Dublin, July 08, 2024 (GLOBE NEWSWIRE) -- The "Global Materials Informatics Market 2024-2035" report has been added to ResearchAndMarkets.com's offering.
Large-scale digital transformation is occurring across a broad range of industries, fuelled by cheap computing power, the proliferation of cloud-based database hosting infrastructure, ubiquitous data collection, and powerful artificial intelligence (AI). Materials and chemicals companies are also following digitalisation trends, and industry leaders have begun adopting systematic data-driven R&D practices to optimize materials and formulations through tuning of composition and processing conditions.
Materials informatics (MI), the application of data science, materials science, and AI to the materials and chemicals space, has enabled researchers to leverage complex, data-driven insights for the discovery of novel materials faster than ever before by reducing the number of experiments required during the materials development process by 50-70%. By leveraging the power of AI and data science, we can accelerate discovery, optimize processes, and develop materials with unprecedented precision and efficiency.
The integration of MI with other emerging technologies, such as robotics for autonomous experimentation and quantum computing for advanced simulations, promises to further revolutionize the field. As these technologies mature, we can expect to see even more rapid advancements in materials discovery and development.
However, realizing the full potential of MI requires addressing significant challenges in data quality, algorithm development, and integration across different scales and disciplines.
The Materials Informatics Global Market 2024-2035 covers the global MI market from 2024 to 2035, offering in-depth insights into market trends, key players, technological advancements, and growth opportunities across various industries.
Report contents include:
- Critical issues in materials science data, strategies for dealing with sparse data, and key technologies driving the MI revolution.
- Market challenges, recent industry developments, leading market players.
- Integration of artificial intelligence into materials science and engineering, presenting AI opportunities and algorithm advancements.
- Comprehensive overview of MI approaches, including data mining, machine learning, high-throughput computation, and quantum computing. It examines
- MI algorithms, automated feature selection, supervised learning models, and deep learning techniques.
- Data infrastructure, databases, and the transition from traditional databases to big data in materials science.
- MI applications across diverse fields including alloy design and optimization, drug discovery and development, battery materials, polymer informatics, nanomaterials, and many other areas.
- Market players including market strategies, funding trends, corporate initiatives, and strategic collaborations.
- Global initiatives and research activities driving MI advancement.
- Market forecasts, projecting the global MI market size from 2023 to 2035. Growth trends, market drivers, and potential barriers to adoption.
- Cost savings in materials R&D accelerated time-to-market for new materials, job creation, and the impact on traditional materials industries.
- Sustainability and environmental considerations highlight MI's role in sustainable development, reducing the environmental impact of materials production, and supporting the circular economy.
- Future trends include the integration of AI and robotics in materials labs, quantum machine learning, and materials informatics as a service (MIaaS).
- Detailed company profiles provide insights into the strategies, technologies, and market positioning of leading MI companies. These profiles cover a wide range of players, from established software companies, chemicals and materials corporations, to innovative startups specializing in MI solutions.
Companies profiled include
- Alchemy Cloud
- Asahi Kasei
- Citrine Informatics
- Copernic Catalysts
- Elix Inc
- Enthought
- Exomatter GmbH
- Exponential Technologies Ltd.
- FEHRMANN MaterialsX
- Genie TechBio
- Hitachi High-Tech
- Innophore
- Intellegens
- Kebotix
- Kyulux
- Mat3ra
- Materials Zone
- Matmerize
- Noble.AI
- OntoChem GmbH
- Phaseshift Technologies
- Polymerize
- Proterial Ltd.
- Schrodinger
- Sumitomo Chemical
- TDK
- Toray
- Uncountable
- Xinterra
- Yokogawa Fluence Analytics.
This report is an essential resource for:
- Materials scientists and researchers seeking to understand and leverage MI technologies
- R&D managers in industries relying on advanced materials
- Investors and venture capitalists interested in the MI market
- Technology companies developing MI solutions
- Policy makers and regulators involved in materials science and technology innovation
- Academic institutions and research organizations focused on materials science and data-driven approaches
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 Future Markets Outlook and Opportunities
1.9.1 Integration of AI and Robotics in Materials Labs
1.9.2 Quantum Machine Learning for Materials Discovery
1.9.3 Blockchain for Materials Data Management
1.9.4 Edge Computing in Materials Informatics
1.9.5 Augmented and Virtual Reality in Materials Design
1.9.6 Neuromorphic Computing for Materials Modeling
1.9.7 Materials Informatics as a Service (MIaaS)
1.9.8 Integration with the Internet of Things (IoT)
1.9.9 Green Technology and Circular Economy Applications
1.10 MI Roadmap
1.11 Economic Impact Analysis
1.11.1 Cost Savings in Materials R&D
1.11.2 Accelerated Time-to-Market for New Materials
1.11.3 Job Creation and Skill Development
1.11.4 Impact on Traditional Materials Industries
1.12 Sustainability and Environmental
1.12.1 Role of Materials Informatics in Sustainable Development
1.12.2 Reducing Environmental Impact of Materials Production
1.12.3 Design for Recyclability and Circular Economy
1.12.4 Bio-inspired Materials Discovery
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
2.3.1 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
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
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
3 TECHNOLOGY ANALYSIS
3.1 Overview
3.2 Technology approaches
3.2.1 Data Mining
3.2.2 Machine Learning and AI
3.2.3 High-Throughput Computation
3.2.4 Data Infrastructure
3.2.5 Visualization Tools
3.2.6 Reinforcement Learning
3.2.7 Natural Language Processing
3.2.8 Automated Experimentation
3.2.9 Workflow Management
3.2.10 Quantum Computing
3.2.11 QSAR and QSPR
3.3 MI algorithms
3.3.1 Types of MI Algorithms
3.3.2 Automated feature selection
3.3.3 Supervised Learning Models
3.3.3.1 Supervised Learning Algorithms
3.3.3.2 Unsupervised Learning Algorithms
3.3.4 Bayesian optimization
3.3.5 Genetic algorithms
3.3.6 Generative vs discriminative algorithms
3.3.7 Deep learning
3.3.8 Large Language Models (LLMs) and Materials R&D
3.4 Data infrastructure
3.5 Databases
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 Characterization
3.8.3 Advanced Imaging and Spectroscopy
3.9 Computational Materials Science
3.9.1 Integrated Computational Materials Engineering (ICME)
3.9.2 Quantum Computing
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
4.1.1 High-Entropy Alloy Design
4.1.2 Aluminum and titanium alloys
4.1.3 Metallic glass alloys
4.1.4 Nickel-base superalloys
4.2 Drug Discovery and Development
4.2.1 AI-Driven Drug Design
4.3 Intermetallics
4.4 Organometallics
4.5 Organic Electronics
4.6 Coatings
4.7 Catalysts
4.8 Ionic liquids
4.9 Battery Materials
4.9.1 Lithium-ion batteries
4.9.2 Accelerated Battery Material Discovery
4.10 High-density Heat Storage Materials
4.11 Hydrogen-based Superconductors
4.12 Polymer Informatics
4.12.1 Optimizing Additive Manufacturing Materials
4.12.2 Sustainable Polymer Development
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 (35 company profiles)
For more information about this report visit https://www.researchandmarkets.com/r/xlwp5e
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