Accelerated Materials Discovery : : How to Use Artificial Intelligence to Speed Up Development / / ed. by Phil De Luna.

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 looki...

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Superior document:Title is part of eBook package: De Gruyter DG Plus DeG Package 2022 Part 1
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Place / Publishing House:Berlin ;, Boston : : De Gruyter, , [2022]
©2022
Year of Publication:2022
Language:English
Series:De Gruyter STEM
Online Access:
Physical Description:1 online resource (X, 205 p.)
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245 0 0 |a Accelerated Materials Discovery :  |b How to Use Artificial Intelligence to Speed Up Development /  |c ed. by Phil De Luna. 
264 1 |a Berlin ;  |a Boston :   |b De Gruyter,   |c [2022] 
264 4 |c ©2022 
300 |a 1 online resource (X, 205 p.) 
336 |a text  |b txt  |2 rdacontent 
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505 0 0 |t Frontmatter --   |t Preface --   |t Contents --   |t List of contributors --   |t 1 An overview of accelerated materials discovery --   |t 2 Artificial intelligence for catalysis --   |t 3 Artificial intelligence for materials spectroscopy --   |t 4 Flexible automation for self-driving laboratories --   |t 5 Algorithms for materials discovery --   |t 6 Industrial materials informatics --   |t About the editor --   |t Index 
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520 |a 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). 
530 |a Issued also in print. 
538 |a Mode of access: Internet via World Wide Web. 
546 |a In English. 
588 0 |a Description based on online resource; title from PDF title page (publisher's Web site, viewed 02. Mai 2023) 
650 4 |a Materialwirtschaft. 
650 4 |a Materialwissenschaft. 
650 4 |a künstliche Intelligenz. 
650 4 |a maschinelles Lernen. 
653 |a Artificial Intelligence. 
653 |a Machine Learning. 
653 |a Materials Development. 
653 |a Materials Discovery. 
700 1 |a Berlinguette, Curtis P.,   |e contributor.  |4 ctb  |4 https://id.loc.gov/vocabulary/relators/ctb 
700 1 |a Black, Robert,   |e contributor.  |4 ctb  |4 https://id.loc.gov/vocabulary/relators/ctb 
700 1 |a Brown, Amanda K.,   |e contributor.  |4 ctb  |4 https://id.loc.gov/vocabulary/relators/ctb 
700 1 |a Carbone, Matthew R.,   |e contributor.  |4 ctb  |4 https://id.loc.gov/vocabulary/relators/ctb 
700 1 |a Dagdelen, John,   |e contributor.  |4 ctb  |4 https://id.loc.gov/vocabulary/relators/ctb 
700 1 |a De Luna, Phil,   |e editor.  |4 edt  |4 http://id.loc.gov/vocabulary/relators/edt 
700 1 |a Dunn, Alex,   |e contributor.  |4 ctb  |4 https://id.loc.gov/vocabulary/relators/ctb 
700 1 |a Gates, Ian D.,   |e contributor.  |4 ctb  |4 https://id.loc.gov/vocabulary/relators/ctb 
700 1 |a Gomes, Carla P.,   |e contributor.  |4 ctb  |4 https://id.loc.gov/vocabulary/relators/ctb 
700 1 |a Gregoire, John M.,   |e contributor.  |4 ctb  |4 https://id.loc.gov/vocabulary/relators/ctb 
700 1 |a Gusarov, Sergey,   |e contributor.  |4 ctb  |4 https://id.loc.gov/vocabulary/relators/ctb 
700 1 |a Guzman, Gustavo,   |e contributor.  |4 ctb  |4 https://id.loc.gov/vocabulary/relators/ctb 
700 1 |a Hein, Jason E.,   |e contributor.  |4 ctb  |4 https://id.loc.gov/vocabulary/relators/ctb 
700 1 |a Hung, Linda,   |e contributor.  |4 ctb  |4 https://id.loc.gov/vocabulary/relators/ctb 
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700 1 |a Yano, Junko,   |e contributor.  |4 ctb  |4 https://id.loc.gov/vocabulary/relators/ctb 
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