Machine 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.

Book Information
ISBN 9783031012556
Author Ghanshyam Pilania
Format Paperback
Page Count 172
Imprint Springer International Publishing AG
Publisher Springer International Publishing AG
Weight(grams) 372g