A data-efficient machine-learning approach for modeling the photodynamics of all-trans hexatriene based on multireference configuration interaction calculations.
- DOI
- 10.1039/d6fd00061d
- Published
- 2026 Sep 24
- Container
- Faraday discussions
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1039/d6fd00061d,
title = {A data-efficient machine-learning approach for modeling the photodynamics of all-trans hexatriene based on multireference configuration interaction calculations.},
author = {Dos Santos LGF and Chagas JCV and Martyka M and Dral PO and Barbatti M and Machado FBC and Messerly RA and Lischka H},
year = {2026},
journal = {Faraday discussions},
doi = {10.1039/d6fd00061d},
url = {https://doi.org/10.1039/d6fd00061d}
}RIS
TY - JOUR TI - A data-efficient machine-learning approach for modeling the photodynamics of all-trans hexatriene based on multireference configuration interaction calculations. AU - Dos Santos LGF AU - Chagas JCV AU - Martyka M AU - Dral PO AU - Barbatti M AU - Machado FBC AU - Messerly RA AU - Lischka H PY - 2026 JO - Faraday discussions DO - 10.1039/d6fd00061d UR - https://doi.org/10.1039/d6fd00061d ER -
APA
LGF, D. S., JCV, C., M, M., PO, D., M, B., FBC, M., RA, M., & H, L. (2026). A data-efficient machine-learning approach for modeling the photodynamics of all-trans hexatriene based on multireference configuration interaction calculations.. Faraday discussions. https://doi.org/10.1039/d6fd00061d