Data-Based Methods for Materials Design and Discovery Basic Ideas and General Methods
Pilania, Ghanshyam
Data-Based Methods for Materials Design and Discovery Basic Ideas and General Methods by Ghanshyam Pilania, Prasanna V. Balachandran, James E. Gubernatis, Turab Lookman. - 1st edition 2020 - 1 recurso en línea (XVI, 172 páginas) - Synthesis Lectures on Materials and Optics 2691-1949 .
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
Materiales--Modelos matemáticos
Aprendizaje automático
TA404.23 / 2020 EB
Data-Based Methods for Materials Design and Discovery Basic Ideas and General Methods by Ghanshyam Pilania, Prasanna V. Balachandran, James E. Gubernatis, Turab Lookman. - 1st edition 2020 - 1 recurso en línea (XVI, 172 páginas) - Synthesis Lectures on Materials and Optics 2691-1949 .
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
Materiales--Modelos matemáticos
Aprendizaje automático
TA404.23 / 2020 EB