AI-Powered Innovation in Materials Science: The Role of Language Models in Discovery and Design - Tapa dura

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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 China’s National First Prize for Educational Achievement.

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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.