Rational Design of Light-Driven Molecular Motors Enabled by a Data-Efficient Machine Learning Framework for Nonadiabatic Dynamics
- DOI
- 10.1021/jacs.6c10141
- Published
- 2026-08-04
- Container
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/jacs.6c10141,
title = {Rational Design of
Light-Driven Molecular Motors Enabled
by a Data-Efficient Machine Learning Framework for Nonadiabatic Dynamics},
author = {Jianzheng Ma and Chenwei Jiang and Lei Peng and Yongliang Shi and Yingzhou Li and Zhenggang Lan and Oleg V. Prezhdo and Xin-Gao Gong and Weibin Chu},
year = {2026},
journal = {Journal of the American
Chemical Society},
doi = {10.1021/jacs.6c10141},
url = {https://doi.org/10.1021/jacs.6c10141}
}RIS
TY - JOUR TI - Rational Design of Light-Driven Molecular Motors Enabled by a Data-Efficient Machine Learning Framework for Nonadiabatic Dynamics AU - Jianzheng Ma AU - Chenwei Jiang AU - Lei Peng AU - Yongliang Shi AU - Yingzhou Li AU - Zhenggang Lan AU - Oleg V. Prezhdo AU - Xin-Gao Gong AU - Weibin Chu PY - 2026 JO - Journal of the American Chemical Society DO - 10.1021/jacs.6c10141 UR - https://doi.org/10.1021/jacs.6c10141 ER -
APA
Ma, J., Jiang, C., Peng, L., Shi, Y., Li, Y., Lan, Z., Prezhdo, O. V., Gong, X., & Chu, W. (2026). Rational Design of Light-Driven Molecular Motors Enabled by a Data-Efficient Machine Learning Framework for Nonadiabatic Dynamics. Journal of the American Chemical Society. https://doi.org/10.1021/jacs.6c10141
Source records
- crossref · retrieved 2026-09-26T13:10:09.075Z