Machine Learning in Materials Science
Author(s):Keith T. ButlerFelipe OviedoPieremanuele Canepa
Publication Date:
June 16, 2022
Copyright © 2022 American Chemical Society
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Book Description
Machine Learning for Materials Science provides the fundamentals and useful insight into where Machine Learning (ML) will have the greatest impact for the materials science researcher. This digital primer provides example methods for ML applied to experiments and simulations, including the early stages of building an ML solution for a materials science problem, concentrating on where and how to get data and some of the considerations when choosing an approach.
The authors demonstrate how to build more robust models, how to make sure that your colleagues trust the results, and how to use ML to accelerate or augment simulations, by introducing methods in which ML can be applied to analyze and process experimental data. They also cover how to build integrated closed-loop experiments where ML is used to plan the course of a materials optimization experiment and how ML can be utilized in the discovery of materials on computers.
Table of Contents
About the Series
Preface
Chapter 1 Applying Machine Learning (ML) to Materials Science
Chapter 2 Building Trust in Machine Learning
Chapter 3 Machine Learning for Materials Simulations
Chapter 4 Analyzing Experimental Data
Chapter 5 Closed-Loop Optimization and Active Learning for Materials
Chapter 6 Discovering New Materials
Chapter 7 Coda
Bibliography
Footnotes
Glossary
Index
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