N-ERGY
- Noble.AI
- Novyte Materials
For more information about this report visit https://www.researchandmarkets.com/r/17i2g2
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