Mining structure-property relationships in polymer nanocomposites using data driven finite element analysis and multi-task convolutional neural networks (Record no. 61926)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MX-MdCICY |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20251009160707.0 |
| 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-21836 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 251009s9999 xx 000 0 und d |
| 245 10 - TITLE STATEMENT | |
| Title | Mining structure-property relationships in polymer nanocomposites using data driven finite element analysis and multi-task convolutional neural networks |
| 490 0# - SERIES STATEMENT | |
| Series statement | Molecular Systems Design & Engineering, 5(5), p.962-975, 2020 |
| 500 ## - GENERAL NOTE | |
| General note | Artículo |
| 520 3# - SUMMARY, ETC. | |
| Summary, etc. | Data-driven methods have attracted increasingly more attention in materials research since the advent of the material genome initiative. The combination of material science with computer science, statistics, and data-driven methods aims to expediate materials research and applications and can utilize both new and archived research data. In this paper, we present a data driven and deep learning approach that builds a portion of the structure-property relationship for polymer nanocomposites. Analysis of archived experimental data motivates development of a computational model which allows demonstration of the approach and gives flexibility to sufficiently explore a wide range of structures. Taking advantages of microstructure reconstruction methods and finite element simulations, we first explore qualitative relationships between microstructure descriptors and mechanical properties, resulting in new findings regarding the interplay of interphase, volume fraction and dispersion. Then we present a novel deep learning approach that combines convolutional neural networks with multi-task learning for building quantitative correlations between microstructures and property values. The performance of the model is compared with other state-of-the-art strategies including two-point statistics and structure descriptor-based approaches. Lastly, the interpretation of the deep learning model is investigated to show that the model is able to capture physical understandings while learning. |
| 700 12 - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Wang, Y.;Zhang, M.;Lin, A.;Iyer, A.;Prasad, A. S.;Li, X.;Brinson, L. C. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://drive.google.com/file/d/1npE86E5wPWWK20md1kp5SV8k0Y8TR1Xr/view?usp=drive_link">https://drive.google.com/file/d/1npE86E5wPWWK20md1kp5SV8k0Y8TR1Xr/view?usp=drive_link</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 | 09.10.2025 | B-21836 | 09.10.2025 | 09.10.2025 | Documentos solicitados |