| dc.contributor.author | Buehler, Markus J | |
| dc.date.accessioned | 2022-11-18T19:38:48Z | |
| dc.date.available | 2022-11-18T19:38:48Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/146552 | |
| dc.description.abstract | <jats:p>Deep learning holds great promise for applications in materials science, including the discovery of physical laws and materials design.</jats:p> | en_US |
| dc.language.iso | en | |
| dc.publisher | Royal Society of Chemistry (RSC) | en_US |
| dc.relation.isversionof | 10.1039/D2MA00223J | en_US |
| dc.rights | Creative Commons Attribution NonCommercial License 3.0 | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/3.0/ | en_US |
| dc.source | Royal Society of Chemistry (RSC) | en_US |
| dc.title | Prediction of Atomic Stress Fields using Cycle-Consistent Adversarial Neural Networks based on Unpaired and Unmatched Sparse Datasets | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | 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). | |
| dc.contributor.department | Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics | |
| dc.contributor.department | Massachusetts Institute of Technology. Center for Computational Science and Engineering | |
| dc.relation.journal | Materials Advances | en_US |
| dc.eprint.version | Final published version | en_US |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
| eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
| dc.date.updated | 2022-11-18T19:34:47Z | |
| dspace.orderedauthors | Buehler, MJ | en_US |
| dspace.date.submission | 2022-11-18T19:34:55Z | |
| mit.journal.volume | 3 | en_US |
| mit.journal.issue | 15 | en_US |
| mit.license | PUBLISHER_CC | |
| mit.metadata.status | Authority Work and Publication Information Needed | en_US |
