Accelerated Materials Discovery
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Edited by:
Phil De Luna
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 / Editor information
Phil De Luna, National Research Council of Canada, Toronto, Canada.
Reviews
Watch our book talk with the author here: https://www.youtube.com/watch?v=hsy3DaqCCPI
Topics
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Frontmatter
I -
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Preface
V -
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Contents
VII -
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List of contributors
IX -
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1 An overview of accelerated materials discovery
1 -
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2 Artificial intelligence for catalysis
27 -
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3 Artificial intelligence for materials spectroscopy
65 -
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4 Flexible automation for self-driving laboratories
105 -
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5 Algorithms for materials discovery
123 -
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6 Industrial materials informatics
165 -
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About the editor
201 -
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Index
203
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