Advanced Two Dimensional Material Based Heterostructures In Sustainable Energy Storage Devices
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Advanced Two-Dimensional Material-Based Heterostructures in Sustainable Energy Storage Devices
This book provides a detailed overview of advances and challenges in the development of 2D materials for use in energy storage devices. It offers insight into the synthesis, characterization, and application of 2D materials and their heterostructures in various energy storage devices, focusing on new phenomena and enhanced electrochemistry.
Advanced Two-Dimensional Material-Based Heterostructures in Sustainable Energy Storage Devices
Advanced Two-Dimensional Material-Based Heterostructures in Sustainable Energy Storage Devices provides a detailed overview of advances and challenges in the development of 2D materials for use in energy storage devices. It offers deep insight into the synthesis, characterization, and application of different 2D materials and their heterostructures in a variety of energy storage devices, focusing on new phenomena and enhanced electrochemistry. This book: Introduces 2D materials, synthesis methods, and characterization techniques Discusses application in a wide range of batteries and supercapacitors Offers perspectives on future investigations necessary to overcome existing challenges This comprehensive reference is written to guide researchers and engineers working to advance the technology of energy-efficient energy storage devices.
Machine Learning in 2D Materials Science
Data science and machine learning (ML) methods are increasingly being used to transform the way research is being conducted in materials science to enable new discoveries and design new materials. For any materials science researcher or student, it may be daunting to figure out if ML techniques are useful for them or, if so, which ones are applicable in their individual contexts, and how to study the effectiveness of these methods systematically. KEY FEATURES Provides broad coverage of data science and ML fundamentals to materials science researchers so that they can confidently leverage these techniques in their research projects Offers introductory material in topics such as ML, data integration, and 2D materials Provides in-depth coverage of current ML methods for validating 2D materials using both experimental and simulation data, researching and discovering new 2D materials, and enhancing ML methods with physical properties of materials Discusses customized ML methods for 2D materials data and applications and high-throughput data acquisition Describes several case studies illustrating how ML approaches are currently leading innovations in the discovery, development, manufacturing, and deployment of 2D materials needed for strengthening industrial products Gives future trends in ML for 2D materials, explainable AI, and dealing with extremely large and small, diverse datasets Aimed at materials science researchers, this book allows readers to quickly, yet thoroughly, learn the ML and AI concepts needed to ascertain the applicability of ML methods in their research.