9780367678210 - Machine Learning in 2D Materials Science (15 resultados)

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Paperback. Condición: new. Paperback. 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 FEATURESProvides broad coverage of data science and ML fundamentals to materials science researchers so that they can confidently leverage these techniques in their research projectsOffers introductory material in topics such as ML, data integration, and 2D materialsProvides 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 materialsDiscusses customized ML methods for 2D materials data and applications and high-throughput data acquisitionDescribes 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 productsGives future trends in ML for 2D materials, explainable AI, and dealing with extremely large and small, diverse datasetsAimed 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. This book 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condición: new. Paperback. 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 FEATURESProvides broad coverage of data science and ML fundamentals to materials science researchers so that they can confidently leverage these techniques in their research projectsOffers introductory material in topics such as ML, data integration, and 2D materialsProvides 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 materialsDiscusses customized ML methods for 2D materials data and applications and high-throughput data acquisitionDescribes 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 productsGives future trends in ML for 2D materials, explainable AI, and dealing with extremely large and small, diverse datasetsAimed 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. This book 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condición: new. Paperback. 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 FEATURESProvides broad coverage of data science and ML fundamentals to materials science researchers so that they can confidently leverage these techniques in their research projectsOffers introductory material in topics such as ML, data integration, and 2D materialsProvides 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 materialsDiscusses customized ML methods for 2D materials data and applications and high-throughput data acquisitionDescribes 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 productsGives future trends in ML for 2D materials, explainable AI, and dealing with extremely large and small, diverse datasetsAimed 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. This book 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Parvathi Chundi, PhD is Professor of Computer Science, University of Nebraska-Omaha. Prior to Omaha, Dr. Chundi was with Agilent Technologies and HP Labs, both in Palo Alto, CA.Venkataramana Gadhamshetty, PhD, PE .

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book 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.