Format:
1 Online-Ressource(XVI, 172 p.)
Edition:
1st ed. 2020.
ISBN:
9783031023835
Series Statement:
Synthesis Lectures on Materials and Optics
Content:
Preface -- Acknowledgments -- Introduction -- Materials Representations -- Learning with Large Databases -- Learning with Small Databases -- Multi-Objective Learning -- Multi-Fidelity Learning -- Some Closing Thoughts -- Authors' Biographies.
Content:
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.
Additional Edition:
ISBN 9783031012556
Additional Edition:
ISBN 9783031002472
Additional Edition:
ISBN 9783031035111
Additional Edition:
Erscheint auch als Druck-Ausgabe ISBN 9783031012556
Additional Edition:
Erscheint auch als Druck-Ausgabe ISBN 9783031002472
Additional Edition:
Erscheint auch als Druck-Ausgabe ISBN 9783031035111
Language:
English
DOI:
10.1007/978-3-031-02383-5
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