Materials Informatics Global Market Report 2025-2035, with Profiles of 40+ Players including Albert Invent, Alchemy Cloud, Cynora, Elix, Exomatter, Genie TechBio, Innophore, Kebotix, Matmerize & more - ResearchAndMarkets.com

The "Global Materials Informatics Market 2025-2035" report has been added to ResearchAndMarkets.com's offering.

The materials informatics (MI) market represents a rapidly developing sector where data science, artificial intelligence, and materials science converge to accelerate discovery and optimization of new materials. The core value proposition driving this growth is the dramatic reduction in materials development timelines. Traditional approaches typically require 10-20 years from concept to commercialization, whereas MI-enabled methods can potentially compress this to 2-5 years. This acceleration delivers significant competitive advantages in industries where material innovation directly impacts product performance and market differentiation.

Several distinct business models have emerged within the MI ecosystem. Software-as-a-Service (SaaS) platforms from companies like Citrine Informatics, Kebotix, and Materials Design provide specialized tools for materials scientists with limited data science expertise. These platforms typically employ subscription models with tiered pricing based on functionality and user numbers.

Meanwhile, MI consultancies like NobleAI offer project-based engagements focusing on specific material development challenges. Major corporations including BASF, Toyota, and Samsung have also established substantial in-house MI capabilities, representing a third pathway to market adoption.

Recent market activity has been characterized by significant venture capital investment, with several MI startups securing funding rounds exceeding $50 million. Simultaneously, large technology companies have entered the space, most notably Microsoft with its Azure Quantum Elements platform, potentially disrupting smaller players' market positions. Strategic partnerships between MI providers and traditional materials simulation software companies have also increased, creating more comprehensive integrated solutions.

By application sector, battery materials currently represent the largest segment (approximately 30% of market value), followed by advanced polymers (20%), catalysts (15%), and alloys (12%). The strongest growth is projected in pharmaceutical materials discovery and renewable energy applications.

Key challenges facing the market include data quality and standardization issues, the high expertise barrier combining materials science and data science, and questions about return on investment given the significant upfront costs of MI implementation. Despite these challenges, the market is expected to continue rapid expansion as successful case studies demonstrate clear competitive advantages for early adopters, creating pressure across industries to implement MI approaches or risk falling behind in materials innovation capabilities.

The Global Materials Informatics Market 2025-2035 report provides an in-depth analysis of the rapidly evolving materials informatics (MI) industry, examining current technologies, market dynamics, key players, and future growth trajectories through 2035. As materials discovery and optimization increasingly leverage artificial intelligence and data science approaches, this report offers essential strategic insights for stakeholders across the materials value chain.

Report Contents include:

• Historical development of materials informatics within data science evolution
• Analysis of key motivating factors driving MI adoption, including time-to-market acceleration and cost reduction
• Detailed examination of AI integration opportunities in materials science
• Comparative analysis of MI with parallel informatics fields (bioinformatics, cheminformatics, etc.)
• Assessment of primary challenges facing widespread MI implementation
• Evaluation of machine learning advantages specific to materials development workflows
• Technology Analysis
  • Detailed examination of MI workflows from scoping to implementation
  • Comprehensive analysis of core technology approaches including data mining, ML/AI, high-throughput computation
  • In-depth assessment of MI algorithm types, capabilities, and application scenarios
  • Evaluation of data infrastructure requirements and implementation strategies
  • Analysis of database integration approaches and big data challenges in materials science
  • Examination of small data strategies for materials development environments
  • Assessment of physical experimentation integration with MI workflows
  • Detailed overview of computational materials science applications
  • Evaluation of autonomous experimentation technologies and implementation roadmaps
• Applications of Materials Informatics
  • Detailed case studies across 21 material categories including:
    • Alloy design optimization with specific focus on high-entropy, aluminum, titanium, and superalloys
    • Pharmaceutical and drug discovery applications
    • Specialty materials (intermetallics, organometallics, ionic liquids)
    • Electronic materials including organic electronics and 2D materials
    • Energy materials with focus on batteries, hydrogen technologies, and thermoelectrics
    • Structural materials including polymers, nanomaterials, and construction applications
    • Sustainable materials development for circular economy applications
• Market Analysis
  • Comprehensive competitive landscape assessment of major players and emerging competitors
  • Detailed funding analysis for MI companies with investment trends through 2025
  • Strategic approaches analysis for both MI providers and end-users
  • Examination of key consortia, corporate initiatives, and strategic partnerships
  • Analysis of global MI initiatives and government-backed programs
  • Research center and academic activity assessment
  • Detailed company profiles of 42 MI technology providers and end-users.
  • Market size forecasts with segmentation by:
    • Technology type and application area
    • Geographic region and industry vertical
    • Business model (SaaS, consulting, in-house)
    • End-user type and company size
• Future Outlook and Economic Impact
  • Assessment of emerging technologies including quantum machine learning and neuromorphic computing
  • Analysis of economic impacts including R&D cost savings and time-to-market acceleration
  • Evaluation of MI's role in sustainable development and circular economy initiatives
  • Global market forecasts from 2025-2035 with detailed growth analysis
  • Strategic recommendations for MI providers, end-users, and investors

This comprehensive analysis includes company overviews, proprietary technology assessments, business models, key partnerships, target markets, funding history, and strategic positioning within the materials informatics ecosystem.

The report provides both established industry leaders and emerging start-ups with actionable intelligence to navigate this rapidly evolving market landscape through 2035.

Company Profiles Include:

• Albert Invent
• Alchemy Cloud
• Ansatz AI
• Citrine Informatics
• Copernic Catalysts
• Cynora
• Dunia Innovations
• Elix Inc.
• Enthought
• Exomatter GmbH
• Exponential Technologies Ltd.
• FEHRMANN MaterialsX
• Fluence Analytics
• Genie TechBio
• Hitachi High-Tech
• Innophore
• Intellegens
• Kebotix
• Kyulux
• LG AI Research
• materialsIn
• Materials Zone
• Matmerize Inc.
• Mat3ra
• META

Key Topics Covered:

1 EXECUTIVE SUMMARY

1.1 What is Materials Informatics?

1.2 Issues with Materials Science Data

1.3 Dealing with little or sparse data

1.4 Key Technologies Driving Materials Informatics

1.5 Importance in Modern Materials Science and Engineering

1.6 Market Challenges and Restraints

1.7 Recent Industry Developments

1.8 Market Players

1.9 Market Outlook and Opportunities

1.10 MI Roadmap

1.11 Economic Impact Analysis

1.12 Sustainability and Environmental

1.13 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

2.4 Integration of Artificial Intelligence (AI) into materials science and engineering

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

2.9 Challenges

2.10 Advantages of Machine Learning

3 TECHNOLOGY ANALYSIS

3.1 Overview

3.2 Technology approaches

3.3 MI algorithms

3.4 Data infrastructure

3.5 Databases

3.6 Databases to big data

3.7 Small data strategies in materials informatics

3.8 MI with Physical Experiments and Characterization

3.9 Computational Materials Science

3.10 Autonomous Experimentation and Labs

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

4 APPLICATIONS OF MATERIALS INFORMATICS

4.1 Alloy Design and Optimization

4.2 Drug Discovery and Development

4.3 Intermetallics

4.4 Organometallics

4.5 Organic Electronics

4.6 Coatings

4.7 Catalysts

4.8 Ionic liquids

4.9 Battery Materials

4.10 High-density Heat Storage Materials

4.11 Hydrogen-based Superconductors

4.12 Polymer Informatics

4.13 Rubber processing

4.14 Nanomaterials

4.15 2D materials

4.16 Metamaterials

4.17 Lubricants

4.18 Thermoelectric Materials

4.19 Photovoltaics

4.20 Construction Materials

4.21 Biomaterials

5 MARKET PLAYERS

5.1 Main Players

5.2 Funding

5.3 Market Strategies

5.4 MI Consortia

5.5 Corporate Initiatives in MI

5.6 Strategic Collaborations and Agreements

5.7 Global Initiatives

5.8 Research Centre and Academic Activity

6 COMPANY PROFILES (42 company profiles)

For more information about this report visit https://www.researchandmarkets.com/r/8d2416

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