人工智慧增强型配方策略在优化尖端材料性能方面的成长机会

Growth Opportunities in AI-Enhanced Formulation Strategies for Optimized Performance in Advanced Materials

出版日期: 2025年12月11日 | 出版商:

Frost & Sullivan | 英文 74 Pages | 商品交期: 最快1-2个工作天内

价格

简介目录

利用人工智慧进行材料优化，实现可预测且永续的配方策略

人工智慧增强型配方技术正在改变尖端材料的设计、优化和商业化方式，使其从经验实验转向预测性、数据驱动的发现。人工智慧、机器学习和材料资讯学的结合，使配方师能够模拟和优化复杂的多成分体系，从而加速性能调优、提升永续性并缩短产品上市时间。本研究检验了新兴人工智慧平台（由数位双胞胎、自主实验室和高通量实验支援）如何重塑从成分发现到生命週期评估的配方工作流程。

本研究分析了人工智慧能够独特解决的关键配方难题，评估了生成式设计和强化学习等技术平台，并展示了能够带来可衡量性能提升的工业应用案例。研究重点关注创新生态系统的建构、投资和合作趋势的追踪以及成长机会的挖掘，并指出了人工智慧、机器人和高效能运算的融合正在推动聚合物、涂料、复合材料、储能和医疗保健等多个领域的下一代配方科学发展。

利用人工智慧驱动的材料资讯加速聚氨酯防火测试
利用人工智慧驱动的复合材料自动化增强复合晶格设计
利用机器学习加速润滑油配方开发
利用人工智慧驱动的筛检加速润滑剂的发现
透过材料资讯学推动热塑性聚氨酯（TPU）创新
利用人工智慧增强平台探索高熵合金
人工智慧加速低温合金配方优化

分析师观点及未来展望

分析师观点
面向未来的趋势

成长机会领域

成长机会 1：人工智慧引导的自修復材料生命週期开发
成长机会2：可程式设计超材料逆向设计的生成式人工智慧
成长机会3：用于人工生物材料的AI优化生物迴路

附录

技术成熟度等级（TRL）解释
成长机会带来的益处和影响
下一步
免责声明

简介目录

Product Code: DB5E

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.

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