Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders
- DOI
- 10.1038/s41524-024-01250-5
- Published
- 2024-08-14
- Container
- npj Computational Materials
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41524-024-01250-5,
title = {Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders},
author = {Mani Valleti and Maxim Ziatdinov and Yongtao Liu and Sergei V. Kalinin},
year = {2024},
journal = {npj Computational Materials},
doi = {10.1038/s41524-024-01250-5},
url = {https://doi.org/10.1038/s41524-024-01250-5}
}RIS
TY - JOUR TI - Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders AU - Mani Valleti AU - Maxim Ziatdinov AU - Yongtao Liu AU - Sergei V. Kalinin PY - 2024 JO - npj Computational Materials DO - 10.1038/s41524-024-01250-5 UR - https://doi.org/10.1038/s41524-024-01250-5 ER -
APA
Valleti, M., Ziatdinov, M., Liu, Y., & Kalinin, S. V. (2024). Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders. npj Computational Materials. https://doi.org/10.1038/s41524-024-01250-5
Source records
- crossref · retrieved 2026-11-06T11:56:03.060Z