ISBN: 9783527356355 - ai-powered innovation in materials science: the role of language models in discovery and design (10 risultati)

Buch. Condizione: Neu. Neuware -Accelerate materials innovation using language models and machine learning methodsLanguage 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 directionsMaterials 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. 576 pp. Englisch.…

Buch. Condizione: Neu. Neuware -Accelerate materials innovation using language models and machine learning methodsLanguage 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 directionsMaterials 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. 576 pp. Englisch.…

Condizione: New.

Buch. Condizione: Neu. 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. …

Condizione: New.

Condizione: Sehr gut. Zustand: Sehr gut | Seiten: 576 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Buch. Condizione: Neu. Neuware - Accelerate materials innovation using language models and machine learning methodsLanguage 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 directionsMaterials 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.…

Hardcover. Condizione: new. Hardcover. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condizione: new. Hardcover. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardcover. Condizione: new. Hardcover. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…