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230607 ||| eng |
| 020 |
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|a 9780841297623
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| 050 |
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4 |
|a TA404.23
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| 100 |
1 |
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|a An, Yuling
|e [editor]
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| 245 |
0 |
0 |
|a Machine learning in materials informatics
|b methods and applications
|c Yuling An, editor, Machine Learning and Enterprising Informatics Materials Science Department, Schrödinger, Inc. New York City, New York, United States ; sponsored by the ACS Division of Chemical Information
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| 260 |
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|a Washington, DC
|b American Chemical Society
|c 2022, 2022
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| 300 |
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|a 266 pages
|b illustrations
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| 505 |
0 |
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|a Includes bibliographical references and index
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| 653 |
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|a Machine learning / Industrial applications
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| 653 |
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|a Materials science / Mathematical models
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| 653 |
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|a Materials / Data processing
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| 710 |
2 |
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|a American Chemical Society
|b Division of Chemical Information
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b ACS
|a ACS Symposium Series
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| 490 |
0 |
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|a ACS symposium series
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| 500 |
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|a Distributed in print by Oxford University Press
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| 028 |
5 |
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|a 10.1021/bk-2022-1416
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| 856 |
4 |
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|u http://dx.doi.org/10.1021/bk-2022-1416
|x Verlag
|3 Volltext
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| 082 |
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|a 620.11
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| 520 |
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|a "Utilizing Machine Learning in Materials Research. Machine learning has become one of the most exciting tools in materials science in recent years, based on the enormous number of publications and presentations that apply this approach to a broad range of fields. This work provides a comprehensive overview of how machine learning can be applied in various fields of materials science research to improve the efficiency and effectiveness of many challenging projects. Featuring concrete case studies with in-depth discussion, this work will inspire materials scientists to explore the potential of machine learning technology to expedite materials innovation."--
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