Prediction of Atomic Stress Fields using Cycle-Consistent Adversarial Neural Networks based on Unpaired and Unmatched Sparse Datasets
Name
d2ma00223j.pdf
Description
Published version
Size
9.01 MB
Format
Adobe PDF
Checksum (MD5)
bea7c83950e18f7bd4242d59919d8ba8
Author(s)
Buehler, Markus J
Date Issued
2022
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
Abstract
Deep learning holds great promise for applications in materials science, including the discovery of physical laws and materials design.
MIT Department
Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics
Massachusetts Institute of Technology. Center for Computational Science and Engineering
Terms of Use
Creative Commons Attribution NonCommercial License 3.0
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DOI of Published Version
https://doi.org/10.1039/D2MA00223J
