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OverviewMachine learning methods are changing the way we design and discover new materials. This book provides an overview of approaches successfully used in addressing materials problems (alloys, ferroelectrics, dielectrics) with a focus on probabilistic methods, such as Gaussian processes, to accurately estimate density functions. The authors, who have extensive experience in this interdisciplinary field, discuss generalizations where more than one competing material property is involved or data with differing degrees of precision/costs or fidelity/expense needs to be considered. Full Product DetailsAuthor: Ghanshyam Pilania , Prasanna V. Balachandran , James E. Gubernatis , Turab LookmanPublisher: Morgan & Claypool Publishers Imprint: Morgan & Claypool Publishers Dimensions: Width: 19.10cm , Height: 1.30cm , Length: 23.50cm Weight: 0.544kg ISBN: 9781681737393ISBN 10: 1681737396 Pages: 188 Publication Date: 30 March 2020 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsPreface Acknowledgments Introduction Materials Representations Learning with Large Databases Learning with Small Databases Multi-Objective Learning Multi-Fidelity Learning Some Closing Thoughts Authors' BiographiesReviewsAuthor InformationLos Alamos National Laboratory, Los Alamos, New Mexico University of Virginia, Charlottesville, Virgina Santa Fe, New Mexico Santa Fe, New Mexico Tab Content 6Author Website:Countries AvailableAll regions |
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