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[null,"1.","Record Nr.",null,"UNINA9911018888303321"]
[null,null,"Autore",null,"Zhu Xi"]
[null,null,"Titolo",null,"AI and Robotic Technology in Materials and Chemistry Research"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2025"]
[{"$nulls":4},"©2025"]
[null,null,"ISBN",null,"9783527848836"]
[{"$nulls":4},"3527848835"]
[{"$nulls":4},"9783527848829"]
[{"$nulls":4},"3527848827"]
[{"$nulls":4},"9783527848812"]
[{"$nulls":4},"3527848819"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (207 pages)"]
[null,null,"Disciplina",null,"620.11072"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Materials science"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Cover -- Title Page -- Copyright -- Contents -- Preface -- About the Author -- Acknowledgments -- Chapter 1 Survey of Challenges in Chemistry and Materials Science Research -- 1.1 Introduction -- 1.2 Energy Form -- 1.2.1 Steam Power -- 1.2.2 Electricity Power -- 1.2.3 Other Energy Forms -- 1.3 Data -- References -- Chapter 2 Robots Technology Development in Modern Scientific Research -- 2.1 Introduction -- 2.2 Early Development of Laboratory Automation (Before 2000) -- 2.2.1 Early Automation Technologies -- 2.2.2 Laying the Foundation for AI and Robotics -- 2.3 Preliminary Integration and Development of Laboratory Automation (2000-2019) -- 2.3.1 Automation Technologies (2000-2010) -- 2.3.2 Various Forms of Exploration Based on Established Foundations -- 2.4 Latest Developments and Current Trends (2020-2023) -- 2.4.1 Automation Technologies in Five Years -- 2.4.2 Mature Industrialization as well as In‐depth Exploration -- 2.5 Outlook on Future Development -- 2.6 Conclusion -- References -- Chapter 3 AI Algorithm for Chemical and Bio‐material Design -- 3.1 Introduction -- 3.2 Molecular Representation and Encoding -- 3.2.1 Linear Notations for Molecules -- 3.2.2 Graph Representations for Molecules -- 3.3 The Formulation "]
[{"$nulls":4},"of Accessible and Searchable Data -- 3.3.1 Traditional Way for Molecular Structure-Property Relationship Determination: The Kohn-Sham Equation -- 3.3.2 Dataset Preprocessing -- 3.3.3 Current Existing Dataset -- 3.4 AI for Molecular Structure-Property Relationship -- 3.4.1 The Deep Learning Technology -- 3.4.2 AI Solving the Kohn-Sham Equation -- 3.5 AI for Chemical and Bio‐material Design -- 3.5.1 Design Workflows -- 3.5.2 Example of Designed Chemical and Bio‐materials -- References -- Chapter 4 Autonomous Laboratory Empowered by AI and Robotics -- 4.1 Evolution of Laboratory -- 4.2 Core Technologies in Autonomous Laboratories."]
[{"$nulls":4},"4.2.1 Autonomous Laboratory Components -- 4.2.2 Reinforcement Learning -- 4.3 Example Autonomous Laboratory Solution -- 4.3.1 Automatic Device only Solution -- 4.3.2 Solution that Including Design and React -- 4.3.3 Solution in Reaction Optimization -- 4.3.4 Solutions Contain Full Phases -- 4.4 Advanced Autonomous Laboratory Solutions -- 4.4.1 Advanced Experimental Data Analysis Methods -- 4.4.2 Design and Analysis in the Large Model Era -- 4.4.3 Experiment Visualization -- 4.5 Future Prospects and Trends -- References -- Chapter 5 Large Language Models for the Autonomous Material Research -- 5.1 Review of Large Language Models Development and Applications -- 5.2 Fundamentals of LLM for Material Research: Database and Knowledge Base Construction -- 5.3 Evaluation: Spider Matrix -- 5.4 Ideation: AI Supervisor and ScholarNet -- 5.5 Results and Discussion -- 5.6 Conclusion -- References -- Chapter 6 Toward a Blockchain‐Powered Anti‐Counterfeiting Experimental Data System in an Autonomous Laboratory -- 6.1 Blockchain Technology -- 6.2 Laboratory Chemical Management and Safety -- 6.3 The Problem of Data Integrity and Counterfeiting in Scientific Research -- 6.4 Blockchain in the Autonomous Laboratory -- 6.5 Symbolic Representation of Experiments -- 6.6 Challenges and Limitations -- 6.6.1 Standard Compilation for Experiment Methods -- 6.6.2 High Cost for PoW and PoS -- 6.6.2.1 Data Storage Safety -- 6.7 Conclusion -- References -- Chapter 7 The Future Integrated Computational and Experimental Research in Metaverse -- 7.1 Introduction of Metaverse -- 7.1.1 Industry 5.0 Protocol -- 7.1.2 Current Development of Metaverse -- 7.1.3 Human‐in‐Loop Paradigm -- 7.2 Research Paradigm in Metaverse -- 7.3 Autonomous High‐Throughput Experiments -- 7.3.1 Theory Driven by AI -- 7.3.2 HIL Implementation -- 7.3.3 AI Prediction -- 7.4 H2O Phase Research in Metaverse."]
[{"$nulls":4},"7.5 Aqueous System Research in Metaverse -- 7.6 Challenges and Future Directions -- References -- Index -- EULA."]
[null,null,null,"Sommario/riassunto","A singular resource for researchers seeking to apply artificial intelligence and robotics to materials science   In AI and Robotic Technology in Materials and Chemistry Research, distinguished researcher Dr. Xi Zhu delivers an incisive and practical guide to the use of artificial intelligence and robotics in materials science and chemistry. Dr. Zhu explains the principles of AI from the perspective of a scientific researcher, including the challenges of applying the technology to chemical and biomaterials design. He offers concise interviews and surveys of highly regarded industry professionals and highlights the interdisciplinary and broad applicability of widely available AI tools like ChatGPT.   The book covers computational methods and approaches from algorithms, models, and experimental data systems, and includes case studies that showcase the real-world applications of artificial intelligence and lab automation in a variety of scientific research settings from around the world.   You'll also find:    * A thorough introduction to the challenges currently being faced by chemists and materials science researchers  * Comprehensive explorations of "]
[{"$nulls":4},"autonomous laboratories powered by artificial intelligence and robotics  * Practical discussions of a blockchain-powered anti-counterfeiting experimental data system in an autonomous laboratory  * In-depth treatments of large language models as applied to autonomous materials research  Perfect for materials scientists, analytical chemists, and robotics engineers, AI and Robotic Technology in Materials and Chemistry Research will also benefit analytical and pharmaceutical chemists, computer analysts, and other professionals and researchers with an interest in artificial intelligence and robotics."]