첨단 소재의 최적화 성능을 위한 AI 강화형 배합 전략의 성장 기회

Enabling Predictive and Sustainable Formulation Strategies Through AI-Powered Materials Optimization

AI-enhanced formulation transforms how advanced materials are designed, optimized, and commercialized, shifting from empirical experimentation to predictive, data-driven discovery. By combining AI, ML, and materials informatics, formulators can simulate and optimize complex multi-component systems, accelerating performance tuning, improving sustainability, and reducing time-to-market. This study examines how emerging AI platforms-supported by digital twins, autonomous laboratories, and high-throughput experimentation-reshape formulation workflows from ingredient discovery to life cycle assessment.

The research analyzes key formulation challenges that AI uniquely addresses, evaluates technology enablers such as generative design and reinforcement learning, and highlights industrial use cases demonstrating measurable performance gains. It emphasizes mapping innovation ecosystems, tracking investment and partnership trends, and uncovering growth opportunities where AI convergence with robotics and high-performance computing drives next-generation formulation science across sectors, including polymers, coatings, composites, energy storage, and healthcare.

Table of Contents

Strategic Imperatives

• Why Is It Increasingly Difficult to Grow?
• The Strategic Imperative 8™: Factors Creating Pressure on Growth
• The Strategic Imperative 8™
• The Impact of the Top 3 Strategic Imperatives on AI-Enhanced Formulation Strategies for Optimized Performance in Advanced Materials
• Growth Opportunities Fuel the Growth Pipeline Engine™
• Research Methodology

Growth Opportunity Analysis

• Scope of Analysis
• Research Segmentation

Growth Generator

• Present Challenges in Materials Formulation
• Key Challenges in Ingredient and Raw Material Discovery
• Key Challenges in Formulation Design and Optimization
• Key Challenges in Process Simulation and Scale-Up
• Key Challenges in Product Testing and Validation
• Key Challenges in Life Cycle and Sustainability Assessment
• Growth Drivers
• Growth Restraints

Technology Analysis

• Advances in Core AI/ML Frameworks
• Advances in Simulation and Digital Twin Technologies
• Advances in Autonomous and Data-Driven Experimentation Platforms
• Advances in Sustainability and Life Cycle Intelligence Technologies
• Advances in Knowledge Graphs, Data Infrastructure, and Cloud Platforms

Patent and Research Publications Analysis

• Overview of Patents
• Overview of Research Publications

Stakeholder Analysis

• Company Advancements Around the Ecosystem
• Important Research Contributions and Breakthroughs from Academic Institutions
• Notable Collaborations Between Key Stakeholders

Funding and Investment Analysis

• Key Public Investments
• Key Private Investments

Mergers and Acquisitions Analysis

• Notable M&As

Case Study Analysis

• Accelerating PU Fire Testing Through AI-Driven Material Informatics
• Augmenting Composite Lattice Design with AI-Enabled Simulation Automation
• Forwarding Lubricant Formulation Development with ML
• Catalyzing Lubricant Discovery with AI-Driven Screening
• Advancing Thermoplastic Polyurethane (TPU) Innovation Through Material Informatics
• Exploring High-Entropy Alloys with AI-Augmented Platform
• Optimizing Cryogenic Alloy Formulations with AI Acceleration

Analyst Perspective and Future Outlook

• Analyst Perspective
• Future-Looking Trends

Growth Opportunity Universe

• Growth Opportunity 1: AI-Guided Development of Self-Repairing Material Life Cycles
• Growth Opportunity 2: Generative AI for Inverse-Design of Programmable Meta-Materials
• Growth Opportunity 3: AI-Optimized Biological Circuitry for Engineered Living Materials

Appendix

• Technology Readiness Levels (TRL): Explanation
• Benefits and Impacts of Growth Opportunities
• Next Steps
• Legal Disclaimer