Prediction of Atomic Stress Fields using Cycle-Consistent Adversarial Neural Networks based on Unpaired and Unmatched Sparse Datasets
<jats:p>Deep learning holds great promise for applications in materials science, including the discovery of physical laws and materials design.</jats:p>
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Royal Society of Chemistry (RSC)
2022
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| Online Access: | https://hdl.handle.net/1721.1/146552 |
| _version_ | 1811092380251586560 |
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| author | Buehler, Markus J |
| author2 | Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics |
| author_facet | Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics Buehler, Markus J |
| author_sort | Buehler, Markus J |
| collection | MIT |
| description | <jats:p>Deep learning holds great promise for applications in materials science, including the discovery of physical laws and materials design.</jats:p> |
| first_indexed | 2024-09-23T15:17:15Z |
| format | Article |
| id | mit-1721.1/146552 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:17:15Z |
| publishDate | 2022 |
| publisher | Royal Society of Chemistry (RSC) |
| record_format | dspace |
| spelling | mit-1721.1/1465522023-01-27T19:50:25Z Prediction of Atomic Stress Fields using Cycle-Consistent Adversarial Neural Networks based on Unpaired and Unmatched Sparse Datasets Buehler, Markus J Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics Massachusetts Institute of Technology. Center for Computational Science and Engineering <jats:p>Deep learning holds great promise for applications in materials science, including the discovery of physical laws and materials design.</jats:p> 2022-11-18T19:38:48Z 2022-11-18T19:38:48Z 2022 2022-11-18T19:34:47Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/146552 Buehler, Markus J. 2022. "Prediction of Atomic Stress Fields using Cycle-Consistent Adversarial Neural Networks based on Unpaired and Unmatched Sparse Datasets." Materials Advances, 3 (15). en 10.1039/D2MA00223J Materials Advances Creative Commons Attribution NonCommercial License 3.0 https://creativecommons.org/licenses/by-nc/3.0/ application/pdf Royal Society of Chemistry (RSC) Royal Society of Chemistry (RSC) |
| spellingShingle | Buehler, Markus J Prediction of Atomic Stress Fields using Cycle-Consistent Adversarial Neural Networks based on Unpaired and Unmatched Sparse Datasets |
| title | Prediction of Atomic Stress Fields using Cycle-Consistent Adversarial Neural Networks based on Unpaired and Unmatched Sparse Datasets |
| title_full | Prediction of Atomic Stress Fields using Cycle-Consistent Adversarial Neural Networks based on Unpaired and Unmatched Sparse Datasets |
| title_fullStr | Prediction of Atomic Stress Fields using Cycle-Consistent Adversarial Neural Networks based on Unpaired and Unmatched Sparse Datasets |
| title_full_unstemmed | Prediction of Atomic Stress Fields using Cycle-Consistent Adversarial Neural Networks based on Unpaired and Unmatched Sparse Datasets |
| title_short | Prediction of Atomic Stress Fields using Cycle-Consistent Adversarial Neural Networks based on Unpaired and Unmatched Sparse Datasets |
| title_sort | prediction of atomic stress fields using cycle consistent adversarial neural networks based on unpaired and unmatched sparse datasets |
| url | https://hdl.handle.net/1721.1/146552 |
| work_keys_str_mv | AT buehlermarkusj predictionofatomicstressfieldsusingcycleconsistentadversarialneuralnetworksbasedonunpairedandunmatchedsparsedatasets |