Identifiability limits and deep-learning-assisted reconstruction of rotational density matrices for symmetric-top molecules
- DOI
- 10.1063/5.0332944
- Published
- 2026-07-15
- Container
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1063/5.0332944,
title = {Identifiability limits and deep-learning-assisted reconstruction of rotational density matrices for symmetric-top molecules},
author = {Bowen Dong and Ming Zhang and Yicheng Zhuang and Shutao Zhang and Dongyu Liu and Mohan Xu and Sizhe Li and Anatoly A. Ischenko and Haitan Xu and R. J. Dwayne Miller and Zheng Li},
year = {2026},
journal = {The Journal of Chemical Physics},
doi = {10.1063/5.0332944},
url = {https://doi.org/10.1063/5.0332944}
}RIS
TY - JOUR TI - Identifiability limits and deep-learning-assisted reconstruction of rotational density matrices for symmetric-top molecules AU - Bowen Dong AU - Ming Zhang AU - Yicheng Zhuang AU - Shutao Zhang AU - Dongyu Liu AU - Mohan Xu AU - Sizhe Li AU - Anatoly A. Ischenko AU - Haitan Xu AU - R. J. Dwayne Miller AU - Zheng Li PY - 2026 JO - The Journal of Chemical Physics DO - 10.1063/5.0332944 UR - https://doi.org/10.1063/5.0332944 ER -
APA
Dong, B., Zhang, M., Zhuang, Y., Zhang, S., Liu, D., Xu, M., Li, S., Ischenko, A. A., Xu, H., Miller, R. J. D., & Li, Z. (2026). Identifiability limits and deep-learning-assisted reconstruction of rotational density matrices for symmetric-top molecules. The Journal of Chemical Physics. https://doi.org/10.1063/5.0332944
Source records
- crossref · retrieved 2026-09-24T19:41:43.194Z