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市場調查報告書
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1510802

全球材料資訊學（MI）市場（2026-2036 年）

The Global Materials Informatics Market 2026-2036
出版日期: | 出版商: Future Markets, Inc. | 英文 190 Pages, 31 Tables, 20 Figures | 訂單完成後即時交付

價格

自設計數位化以來，材料資訊學（MI）已成為工業研發領域最重要的變革之一。基於材料科學、資料科學和人工智慧的融合，MI 利用機器學習、高通量計算、生成模型和大規模語言模型，大幅縮短了新材料的發現和最佳化時間及成本。如今，業界從業人員普遍反映，新材料開發所需的實體實驗數量減少了 50% 至 70%，產品上市時間從數月縮短至數年。過去需要數十年反覆試驗完成的工作，現在只需在資料驅動的工作流程指導下，於 2 至 5 年內即可完成。

市場已經歷了從早期採用者階段（2014-2018年）到成長階段（2019-2023年）的轉變，並進入了始於2024年的人工智慧熱潮加速階段，這一階段也成為當今產業的特徵。三個因素將影響2026年的產業前景。首先，最初為語言和視覺領域開發的基礎模型、Transformer架構、生成擴散模型和通用機器學習原子間勢能等技術已正式應用於材料科學領域。其次，微軟、GoogleDeepMind、MetaFAIR、IBM研究院和英偉達等大型科技公司正作為直接競爭對手和基礎設施供應商進入該領域，重塑專業機器學習供應商的競爭格局。第三，大規模資金籌措正在進行中，光是Lila Sciences一家公司就計劃在2026年第一季籌集約5.5億美元，用於推進生命科學、化學和材料科學領域全自動實驗室的建設。

如今，材料智慧（MI）的應用已成為主流。幾乎所有大型材料相關企業都在透過外部服務供應商、參與聯盟或內部專案等方式應用MI。經營團隊要求展示人工智慧（AI）對整個業務的影響，科學家自下而上開展的先導計畫也屢見不鮮。以永續性為導向的應用（綠氫催化劑、碳捕獲吸附劑、低壓紋碳水泥、可回收聚合物、PFAS替代品、能量轉換電池、燃料電池材料）仍然是最大的應用促進因素，並且到2036年，其在專案支出中所佔的佔有率將繼續成長。

本報告考察了全球材料資訊學（MI）市場，分析了定義現代MI產業的技術、經營模式、應用和主要企業。

目錄

第1章執行摘要

  • 材料資訊學（MI）
  • 材料資訊學（MI）：產業現況（2026 年）
  • 與材料科學數據相關的問題
  • 處理少量數據或稀疏數據
  • 推動材料資訊學（MI）發展的關鍵技術
  • 它在現代材料科學與工程中的重要性
  • 市場挑戰與限制因素
  • 近期產業發展動態
  • 人工智慧熱潮對材料資訊學（MI）的影響
  • 基礎模型、生成式人工智慧、材料發現
  • 大型科技公司進軍材料資訊學（MI）市場。
  • 參與企業
  • 資金籌措現況：巨額融資輪次與SaaS產業面臨的挑戰
  • 未來市場展望及機遇
  • MI藍圖
  • 經濟影響分析
  • 永續性與環境
  • 地緣政治考量：美國、歐盟、中國、日本、韓國
  • 全球市場預測

第2章：引言

  • 資料科學時代
  • MI出場的背景
  • 材料資訊學（MI）發展動機
  • 將人工智慧融入材料科學與工程
  • 材料科學數據有問題
  • 演算法的進步
  • 材料資訊學（MI）類別
  • 科學技術領域資料驅動方法的發展趨勢
  • 任務
  • 機器學習的優勢
  • 化學和材料科學中使用的數據基礎設施
  • ELN/LIMS 軟體和材料資訊學 (MI)
  • 材料資訊學（MI）價值的證明：案例研究

第3章 技術分析

  • 概述
  • 技術方法
  • MI演算法
  • 數據基礎設施
  • 資料庫和外部儲存庫
  • 從資料庫到巨量資料
  • 材料資訊學（MI）中的小數據策略
  • 透過物理實驗和表徵進行MI
  • 計算材料科學
  • 自主實驗與自動駕駛實驗室
  • 多模態資料整合
  • 材料表徵中的反問題
  • 資料驅動的實驗設計
  • 自動化數據分析與解讀
  • 材料研究中的機器人與自動化
  • 數位雙胞胎在材料與程式工程的應用

第4章：材料資訊學（MI）的應用

  • 合金設計與最佳化
  • 藥物發現與開發
  • 金屬間化合物
  • 有機金屬化合物
  • 有機電子
  • 塗料和油漆
  • 催化劑
  • 離子液體
  • 電池材料
  • 高密度儲熱材料
  • 氫基超導性
  • 碳回收吸附劑
  • 聚合物資訊學
  • 橡膠加工
  • 奈米材料
  • 2D材料
  • 超材料
  • 潤滑劑
  • 熱電材料
  • 太陽能
  • 金屬-絕緣體過渡化合物
  • 自組裝單層
  • 建築材料及水泥
  • 生物材料
  • 量子技術材料
  • 用於國防和極端環境的材料
  • PFAS替代材料
  • 必需礦物質和稀土元素的替代品

第5章 產業分析

  • 材料資訊學（MI）：產業現況（2026 年）
  • 管理資訊系統的策略方法
  • 公司分析
  • MI聯盟和公私合作項目
  • 密西根州的企業計劃
  • 戰略合作與協議（2024-2026）
  • 地緣政治、出口管制、軍事情報
  • 材料資訊學（MI）的應用
  • 市場預測與展望
  • 參與企業數據。

第6章：公司簡介（53家公司簡介）

第7章：調查方法

第8章參考文獻

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 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.

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 Contents

  • 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

Companies Profiled include 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, MaterialsZone, Matmerize Inc, Mat3ra, META, Microsoft, N-ERGY, Noble.AI, Novyte Materials and more......

Table of Contents

1 EXECUTIVE SUMMARY

  • 1.1 What is Materials Informatics?
  • 1.2 Materials Informatics: State of the Industry in 2026
  • 1.3 Issues with Materials Science Data
  • 1.4 Dealing with Little or Sparse Data
  • 1.5 Key Technologies Driving Materials Informatics
  • 1.6 Importance in Modern Materials Science and Engineering
  • 1.7 Market Challenges and Restraints
  • 1.8 Recent Industry Developments
  • 1.9 The AI Boom and Its Impact on Materials Informatics
  • 1.10 Foundation Models, Generative AI and Materials Discovery
  • 1.11 Big Tech Entry into Materials Informatics
  • 1.12 Market Players
  • 1.13 Funding Landscape: Mega-Rounds and SaaS Headwinds
  • 1.14 Future Markets Outlook and Opportunities
    • 1.14.1 Integration of AI and Robotics in Materials Labs
    • 1.14.2 Self-Driving Laboratories and Autonomous Science Platforms
    • 1.14.3 Quantum Machine Learning for Materials Discovery
    • 1.14.4 Blockchain for Materials Data Provenance
    • 1.14.5 Edge Computing in Materials Informatics
    • 1.14.6 Augmented and Virtual Reality in Materials Design
    • 1.14.7 Neuromorphic Computing for Materials Modeling
    • 1.14.8 Materials Informatics as a Service (MIaaS)
    • 1.14.9 Integration with Internet of Things (IoT)
    • 1.14.10 Green Technology and Circular Economy Applications
    • 1.14.11 Agentic AI Scientists
  • 1.15 MI Roadmap
  • 1.16 Economic Impact Analysis
    • 1.16.1 Cost Savings in Materials R&D
    • 1.16.2 Accelerated Time-to-Market for New Materials
    • 1.16.3 Job Creation and Skill Development
    • 1.16.4 Impact on Traditional Materials Industries
  • 1.17 Sustainability and Environmental
    • 1.17.1 Role of Materials Informatics in Sustainable Development
    • 1.17.2 Reducing Environmental Impact of Materials Production
    • 1.17.3 Design for Recyclability and Circular Economy
    • 1.17.4 Bio-inspired Materials Discovery
    • 1.17.5 Materials for Energy Transition
  • 1.18 Geopolitical Considerations: U.S., EU, China, Japan, Korea
  • 1.19 Global Market Forecasts

2 INTRODUCTION

  • 2.1 Advent of the Data Science Era
  • 2.2 Background to the Emergence of MI
  • 2.3 Motivation for Materials Informatics Development
    • 2.3.1 Accelerating Discovery
    • 2.3.2 Cost Reduction
    • 2.3.3 Addressing Global Challenges
    • 2.3.4 Maximizing Data Value
    • 2.3.5 Handling Complexity
    • 2.3.6 Enabling Targeted Design (Inverse Design)
    • 2.3.7 Improving Reproducibility
    • 2.3.8 Integrating Multidisciplinary Knowledge
    • 2.3.9 Supporting Sustainability
    • 2.3.10 Competitive Advantage
  • 2.4 Integration of Artificial Intelligence (AI) into materials science and engineering
    • 2.4.1 AI Opportunities at Every Stage of Materials Design and Development
    • 2.4.2 The Transition from Predictive AI to Generative AI in Materials
    • 2.4.3 Physical AI: Models that Understand Physics and Chemistry
  • 2.5 Problems with Materials Science Data
  • 2.6 Algorithm Advancements
  • 2.7 Materials Informatics Categories
  • 2.8 Trend towards data-driven approaches in science and engineering
    • 2.8.1 Bioinformatics
    • 2.8.2 Cheminformatics
    • 2.8.3 Geoinformatics
    • 2.8.4 Health Informatics
    • 2.8.5 Environmental Informatics
    • 2.8.6 Astroinformatics
    • 2.8.7 Neuroinformatics
    • 2.8.8 Engineering Informatics
    • 2.8.9 Energy Informatics
    • 2.8.10 Quantum Informatics
  • 2.9 Challenges
  • 2.10 Advantages of Machine Learning
    • 2.10.1 Acceleration
    • 2.10.2 Scoping and Screening
    • 2.10.3 New Species and Relationships
    • 2.10.4 Closing the Loop on Traditional Synthetic Approaches
    • 2.10.5 High-Throughput Virtual Screening (HTVS)
  • 2.11 Data Infrastructures for Chemistry and Materials Science
  • 2.12 ELN/LIMS Software and Materials Informatics
  • 2.13 Proving the Value of Materials Informatics: Case Studies

3 TECHNOLOGY ANALYSIS

  • 3.1 Overview
    • 3.1.1 Inputs and Outputs of Materials Informatics Algorithms
    • 3.1.2 What is Needed for Materials Informatics?
  • 3.2 Technology approaches
    • 3.2.1 Summary of Technology Approaches
    • 3.2.2 Uncertainty in Experimental Data
    • 3.2.3 Data Mining
    • 3.2.4 Machine Learning and AI
    • 3.2.5 High-Throughput Computation
    • 3.2.6 Data Infrastructure
    • 3.2.7 Visualization Tools
    • 3.2.8 Reinforcement Learning
    • 3.2.9 Natural Language Processing
    • 3.2.10 Automated Experimentation
    • 3.2.11 Workflow Management
    • 3.2.12 Quantum Computing
    • 3.2.13 QSAR and QSPR
    • 3.2.14 Automated feature selection
    • 3.2.15 Exploitation vs exploration
    • 3.2.16 Pure exploitation vs epsilon-greedy policies in materials informatics
    • 3.2.17 Active learning and MI: Choosing experiments to maximize improvement
  • 3.3 MI Algorithms
    • 3.3.1 Overview of MI Algorithms
    • 3.3.2 Types of MI Algorithms
    • 3.3.3 Descriptors and Training a Model
    • 3.3.4 Supervised vs. Unsupervised Learning
    • 3.3.5 Automated Feature Selection
    • 3.3.6 Exploitation vs. Exploration; Active Learning
    • 3.3.7 Bayesian Optimization
    • 3.3.8 Genetic Algorithms
    • 3.3.9 Generative vs. Discriminative Algorithms
    • 3.3.10 Deep Learning and Neural Network Types
    • 3.3.11 Physics-Informed Neural Networks (PINNs)
    • 3.3.12 Graph Neural Networks (GNNs) for Materials
    • 3.3.13 Transformer Models and the AI Boom
    • 3.3.14 Foundation Models for Materials
      • 3.3.14.1 Definition and Architecture
      • 3.3.14.2 Foundation Models for Computational Data
      • 3.3.14.3 Foundation Models for Experimental Data
      • 3.3.14.4 Limitations: Data Availability and Compute Cost
    • 3.3.15 Generative Models for Inorganic Compounds
      • 3.3.15.1 Variational Autoencoders and GANs
      • 3.3.15.2 Diffusion Models for Crystal Generation
    • 3.3.16 Large Language Models (LLMs) and Materials R&D
      • 3.3.16.1 Capabilities of LLMs in Science
      • 3.3.16.2 LLM-Powered Material Data Mining
      • 3.3.16.3 Agentic LLMs and Autonomous Research
    • 3.3.17 AutoML: Democratizing Machine Learning
    • 3.3.18 Multi-Model Ensembles
    • 3.3.19 How to Work with Small Material Datasets
    • 3.3.20 Algorithmic Approaches in MI Are Diverse — Summary
  • 3.4 Data infrastructure
    • 3.4.1 Overview
    • 3.4.2 Developments Targeted for Chemical and Materials Science
    • 3.4.3 ELN/LIMS Integration with MI Workflows
  • 3.5 Databases and External Repositories
    • 3.5.1 Data Repositories — Organizations
    • 3.5.2 Leveraging Data Repositories
    • 3.5.3 The Materials Project, AFLOW, NOMAD, OQMD
    • 3.5.4 Meta's Open Materials 2024 (OMat24) Dataset
    • 3.5.5 GNoME Dataset and DeepMind's Contributions to the Materials Project
    • 3.5.6 Text Extraction and Analysis
    • 3.5.7 ChemDataExtractor V1.0 and V2.0
    • 3.5.8 LLMs Expand Material Data Mining Capabilities
  • 3.6 Databases to Big Data
    • 3.6.1 Rapid data generation and collection
    • 3.6.2 Integrated use of materials databases
    • 3.6.3 Data reliability
  • 3.7 Small Data Strategies in Materials Informatics
    • 3.7.1 Utilizing data correlations
    • 3.7.2 Selecting descriptors based on theory and experience
  • 3.8 MI with Physical Experiments and Characterization
    • 3.8.1 High-Throughput Experimentation (HTE)
    • 3.8.2 In-situ and Operando Characterisation
    • 3.8.3 Advanced Imaging and Spectroscopy
    • 3.8.4 Why High-Throughput Screening in Materials is Tougher Than in Other Fields
  • 3.9 Computational Materials Science
    • 3.9.1 Simulations for Chemistry and Materials Science R&D
    • 3.9.2 Density Functional Theory (DFT) — Quantum Mechanical Modeling
    • 3.9.3 Surrogate Models for Atomistic Simulation
    • 3.9.4 Universal ML Interatomic Potentials (CHGNet, MACE, M3GNet, MatterSim)
    • 3.9.5 Multiscale Modelling
    • 3.9.6 Integrated Computational Materials Engineering (ICME)
    • 3.9.7 ICME and the Role of Machine Learning
    • 3.9.8 QuesTek Innovations and ICME: From Service to SaaS
    • 3.9.9 Thermo-Calc, CompuTherm and the ICME Software Ecosystem
    • 3.9.10 Cloud-Based Simulation Platforms
    • 3.9.11 The Potential in Leveraging Quantum Computing
    • 3.9.12 Big Tech, Computational Materials Science and MI
  • 3.10 Autonomous Experimentation and Self-Driving Labs
    • 3.10.1 The Vision: Fully Autonomous Labs
    • 3.10.2 The Chemputer
    • 3.10.3 Workflow Management for Laboratory Automation
    • 3.10.4 A-Lab (Lawrence Berkeley): Closed-Loop Synthesis Validation
    • 3.10.5 Lila Sciences AI Science Factory
    • 3.10.6 Dunia Innovations: Physics-Informed ML + Lab Automation
    • 3.10.7 Google DeepMind's Gemini-Powered Autonomous Lab
    • 3.10.8 Commercial Self-Driving Laboratories
    • 3.10.9 Mobile Autonomous Robots in Academia
    • 3.10.10 Retrosynthesis Through to Robot Execution
    • 3.10.11 Technology Pillars for Chemical Autonomy
  • 3.11 Multi-modal Data Integration
  • 3.12 Inverse Problems in Materials Characterization
  • 3.13 Data-driven Experimental Design
  • 3.14 Automated Data Analysis and Interpretation
  • 3.15 Robotics and Automation in Materials Research
  • 3.16 Digital Twins for Materials and Process Engineering

4 APPLICATIONS OF MATERIALS INFORMATICS

  • 4.1 Alloy Design and Optimization
    • 4.1.1 High-Entropy Alloy Design
    • 4.1.2 Aluminum and titanium alloys
    • 4.1.3 Metallic glass alloys
    • 4.1.4 Nickel-base superalloys
    • 4.1.5 Steels for Extreme Environments
  • 4.2 Drug Discovery and Development
    • 4.2.1 AI-Driven Drug Design
  • 4.3 Intermetallics
  • 4.4 Organometallics
  • 4.5 Organic Electronics
    • 4.5.1 RFID
    • 4.5.2 OPV
    • 4.5.3 OLEDs
    • 4.5.4 Emerging Areas
  • 4.6 Coatings and Paints
  • 4.7 Catalysts
    • 4.7.1 Heterogeneous Catalysts
    • 4.7.2 Catalysts for Green Hydrogen Production
    • 4.7.3 Open Catalyst Project (Meta)
  • 4.8 Ionic liquids
  • 4.9 Battery Materials
    • 4.9.1 Lithium-ion batteries
    • 4.9.2 Solid-State Batteries
    • 4.9.3 Lithium-Sulfur and Beyond-Li Batteries
    • 4.9.4 Accelerated Battery Material Discovery
  • 4.10 High-density Heat Storage Materials
  • 4.11 Hydrogen-based Superconductors
  • 4.12 Sorbents for Carbon Capture
  • 4.13 Polymer Informatics
    • 4.13.1 Optimizing Additive Manufacturing Materials
    • 4.13.2 Sustainable Polymer Development
    • 4.13.3 Large Engineering Models for Polymer Processing
  • 4.14 Rubber processing
  • 4.15 Nanomaterials
    • 4.15.1 Nanofabrication
    • 4.15.2 Quantum Dots
    • 4.15.3 Other Nanomaterials
  • 4.16 2D materials
  • 4.17 Metamaterials
  • 4.18 Lubricants
  • 4.19 Thermoelectric Materials
  • 4.20 Photovoltaics
    • 4.20.1 Light Absorbers and Solar Cells
    • 4.20.2 Perovskite Photovoltaics
    • 4.20.3 Tandem Cells
  • 4.21 Metal-insulator transition compounds
  • 4.22 Self-assembled monolayers
  • 4.23 Construction Materials and Cement
  • 4.24 Biomaterials
  • 4.25 Materials for Quantum Technologies
  • 4.26 Materials for Defence and Extreme Environments
  • 4.27 PFAS Replacement Materials
  • 4.28 Critical Minerals and Rare-Earth Substitution

5 INDUSTRY ANALYSIS

  • 5.1 Materials Informatics: State of the Industry in 2026
  • 5.2 Strategic Approaches to MI
    • 5.2.1 Materials Informatics Players
    • 5.2.2 SaaS Platforms
    • 5.2.3 Project-Based Consultancies
    • 5.2.4 In-house Development by Materials Corporates
    • 5.2.5 Big Tech Cloud Platforms
    • 5.2.6 Conclusions for End-Users
    • 5.2.7 Conclusions for External MI Companies
  • 5.3 Player Analysis
    • 5.3.1 Materials Informatics Players — Overview
    • 5.3.2 Key Partners and Customers of Selected External Providers
    • 5.3.3 Partnerships with Engineering Simulation Software
    • 5.3.4 Funding Raised by Private Companies (I): In-House Development Drives Capital Requirements
    • 5.3.5 Funding Raised by Private Companies (II): The State of SaaS Business Models
    • 5.3.6 Pricing MI SaaS Platforms
      • 5.3.6.1 Risks for SaaS Business Models in MI
    • 5.3.7 Barriers to Profitability for MI SaaS Players
    • 5.3.8 Microsoft's Azure Quantum Elements: Competition for Dedicated MI Players
    • 5.3.9 Applications of Azure Quantum Elements
    • 5.3.10 Google DeepMind's GNoME and the Vertical Integration Play
    • 5.3.11 Meta's FAIR, OMat24 and the Open Catalyst Project
    • 5.3.12 Taking Materials Informatics In-House
    • 5.3.13 Offering In-Housed Operations as a Service
    • 5.3.14 Retrosynthesis Prediction
    • 5.3.15 Commercial Retrosynthesis Predictors
  • 5.4 MI Consortia and Public-Private Initiatives
    • 5.4.1 NIMS and Materials Open Platforms (Japan)
    • 5.4.2 AIST Data-Driven Consortium (Japan)
    • 5.4.3 Toyota Research Institute and University Collaboration
    • 5.4.4 The Global Acceleration Network
    • 5.4.5 IBM Collaborations
    • 5.4.6 ChiMaD and the CMD Network
    • 5.4.7 The Open Catalyst Project: Crowdsourcing MI
    • 5.4.8 Materials Genome Initiative (MGI) — U.S.
    • 5.4.9 Materials Genome Engineering / National Materials Genome Project (China)
    • 5.4.10 Horizon Europe Materials Initiatives
    • 5.4.11 K-Moonshot
    • 5.4.12 Additional Initiatives and Research Centers
  • 5.5 Corporate Initiatives in MI
  • 5.6 Strategic Collaborations and Agreements 2024–2026
  • 5.7 Geopolitics, Export Controls and MI
  • 5.8 Applications of Materials Informatics
    • 5.8.1 Project Categories in MI
    • 5.8.2 Application Progression
    • 5.8.3 Materials Informatics Roadmap 2026–2036
  • 5.9 Market Forecast and Outlook
    • 5.9.1 Market Forecast: External Materials Informatics Players (Provider Revenue)
    • 5.9.2 Market Forecast: Total MI Software & Services Market
    • 5.9.3 Forecast Data and Market Outlook
    • 5.9.4 Sensitivity Analysis: Bull, Base, and Bear Scenarios
  • 5.10 MI Industry Player Data
    • 5.10.1 Lists of MI Players
    • 5.10.2 Full Player List — Commercial Companies (Confirmed Operational)
    • 5.10.3 Industry Leavers (Likely and Confirmed)

6 COMPANY PROFILES (53 company profiles)

7 RESEARCH METHODOLOGY

8 REFERENCES

List of Tables

  • Table 1. Issues with materials science data.
  • Table 2. Key Technologies Driving Materials Informatics.
  • Table 3. Market Challenges and Restraint in Materials Informatics.
  • Table 4. Materials informatics industry developments 2024-2026.
  • Table 5. Foundation models for materials science: comparison
  • Table 6. Big Tech entrants in materials informatics: capabilities and strategy
  • Table 7. Market players in materials informatics-comparative analysis.
  • Table 8. Global materials informatics market size 2025–2036 (USD millions)
  • Table 9. Key areas of algorithm advancements in materials informatics
  • Table 10. Main categories within Materials Informatics.
  • Table 11. Key challenges for MI in materials-by type.
  • Table 12. Generative vs. discriminative algorithms
  • Table 13. Types of neural network
  • Table 14. Materials data repositories: open-source and commercial (new)
  • Table 15. Universal ML interatomic potentials — comparison
  • Table 16. Mega-rounds in MI 2024–2026 (new)
  • Table 17. Pricing models for MI SaaS platforms
  • Table 18. National MI initiatives by country
  • Table 19.Corporate initiatives in MI
  • Table 20. MI strategic collaborations and agreements 2024–2026
  • Table 21. External MI provider revenue forecast 2025–2036
  • Table 22. Global materials informatics market size 2025–2036 (US$M)
  • Table 23. Bull, base, and bear case forecasts to 2036 (US$M, total MI software and services market)
  • Table 24. Dedicated MI SaaS Platforms
  • Table 25. Project-Based Consultancies
  • Table 26. Physics-Based Incumbents with AI Capabilities
  • Table 27. Autonomous-Laboratory and Integrated AI-Plus-Experimentation Platforms
  • Table 28. Big-Tech Cloud Platforms and Open-Model Providers
  • Table 29. Materials-Specialty MI Players (Single-Domain Focus)
  • Table 30. Major Materials Corporates with In-House MI Capability
  • Table 31. Industry leavers and consolidations 2023–2026

List of Figures

  • Figure 1. Comparison of Conventional Materials Development and Materials Informatics.
  • Figure 2. Materials informatics maturity curve 2014–2026
  • Figure 3. The shift from predictive AI to generative AI in materials
  • Figure 4. Materials informatics roadmap 2026–2036
  • Figure 5. Global materials informatics market size 2025–2036 (USD millions)
  • Figure 6. Incorporating Machine Learning into Established Bioinformatics Frameworks.
  • Figure 7. Example of cheminformatics utilization
  • Figure 8. Molecular design methodology based on QSPR/QSAR.
  • Figure 9. Foundation model architecture for materials science
  • Figure 10. Diffusion model schematic for crystal generation (MatterGen)
  • Figure 11. Growth of stable known crystals
  • Figure 12. Overview of the ICME process integration and optimization workflow
  • Figure 13. Chemputer.
  • Figure 14. A-Lab autonomous synthesis workflow (Lawrence Berkeley)
  • Figure 15. Lila Sciences AI Science Factory architecture
  • Figure 16. Classes of players in materials informatics (updated)
  • Figure 17. Funding raised by major MI private companies cumulative to 2026
  • Figure 18. External MI provider revenue forecast 2025–2036
  • Figure 19. Citrine Platform Overview.
  • Figure 20. Hitachi High-Tech Chemicals Informatics and Materials Informatics proof of concept.