Prediction of Atomic Stress Fields using Cycle-Consistent Adversarial Neural Networks based on Unpaired and Unmatched Sparse Datasets

Author(s)
Buehler, Markus J
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Creative Commons Attribution NonCommercial License 3.0 https://creativecommons.org/licenses/by-nc/3.0/
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Abstract
<jats:p>Deep learning holds great promise for applications in materials science, including the discovery of physical laws and materials design.</jats:p>
Date issued
2022
URI
https://hdl.handle.net/1721.1/146552
Department
Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics; Massachusetts Institute of Technology. Center for Computational Science and Engineering
Journal
Materials Advances
Publisher
Royal Society of Chemistry (RSC)
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).
Version: Final published version
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