AI-Powered Innovation in Materials Science

The Role of Language Models in Discovery and Design
1. Auflage 2026
ISBN: 978-3-527-35635-5
Verlag: WILEY-VCH

Buch, Englisch, 592 Seiten, Format (B × H): 170 mm x 244 mm

Accelerate materials discovery 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.

Autoren/Hrsg.

Jiang, Xue

Su, Yanjing

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Naturwissenschaften Chemie Analytische Chemie Instrumentelle Analytik, Chromatographie
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Werkstoffkunde, Materialwissenschaft: Forschungsmethoden

Inhaltsverzeichnis

Chapter 1: The Revolution of AI for Materials
Chapter 2: Fundamentals of Language Models and NLP
Chapter 3: Reinforcement Learning in Materials
Chapter 4: Large Language Models for Materials
Chapter 5: Materials Data Extraction from Literature by NLP and Large Language Models
Chapter 6: Predictive Modeling with Language-Augmented Approaches
Chapter 7: Chapter 7 Conversational Large Language Models for Materials Research
Chapter 8: Materials Agents for Autonomous Research
Chapter 9: Challenges and Future Developments
 

Über Autor(innen)

Xue Jiang is an Associate Professor at the University of Science and Technology Beijing, specializing in materials big data and intelligent materials R&D. She has led 6 competitive research projects, including the National Natural Science Foundation of China (NSFC) Young Scientists Fund, Guangdong Basic and Applied Basic Research Foundation, and key topics under Guangdong Provincial Key R&D Program. Additionally, she has contributed to 14 major national initiatives, such as the National Key R&D Program of China and NSFC Joint Key Projects. With 30 first/corresponding-author publications in top-tier journals (e.g., Acta Materialia, npj Computational Materials, Scripta Materialia), her work includes 25 SCI-indexed papers (12 in TOP journals) and 1 ESI Highly Cited Paper. She holds 22 authorized invention patents and software copyrights. An active educator, Dr. Jiang co-developed the "Materials Genome Engineering" curriculum series, teaching courses like Fundamentals of Materials Design, Materials Data Science, and Materials Big Data Technology. She has co-authored 3 academic books. As a core member of the National Advanced Materials Big Data Center and recipient of the 2023 Materials Genome Engineering Young Scientist Award, she serves on the CSTM Committee for Materials Genome Engineering and as a Youth Editorial Board Member of MGE Advances. She also reviews for prestigious journals (Nature Synthesis, npj Computational Materials, etc.).
 
Yanjing Su is a distinguished scholar and leading expert at the University of Science and Technology Beijing in materials big data, artificial intelligence, and corrosion science. With extensive expertise spanning fundamental research and industrial applications, he has made seminal contributions to the development of data-driven materials science and next-generation corrosion-resistant alloys. As a key advisor to China's national scientific initiatives, he serves on multiple high-level expert committees, including the Ministry of Industry and Information Technology's "Materials Genome Engineering Key Technologies" program, the National Key R&D Program on "Rare Earth New Materials," and the NSFC's major research plan on explainable AI technologies. His work has resulted in over 300 publications in top-tier journals including Acta Materialia, Corrosion Science, and npj Computational Materials, along with 4 influential academic monographs. His achievements have been recognized with numerous honors, including the National First Prize for Educational Achievement (China's highest teaching award) and six provincial/ministerial awards for scientific and technological progress. The integrated Materials Genome Engineering Platform he developed, combining databases, data acquisition, and machine learning tools, has become a valuable resource for both academic research and industrial R&D.