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Description
Accelerate materials innovation using language models and machine learning methods
Language models and machine learning are transforming how researchers discover, design, and optimize advanced materials. AI-Powered Innovation in Materials Science: The Role of Language Models in Discovery and Design provides a systematic exploration of these methods, from data mining and predictive modeling to autonomous experimentation. Written by award-winning researchers from the University of Science and Technology Beijing, this reference connects foundational AI theory with practical implementations.
The book covers the evolution of language models in materials science, demonstrating methodologies through real-world case studies in energy, sustainability, and advanced manufacturing applications. Readers gain actionable insights into predicting material properties before experimental validation, optimizing synthesis pathways, and uncovering hidden correlations in materials data. The authors critically analyze current challenges while mapping future directions for materials intelligence research.
You’ll also discover:
- Methodologies for integrating AI throughout the materials research pipeline from initial data mining through autonomous experimentation and discovery workflows
- Practical case studies demonstrating how language models accelerate innovation in renewable energy, aerospace, and high-performance electronics applications
- Frameworks for predictive modeling that minimize costly trial-and-error processes while optimizing synthesis pathways for scalable material production
- Strategies for translating laboratory breakthroughs into practical manufacturing solutions through end-to-end lifecycle management and sustainability considerations
- Critical analysis of current limitations and a comprehensive roadmap for developing next-generation materials intelligence capabilities and research directions
Materials scientists, theoretical chemists, computational scientists, and computer scientists working at the intersection of AI and materials research will find this book invaluable. It provides the theoretical foundations and practical methodologies needed to accelerate materials development for grand challenges in energy, sustainability, and advanced manufacturing.
Table of Contents
Preface xi
1 The Revolution of AI for Materials 1
2 Fundamentals of Language Models and NLP 37
3 Reinforcement Learning in Materials 89
4 Materials Word Embedding Models 135
5 Materials Transformer-based Models 157
6 Materials Data Extraction from Literature by NLP and Large Language Models 205
7 Case Studies of Chemical Information Extraction 219
8 Case Studies of Alloy Information Extraction 243
9 Case Studies of Materials Synthesis Information Extraction 299
10 Materials Predictive Modeling with Language-augmented Approaches 317
11 Case Studies of Materials Predictive Modeling 339
12 Retrieval-augmented Generation for Materials Large Language Models 387
13 Fine-tuning and Application for Materials Large Language Models 419
14 Materials Agents for Autonomous Research 449
15 Case Studies of Materials Agents 505
16 Challenges and Future Developments 551
Index 559
