A Topological\-Geometrical Descriptor (TGD) for Scalable and Interpretable Modeling of Two\-Dimensional Carbon Networks
Abstract
We introduce a compact Topological-Geometrical Descriptor (TGD) for planar two-dimensional carbon networks. Atomic coordinates are mapped to a periodic planar graph and represented through ring populations, topological-charge statistics, projected ring geometry, and dual-graph defect adjacency. The resulting modeling vector contains 19 physically interpretable features. We evaluate TGD on 120 published planar carbon allotropes using the DFT relative energy per atom with respect to graphene as the target. Repeated five-fold cross-validation with gradient boosting yields a mean absolute error (MAE) of 0.0826 +/- 0.0138 eV/atom and R2 = 0.803 +/- 0.074. Under the same validation protocol, TGD gives a mean MAE of 0.0828 eV/atom, compared with 0.0929 eV/atom for a 203-feature Matminer representation. We further analyze 360 vacancy-defected graphene configurations spanning 12 vacancy concentrations from 2 to 24 percent. When entire vacancy concentrations are held out, TGD gives a pooled MAE of 0.269 eV/vacancy and R2 = 0.861, outperforming simple baselines based on vacancy concentration alone or non-hexagonal-ring fraction alone. Sensitivity tests show that the inferred edge sets are stable to moderate changes in the adaptive bond factor. Finally, the mean absolute topological charge is examined as a one-variable empirical surrogate for the allotrope dataset and is treated explicitly as dataset-specific rather than universal. These results show that TGD combines compact prediction with direct structural interpretation while making clear the present restriction to single-layer planar carbon networks.
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BibTeX
@misc{oliveira2026topologicalgeometrical,
title = {{A Topological\-Geometrical Descriptor (TGD) for Scalable and Interpretable Modeling of Two\-Dimensional Carbon Networks}},
author = {Rômulo Uriel Liberal de Oliveira and Raphael Matozo Tromer},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.20216},
eprint = {2610.20216},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.20216},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A Topological\-Geometrical Descriptor (TGD) for Scalable and Interpretable Modeling of Two\-Dimensional Carbon Networks
AU - Rômulo Uriel Liberal de Oliveira
AU - Raphael Matozo Tromer
PY - 2026
DA - 2026/10/14
PB - arXiv
DO - 10.48550/arxiv.2610.20216
UR - https://arxiv.org/abs/2610.20216
AB - We introduce a compact Topological-Geometrical Descriptor (TGD) for planar two-dimensional carbon networks. Atomic coordinates are mapped to a periodic planar graph and represented through ring populations, topological-charge statistics, projected ring geometry, and dual-graph defect adjacency. The resulting modeling vector contains 19 physically interpretable features. We evaluate TGD on 120 published planar carbon allotropes using the DFT relative energy per atom with respect to graphene as the target. Repeated five-fold cross-validation with gradient boosting yields a mean absolute error (MAE) of 0.0826 +/- 0.0138 eV/atom and R2 = 0.803 +/- 0.074. Under the same validation protocol, TGD gives a mean MAE of 0.0828 eV/atom, compared with 0.0929 eV/atom for a 203-feature Matminer representation. We further analyze 360 vacancy-defected graphene configurations spanning 12 vacancy concentrations from 2 to 24 percent. When entire vacancy concentrations are held out, TGD gives a pooled MAE of 0.269 eV/vacancy and R2 = 0.861, outperforming simple baselines based on vacancy concentration alone or non-hexagonal-ring fraction alone. Sensitivity tests show that the inferred edge sets are stable to moderate changes in the adaptive bond factor. Finally, the mean absolute topological charge is examined as a one-variable empirical surrogate for the allotrope dataset and is treated explicitly as dataset-specific rather than universal. These results show that TGD combines compact prediction with direct structural interpretation while making clear the present restriction to single-layer planar carbon networks.
ER -