Neural-Network-Based Molecular Dynamics Unravel Generic Multitransition States in CVD Growth of Planar Honeycomb 2D Lattices.
- DOI
- 10.1021/jacs.6c12198
- Published
- 2026 Sep 23
- Container
- Journal of the American Chemical Society
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1021/jacs.6c12198,
title = {Neural-Network-Based Molecular Dynamics Unravel Generic Multitransition States in CVD Growth of Planar Honeycomb 2D Lattices.},
author = {Lu S and Zhang Z and Jiang J and Liu Z and Cheng T and Wang J and Zeng XC},
year = {2026},
journal = {Journal of the American Chemical Society},
doi = {10.1021/jacs.6c12198},
url = {https://doi.org/10.1021/jacs.6c12198}
}RIS
TY - JOUR TI - Neural-Network-Based Molecular Dynamics Unravel Generic Multitransition States in CVD Growth of Planar Honeycomb 2D Lattices. AU - Lu S AU - Zhang Z AU - Jiang J AU - Liu Z AU - Cheng T AU - Wang J AU - Zeng XC PY - 2026 JO - Journal of the American Chemical Society DO - 10.1021/jacs.6c12198 UR - https://doi.org/10.1021/jacs.6c12198 ER -
APA
S, L., Z, Z., J, J., Z, L., T, C., J, W., & XC, Z. (2026). Neural-Network-Based Molecular Dynamics Unravel Generic Multitransition States in CVD Growth of Planar Honeycomb 2D Lattices.. Journal of the American Chemical Society. https://doi.org/10.1021/jacs.6c12198
Source records
- pubmed · retrieved 2026-11-10T14:24:09.020Z
- europe-pmc · retrieved 2026-11-10T14:24:09.037Z