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>
Main Author: | Buehler, Markus J |
---|---|
Other Authors: | Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics |
Format: | Article |
Language: | English |
Published: |
Royal Society of Chemistry (RSC)
2022
|
Online Access: | https://hdl.handle.net/1721.1/146552 |
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