人工智慧增強型配方策略在最佳化尖端材料性能方面的成長機會

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個工作天內

價格

簡介目錄

利用人工智慧進行材料最佳化，實現可預測且永續的配方策略

人工智慧增強型配方技術正在改變尖端材料的設計、最佳化和商業化方式，使其從經驗實驗轉向預測性、數據驅動的發現。人工智慧、機器學習和材料資訊學的結合，使配方師能夠模擬和最佳化複雜的多成分體系，從而加速性能調優、提升永續性並縮短產品上市時間。本研究檢驗了新興人工智慧平台（由數位雙胞胎、自主實驗室和高通量實驗支援）如何重塑從成分發現到生命週期評估的配方工作流程。

本研究分析了人工智慧能夠獨特解決的關鍵配方難題，評估了生成式設計和強化學習等技術平台，並展示了能夠帶來可衡量性能提升的工業應用案例。研究重點關注創新生態系統的建構、投資和合作趨勢的追蹤以及成長機會的挖掘，並指出了人工智慧、機器人和高效能運算的融合正在推動聚合物、塗料、複合材料、儲能和醫療保健等多個領域的下一代配方科學發展。

利用人工智慧驅動的材料資訊加速聚氨酯防火測試
利用人工智慧驅動的複合材料自動化增強複合晶格設計
利用機器學習加速潤滑油配方開發
利用人工智慧驅動的篩檢加速潤滑劑的發現
透過材料資訊學推動熱塑性聚氨酯（TPU）創新
利用人工智慧增強平台探索高熵合金
人工智慧加速低溫合金配方最佳化

分析師觀點及未來展望

分析師觀點
面向未來的趨勢

成長機會領域

成長機會 1：人工智慧引導的自修復材料生命週期開發
成長機會2：可程式設計超材料逆向設計的生成式人工智慧
成長機會3：用於人工生物材料的AI最佳化生物迴路

附錄

技術成熟度等級（TRL）解釋
成長機會帶來的益處和影響
下一步
免責聲明

簡介目錄

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