AI-Driven Experimental Research in Nanomaterials
Advances in Catalysis, Energy Storage and Sensing
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Beschreibung
This contributed volume provides a comprehensive overview of how artificial intelligence (AI) and machine learning (ML) are transforming experimental research in nanomaterials. It covers foundational AI concepts, data acquisition and preprocessing tailored to nanomaterials and explores AI-enhanced synthesis and characterization techniques. The volume discusses applications in catalysis, environmental remediation, energy storage, and sensor technologies, emphasizing green synthesis and high-performance materials. It concludes with future directions, including responsible AI practices, policy frameworks, and educational strategies. Designed for graduate students, academic researchers, and R&D professionals in materials science, chemistry, physics, nanotechnology, and engineering, as well as policymakers and educators, this book aims to illuminate how AI can revolutionize nanomaterials research for energy, environmental, and biomedical applications.
This contributed volume provides a comprehensive overview of how artificial intelligence (AI) and machine learning (ML) are transforming experimental research in nanomaterials. It covers foundational AI concepts, data acquisition and preprocessing tailored to nanomaterials and explores AI-enhanced synthesis and characterization techniques. The volume discusses applications in catalysis, environmental remediation, energy storage, and sensor technologies, emphasizing green synthesis and high-performance materials. It concludes with future directions, including responsible AI practices, policy frameworks, and educational strategies. Designed for graduate students, academic researchers, and R&D professionals in materials science, chemistry, physics, nanotechnology, and engineering, as well as policymakers and educators, this book aims to illuminate how AI can revolutionize nanomaterials research for energy, environmental, and biomedical applications.
Integrates artificial intelligence with experimental nanomaterials research for faster discoveries Demonstrates predictive modeling and automated characterization to optimize synthesis pathways Provides reproducible experimental protocols and AI-ready datasets for researchers and industry
Autor*in
Ramachandra Naik
Themen in »AI-Driven Experimental Research in Nanomaterials«
computational chemistry Machine learning for materials discovery nanotechnology Predictive modeling AI-driven energy storage technologies