AI-Powered Innovation in Materials Science | The Role of Language Models in Discovery and Design
Lingua: inglese
Editore: Wiley-VCH, 2026
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AI-Powered Innovation in Materials Science | The Role of Language Models in Discovery and Design | Xue Jiang (u. a.) | Buch | 576 S. | Englisch | 2026 | Wiley-VCH | EAN 9783527356355 | Verantwortliche Person für die EU: Wiley-VCH GmbH, Boschstr. 12, 69469 Weinheim, product-safety[at]wiley[dot]com | Anbieter: preigu.
Codice articolo 136106483
- Titolo
- AI-Powered Innovation in Materials Science | The Role of Language Models in Discovery and Design
- Autore
- Xue Jiang (u. a.)
- Editore
- Wiley-VCH
- Anno di pubblicazione
- 2026
- Condizione
- Neu
- Rilegatura
- Buch
- Lingua
- inglese
- ISBN 10
- 3527356355
- ISBN 13
- 9783527356355
- Peso dell'articolo
- 666 grammi
- Dimensioni
- 35 x 176 x 15 mm
- Cataloghi dei venditori
- Bücher
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.
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Informazioni sull’autore
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.
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