Symm-CGNN: Symmetry-Information-Enhanced Crystal Graph Neural Network for High-Symmetry Point Band Gap Prediction.

Xu Q, Wu J, Zhang X, Zhu S

Open source

DOI: 10.3390/nano16140871
Published: 2026 Jul 15
Container: Nanomaterials (Basel, Switzerland)
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3390/nano16140871,
  title = {Symm-CGNN: Symmetry-Information-Enhanced Crystal Graph Neural Network for High-Symmetry Point Band Gap Prediction.},
  author = {Xu Q and Wu J and Zhang X and Zhu S},
  year = {2026},
  journal = {Nanomaterials (Basel, Switzerland)},
  doi = {10.3390/nano16140871},
  url = {https://doi.org/10.3390/nano16140871}
}

RIS

TY  - JOUR
TI  - Symm-CGNN: Symmetry-Information-Enhanced Crystal Graph Neural Network for High-Symmetry Point Band Gap Prediction.
AU  - Xu Q
AU  - Wu J
AU  - Zhang X
AU  - Zhu S
PY  - 2026
JO  - Nanomaterials (Basel, Switzerland)
DO  - 10.3390/nano16140871
UR  - https://doi.org/10.3390/nano16140871
ER  -

APA

Q, X., J, W., X, Z., & S, Z. (2026). Symm-CGNN: Symmetry-Information-Enhanced Crystal Graph Neural Network for High-Symmetry Point Band Gap Prediction.. Nanomaterials (Basel, Switzerland). https://doi.org/10.3390/nano16140871

Source records

pubmed · retrieved 2026-09-27T14:22:35.299Z
europe-pmc · retrieved 2026-09-27T14:22:35.314Z
doaj · retrieved 2026-09-27T14:22:35.297Z