Pilania, Ghanshyam.
Data-Based Methods for Materials Design and Discovery Basic Ideas and General Methods / [electronic resource] : by Ghanshyam Pilania, Prasanna V. Balachandran, James E. Gubernatis, Turab Lookman. - 1st ed. 2020. - XVI, 172 p. online resource. - Synthesis Lectures on Materials and Optics, 2691-1949 . - Synthesis Lectures on Materials and Optics, .
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.
9783031023835
10.1007/978-3-031-02383-5 doi
Lasers.
Materials science.
Laser.
Materials Science.
QC685-689.55 TA1671-1707
621,366
Data-Based Methods for Materials Design and Discovery Basic Ideas and General Methods / [electronic resource] : by Ghanshyam Pilania, Prasanna V. Balachandran, James E. Gubernatis, Turab Lookman. - 1st ed. 2020. - XVI, 172 p. online resource. - Synthesis Lectures on Materials and Optics, 2691-1949 . - Synthesis Lectures on Materials and Optics, .
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.
9783031023835
10.1007/978-3-031-02383-5 doi
Lasers.
Materials science.
Laser.
Materials Science.
QC685-689.55 TA1671-1707
621,366