Machine learning in materials science / (Record no. 82153)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02876nam a2200409 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 9780841299467 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | DACS |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230516163028.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 100319s2022 dcua ob 101 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780841299467 |
| Qualifying information | electronic |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1021/acsinfocus.7e5033 |
| Source of number or code | doi |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (CaBNVSL)slc00002820 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | NjRocCCS |
| Language of cataloging | eng |
| Description conventions | rda |
| Transcribing agency | NjRocCCS |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | TA404.23 |
| Item number | .B886 2022eb |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 620.11 |
| Edition number | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Butler, Keith T., |
| Relator term | author. |
| Affiliation | Rutherford Appleton Laboratory. |
| 9 (RLIN) | 67855 |
| 245 10 - TITLE STATEMENT | |
| Title | Machine learning in materials science / |
| Statement of responsibility, etc. | Keith T. Butler, Felipe Oviedo & Pieremanuele Canepa. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Washington, DC, USA : |
| Name of producer, publisher, distributor, manufacturer | American Chemical Society, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource : |
| Other physical details | illustrations (some color). |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Source | rdacarrier |
| 490 1# - SERIES STATEMENT | |
| Series statement | ACS in focus, |
| International Standard Serial Number | 2691-8307 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Includes bibliographical references and index. |
| 505 00 - FORMATTED CONTENTS NOTE | |
| Title | Applying Machine Learning to Materials Science -- |
| -- | Building Trust in Machine Learning -- |
| -- | Machine Learning for Materials Simulations -- |
| -- | Analyzing Experimental Data -- |
| -- | Closed-Loop Optimization and Active Learning for Materials -- |
| -- | Discovering New Materials -- |
| -- | Coda. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | " 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."-- |
| Assigning source | Provided by publisher. |
| 590 ## - LOCAL NOTE (RLIN) | |
| Local note | American Chemical Society, ACS In Focus eBooks - 2022 Front Files. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Materials |
| General subdivision | Data processing. |
| 9 (RLIN) | 19619 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Materials science |
| General subdivision | Mathematical models. |
| 9 (RLIN) | 14849 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine learning |
| General subdivision | Industrial applications. |
| 9 (RLIN) | 12876 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Oviedo, Felipe, |
| Relator term | author. |
| Affiliation | Microsoft AI For Good and Massachusetts Institute of Technology. |
| 9 (RLIN) | 67856 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Canepa, Pieremanuele, |
| Relator term | author. |
| Affiliation | National University of Singapore. |
| 9 (RLIN) | 67857 |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | American Chemical Society. |
| 9 (RLIN) | 67532 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | ACS in focus, |
| International Standard Serial Number | 2691-8307. |
| 9 (RLIN) | 67858 |
| 856 4# - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://dx.doi.org/10.1021/acsinfocus.7e5033">http://dx.doi.org/10.1021/acsinfocus.7e5033</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
No items available.