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Data-Based Methods for Materials Design and Discovery [electronic resource] : Basic Ideas and General Methods / by Ghanshyam Pilania, Prasanna V. Balachandran, James E. Gubernatis, Turab Lookman.

By: Pilania, Ghanshyam [author.].
Contributor(s): Balachandran, Prasanna V [author.] | Gubernatis, James E [author.] | Lookman, Turab [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Materials and Optics: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: XVI, 172 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031023835.Subject(s): Lasers | Materials science | Laser | Materials ScienceAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 621,366 Online resources: Click here to access online
Contents:
Preface -- Acknowledgments -- Introduction -- Materials Representations -- Learning with Large Databases -- Learning with Small Databases -- Multi-Objective Learning -- Multi-Fidelity Learning -- Some Closing Thoughts -- Authors' Biographies.
In: Springer Nature eBookSummary: 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.
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Preface -- Acknowledgments -- Introduction -- Materials Representations -- Learning with Large Databases -- Learning with Small Databases -- Multi-Objective Learning -- Multi-Fidelity Learning -- Some Closing Thoughts -- Authors' Biographies.

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.

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