Image-based machine learning for materials science (Record no. 55372)
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| fixed length control field | 02035nam a2200181Ia 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MX-MdCICY |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250625164352.0 |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | CICY |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) | |
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) | B-21305 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250602s9999 xx |||||s2 |||| ||und|d |
| 245 10 - TITLE STATEMENT | |
| Title | Image-based machine learning for materials science |
| 490 0# - SERIES STATEMENT | |
| Series statement | Journal of Applied Physics. 132(10), 100701, 2022, DOI: 10.1063/5.0087381 |
| 520 3# - SUMMARY, ETC. | |
| Summary, etc. | Materials research studies are dealing with a large number of images, which can now be facilitated via image-based machine learning techniques. In this article, we review recent progress of machine learning-driven image recognition and analysis for the materials and chemical domains. First, the image-based machine learning that facilitates the property prediction of chemicals or materials is discussed. Second, the analysis of nanoscale images including those from a scanning electron microscope and a transmission electron microscope is discussed, which is followed by the discussion about the identification of molecular structures via image recognition. Subsequently, the image-based machine learning works to identify and classify various practical materials such as metal, ceramics, and polymers are provided, and the image recognition for a range of real-scenario device applications such as solar cells is provided in detail. Finally, suggestions and future outlook for image-based machine learning for classification and prediction tasks in the materials and chemical science are presented. This article highlights the importance of the integration of the image-based machine learning method into materials and chemical science and calls for a large-scale deployment of image-based machine learning methods for prediction and classification of images in materials and chemical science. © 2022 Author(s). |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Zhang L. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Shao S. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://drive.google.com/file/d/1JCma6Sh-hpcCG9-m8PYBrzUea9x0JtDr/view?usp=drivesdk">https://drive.google.com/file/d/1JCma6Sh-hpcCG9-m8PYBrzUea9x0JtDr/view?usp=drivesdk</a> |
| Public note | Para ver el documento ingresa a Google con tu cuenta @cicy.edu.mx |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Clasificación local |
| Koha item type | Documentos solicitados |
| Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection | Home library | Current library | Shelving location | Date acquired | Total checkouts | Full call number | Date last seen | Price effective from | Koha item type |
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| Clasificación local | Ref1 | CICY | CICY | Documento préstamo interbibliotecario | 25.06.2025 | B-21305 | 25.06.2025 | 25.06.2025 | Documentos solicitados |