Prediction of atomic stress fields using cycle-consistent adversarial neural networks based on unpaired and unmatched sparse datasets
Abstract
Deep learning holds great promise for applications in materials science, including the discovery of physical laws and materials design.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2022
- Source ID
- 10.1039/d2ma00223j
Entities
People
- Markus J. Buehler
Organizations
- Army Research Office
- Massachusetts Institute of Technology
- National Institutes of Health
- Office of Naval Research