Interpretable machine-learning prediction of DFT energies per atom and identification of magic numbers in coinage-metal nanoclusters ( <i>N</i> ≤ 55) from the open quantum cluster database
- DOI
- 10.1039/d6cp01474g
- Published
- 2026
- Container
- Physical Chemistry Chemical Physics
- Publisher
- Royal Society of Chemistry (RSC)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1039/d6cp01474g,
title = {Interpretable machine-learning prediction of DFT energies per atom and identification of magic numbers in coinage-metal nanoclusters (
<i>N</i>
≤ 55) from the open quantum cluster database},
author = {Ali A. Khairbek and Maha I. Al-Zaben and Abdullah Yahya Abdullah Alzahrani and Renjith Thomas},
year = {2026},
journal = {Physical Chemistry Chemical Physics},
doi = {10.1039/d6cp01474g},
url = {https://doi.org/10.1039/d6cp01474g}
}RIS
TY - JOUR
TI - Interpretable machine-learning prediction of DFT energies per atom and identification of magic numbers in coinage-metal nanoclusters (
<i>N</i>
≤ 55) from the open quantum cluster database
AU - Ali A. Khairbek
AU - Maha I. Al-Zaben
AU - Abdullah Yahya Abdullah Alzahrani
AU - Renjith Thomas
PY - 2026
JO - Physical Chemistry Chemical Physics
DO - 10.1039/d6cp01474g
UR - https://doi.org/10.1039/d6cp01474g
ER - APA
Khairbek, A. A., Al-Zaben, M. I., Alzahrani, A. Y. A., & Thomas, R. (2026). Interpretable machine-learning prediction of DFT energies per atom and identification of magic numbers in coinage-metal nanoclusters ( <i>N</i> ≤ 55) from the open quantum cluster database. Physical Chemistry Chemical Physics. https://doi.org/10.1039/d6cp01474g
Source records
- crossref · retrieved 2026-11-10T08:52:34.569Z