Accelerated Materials Discovery

Published by De Gruyter 2022

Accelerated Materials Discovery

How to Use Artificial Intelligence to Speed Up Development

Edited by: Phil De Luna

In the series De Gruyter STEM

https://doi.org/10.1515/9783110738087

Overview
Contents

About this book

Typical timelines to go from discovery to impact in the advanced materials sector are between 10 to 30 years. Advances in robotics and artificial intelligence are poised to accelerate the discovery and development of new materials dramatically. This book is a primer for any materials scientist looking to future-proof their careers and get ahead of the disruption that artificial intelligence and robotic automation is just starting to unleash. It is meant to be an overview of how we can use these disruptive technologies to augment and supercharge our abilities to discover new materials that will solve world’s biggest challenges.

Written by world leading experts on accelerated materials discovery from academia (UC Berkeley, Caltech, UBC, Cornell, etc.), industry (Toyota Research Institute, Citrine Informatics) and national labs (National Research Council of Canada, Lawrence Berkeley National Labs).

Highlights artificial intelligence and robotics to speed up the discovery of advanced materials
Describes machine learning algorithms, self-driving labs, AI in catalysis and spectroscopy, and industrial use cases.

Author information

Phil De Luna, National Research Council of Canada, Toronto, Canada.

Topics

Frontmatter Publicly Available Download PDF	I
Preface Publicly Available Download PDF	V
Contents Publicly Available Download PDF	VII
List of contributors Publicly Available Download PDF	IX
1 An overview of accelerated materials discovery Robert Black and Isaac Tamblyn Requires Authentication Unlicensed Licensed Download PDF	1
2 Artificial intelligence for catalysis Samira Siahrostami, Stanislav R. Stoyanov, Sergey Gusarov and Ian D. Gates Requires Authentication Unlicensed Licensed Download PDF	27
3 Artificial intelligence for materials spectroscopy Steven B. Torrisi, John M. Gregoire, Junko Yano, Matthew R. Carbone, Carla P. Gomes, Linda Hung and Santosh K. Suram Requires Authentication Unlicensed Licensed Download PDF	65
4 Flexible automation for self-driving laboratories Benjamin P. MacLeod, Fraser G. L. Parlane, Amanda K. Brown, Jason E. Hein and Curtis P. Berlinguette Requires Authentication Unlicensed Licensed Download PDF	105
5 Algorithms for materials discovery John Dagdelen and Alex Dunn Requires Authentication Unlicensed Licensed Download PDF	123
6 Industrial materials informatics Gustavo Guzman Requires Authentication Unlicensed Licensed Download PDF	165
About the editor Requires Authentication Unlicensed Licensed Download PDF	201
Index Requires Authentication Unlicensed Licensed Download PDF	203

(Deutsch)

Language: English
Publisher: De Gruyter
Audience: Graduate students, career starters, industrial scientists, government researchers, experts, and entrepreneurs.
Pages
- Front matter: 10
- Main content: 205
Illustrations
- Illustrations: 1
- Coloured Illustrations: 58
- Tables: 5
Keywords: Artificial Intelligence; Machine Learning; Materials Discovery; Materials Development.

eBook

Published: February 21, 2022
ISBN: 9783110738087

Paperback

Published: March 7, 2022
ISBN: 9783110738049