Explainable machine learning reveals topological control of vacancy energetics in 2D carbon.
- DOI
- 10.1039/d6cp02276f
- Published
- 2026 Nov 9
- Container
- Physical chemistry chemical physics : PCCP
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1039/d6cp02276f,
title = {Explainable machine learning reveals topological control of vacancy energetics in 2D carbon.},
author = {Pereira Junior ML and Félix IM and de Melo LP and de Mendonça FLL and Azevedo DL and Tromer RM},
year = {2026},
journal = {Physical chemistry chemical physics : PCCP},
doi = {10.1039/d6cp02276f},
url = {https://doi.org/10.1039/d6cp02276f}
}RIS
TY - JOUR TI - Explainable machine learning reveals topological control of vacancy energetics in 2D carbon. AU - Pereira Junior ML AU - Félix IM AU - de Melo LP AU - de Mendonça FLL AU - Azevedo DL AU - Tromer RM PY - 2026 JO - Physical chemistry chemical physics : PCCP DO - 10.1039/d6cp02276f UR - https://doi.org/10.1039/d6cp02276f ER -
APA
ML, P. J., IM, F., LP, D. M., FLL, D. M., DL, A., & RM, T. (2026). Explainable machine learning reveals topological control of vacancy energetics in 2D carbon.. Physical chemistry chemical physics : PCCP. https://doi.org/10.1039/d6cp02276f
Source records
- pubmed · retrieved 2026-11-10T07:17:26.186Z