A Physics-Informed Machine Learning Framework for Modeling Biaxial Strain-Induced Band Gap Variation in Zig-Zag Single-Walled Carbon Nanotubes.
- DOI
- 10.3390/nano16171114
- Published
- 2026 Sep 3
- Container
- Nanomaterials (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/nano16171114,
title = {A Physics-Informed Machine Learning Framework for Modeling Biaxial Strain-Induced Band Gap Variation in Zig-Zag Single-Walled Carbon Nanotubes.},
author = {Vardar N},
year = {2026},
journal = {Nanomaterials (Basel, Switzerland)},
doi = {10.3390/nano16171114},
url = {https://doi.org/10.3390/nano16171114}
}RIS
TY - JOUR TI - A Physics-Informed Machine Learning Framework for Modeling Biaxial Strain-Induced Band Gap Variation in Zig-Zag Single-Walled Carbon Nanotubes. AU - Vardar N PY - 2026 JO - Nanomaterials (Basel, Switzerland) DO - 10.3390/nano16171114 UR - https://doi.org/10.3390/nano16171114 ER -
APA
N, V. (2026). A Physics-Informed Machine Learning Framework for Modeling Biaxial Strain-Induced Band Gap Variation in Zig-Zag Single-Walled Carbon Nanotubes.. Nanomaterials (Basel, Switzerland). https://doi.org/10.3390/nano16171114
Source records
- pubmed · retrieved 2026-11-10T10:06:04.711Z