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Global Material Informatics Market to Reach US$379.9 Million by 2030

The global market for Material Informatics estimated at US$152.1 Million in the year 2024, is expected to reach US$379.9 Million by 2030, growing at a CAGR of 16.5% over the analysis period 2024-2030. Elements Material, one of the segments analyzed in the report, is expected to record a 18.2% CAGR and reach US$241.9 Million by the end of the analysis period. Growth in the Chemicals Material segment is estimated at 13.4% CAGR over the analysis period.

The U.S. Market is Estimated at US$41.4 Million While China is Forecast to Grow at 22.1% CAGR

The Material Informatics market in the U.S. is estimated at US$41.4 Million in the year 2024. China, the world's second largest economy, is forecast to reach a projected market size of US$84.5 Million by the year 2030 trailing a CAGR of 22.1% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 12.0% and 14.9% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 13.2% CAGR.

Global Material Informatics Market - Key Trends & Drivers Summarized

Why Is Material Informatics Emerging as the Future of Materials Discovery and Development?

Material informatics is rapidly redefining how new materials are discovered, optimized, and applied across industries, marking a significant departure from traditional trial-and-error approaches. By leveraging data science, machine learning, and computational modeling, material informatics enables researchers and engineers to predict material properties, simulate molecular structures, and accelerate the innovation pipeline. This technology empowers R&D teams to reduce the time, cost, and uncertainty involved in developing novel materials for applications ranging from aerospace and semiconductors to energy storage and biomedicine. The global push for next-generation technologies-like solid-state batteries, flexible electronics, and sustainable polymers-is creating urgent demand for materials with highly specific properties. Material informatics addresses this need by uncovering hidden correlations between structure and performance using vast datasets that traditional lab testing would take years to process. The digitization of legacy materials data and the integration of high-throughput experimentation platforms are further amplifying the value of informatics-driven research. This computational-first approach is also critical in identifying rare or hard-to-synthesize compounds, optimizing composites, and solving complex multi-objective problems that require balancing multiple performance metrics. As organizations embrace innovation and speed-to-market as competitive differentiators, material informatics is becoming a strategic asset in materials science and advanced manufacturing ecosystems.

How Are AI and Big Data Shaping the Next Leap in Materials Innovation?

Artificial intelligence and big data are central to the rise of material informatics, enabling a fundamental shift from empirical guesswork to predictive, data-driven design. AI algorithms, especially deep learning and neural networks, are being trained on experimental and simulated data to recognize complex patterns in atomic arrangements, phase transitions, and reaction pathways. These models can accurately predict key properties such as strength, conductivity, corrosion resistance, and thermal stability, significantly reducing the need for expensive and time-consuming lab trials. Cloud computing platforms and open-source materials databases are playing a crucial role by democratizing access to curated datasets, fostering collaboration among academic institutions, startups, and industry leaders. Tools such as materials graph networks, quantum chemistry simulations, and generative AI models are enabling the creation of entirely new material classes with engineered functionalities. Furthermore, active learning systems allow AI models to refine themselves based on new experimental results, creating an iterative feedback loop that continuously improves predictive accuracy. The fusion of informatics with autonomous labs-where robots execute AI-designed experiments-is unlocking previously unattainable research velocity. This convergence of AI, big data, and automation is not only transforming R&D in large corporations but also enabling small labs and startups to compete at the cutting edge of materials science.

Is Cross-Industry Adoption Driving a Paradigm Shift in Material Innovation Cycles?

Material informatics is seeing rapid adoption across a wide spectrum of industries seeking to innovate faster, reduce costs, and gain a competitive edge through material differentiation. In energy, the technology is accelerating the development of more efficient solar cells, hydrogen storage materials, and battery chemistries, especially for electric vehicles and grid-scale storage systems. Aerospace and defense sectors are leveraging informatics to design ultra-lightweight, heat-resistant alloys and composites critical for space exploration and supersonic travel. In pharmaceuticals, informatics-driven design is being used to discover biocompatible drug carriers, smart polymers, and next-gen packaging materials. Electronics and semiconductors are benefiting through the creation of high-dielectric materials, low-loss insulators, and novel 2D materials such as graphene alternatives. Even the construction and textile sectors are exploring smart, sustainable materials for green buildings and performance wear. As ESG mandates tighten and sustainability becomes a global priority, material informatics is also helping companies substitute hazardous or non-renewable materials with safer, eco-friendly alternatives. This cross-industry applicability is pushing enterprises to integrate informatics into their product development life cycles, often through partnerships with software vendors, cloud providers, and university research labs. The result is a systemic transformation of innovation cycles-shifting from years-long processes to data-driven pathways that can deliver results in months or even weeks.

What Are the Core Drivers Powering the Material Informatics Market’s Expansion?

The growth in the material informatics market is driven by several factors rooted in technological advancement, evolving R&D methodologies, and market-specific innovation demands. The increasing complexity of product requirements across sectors such as aerospace, electronics, energy, and healthcare has created strong demand for rapid, accurate, and scalable materials discovery platforms. The integration of high-throughput screening techniques with AI-based prediction models is enabling faster iteration and optimization, making informatics-based workflows more attractive to research institutions and commercial R&D teams. The widespread availability of open-access materials databases and advances in cloud computing are reducing entry barriers, allowing startups and mid-sized enterprises to adopt material informatics without massive infrastructure investments. In parallel, growing pressure to meet sustainability goals and regulatory requirements is encouraging industries to use informatics to identify eco-friendly, non-toxic, and recyclable material alternatives. Collaborative frameworks between academic institutions, national labs, and commercial players are accelerating knowledge transfer and real-world deployment of informatics tools. The rise of digital twins and simulation-led prototyping is reinforcing demand for data-centric design platforms that material informatics naturally supports. Furthermore, the emergence of integrated software ecosystems that combine materials modeling, data analytics, and experimental validation under one umbrella is simplifying the adoption process. Finally, government funding initiatives in materials research, particularly in clean tech and defense innovation, are creating a fertile ground for the expansion of the material informatics ecosystem globally.

SCOPE OF STUDY:

The report analyzes the Material Informatics market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:

Material Type (Elements Material, Chemicals Material, Other Materials); Technology (Machine Learning Technology, Deep Tensor Technology, Statistical Analysis Technology, Digital Annealer Technology, Other Technologies); End-Use (Material Science End-Use, Chemical & Pharmaceutical End-Use, Electronics & Semiconductors End-Use, Automotive End-Use, Aerospace & Defense End-Use, Other End-Uses)

Geographic Regions/Countries:

World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; Spain; Russia; and Rest of Europe); Asia-Pacific (Australia; India; South Korea; and Rest of Asia-Pacific); Latin America (Argentina; Brazil; Mexico; and Rest of Latin America); Middle East (Iran; Israel; Saudi Arabia; United Arab Emirates; and Rest of Middle East); and Africa.

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TABLE OF CONTENTS

I. METHODOLOGY

II. EXECUTIVE SUMMARY

III. MARKET ANALYSIS

IV. COMPETITION

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