AI and Robotic Technology in Materials and Chemistry Research

Cover
Half Title
AI and Robotic Technology in Materials and Chemistry Research
Copyright
Contents
Preface
About the Author
Acknowledgments
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
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
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 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 DesignWorkflows
		3.5.2 Example of Designed Chemical and Bio-materials
	References
4. Autonomous Laboratory Empowered by AI and Robotics
	4.1 Evolution of Laboratory
	4.2 Core Technologies in Autonomous Laboratories
		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
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
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
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
	7.5 Aqueous System Research in Metaverse
	7.6 Challenges and Future Directions
	References
Index