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AI-Powered Innovation in Materials Science: The Role of Language Models in Discovery and Design [Kõva köide]

(University of Science and Technology Beijing, China), (University of Science and Technology Beijing, China)
  • Formaat: Hardback, 576 pages, kõrgus x laius: 244x170 mm
  • Ilmumisaeg: 29-Apr-2026
  • Kirjastus: Blackwell Verlag GmbH
  • ISBN-10: 3527356355
  • ISBN-13: 9783527356355
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  • Kõva köide
  • Hind: 171,75 €
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  • Formaat: Hardback, 576 pages, kõrgus x laius: 244x170 mm
  • Ilmumisaeg: 29-Apr-2026
  • Kirjastus: Blackwell Verlag GmbH
  • ISBN-10: 3527356355
  • ISBN-13: 9783527356355
Teised raamatud teemal:
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.

Youll 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.
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
Xue Jiang is an Associate Professor at the University of Science and Technology Beijing, China specializing in materials big data and AI-driven materials research. She has led projects funded by the National Natural Science Foundation of China, published over 80 papers in journals including Acta Materialia and npj Computational Materials, and received the 2025 Science and Technology Award from the Chinese Materials Research Society.

Yanjing Su is a distinguished scholar at the University of Science and Technology Beijing, China specializing in materials big data, artificial intelligence, and corrosion science. He has published over three hundred papers in journals including Acta Materialia and npj Computational Materials, authored four academic monographs, and developed the integrated Materials Genome Engineering Platform. His honors include Chinas National First Prize for Educational Achievement.