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

Format:

Book

Author/Creator:

Pilania, Ghanshyam., Author.

Balachandran, Prasanna V., Author.

Gubernatis, James E., Author.

Lookman, Turab., Author.

Series:

Synthesis Lectures on Materials and Optics, 2691-1949

Language:

English

Subjects (All):

Lasers.

Materials science.

Laser.

Materials Science.

Local Subjects:

Laser.

Materials Science.

Physical Description:

1 online resource (XVI, 172 p.)

Edition:

1st ed. 2020.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

Summary:

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.

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.

ISBN:

9783031023835

3031023838