Interpretable machine-learning prediction of DFT energies per atom and identification of magic numbers in coinage-metal nanoclusters (N ≤ 55) from the open quantum cluster database.
- DOI
- 10.1039/d6cp01474g
- Published
- 2026 Jun 17
- Container
- Physical chemistry chemical physics : PCCP
- Publisher
- Not recorded
- 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 (N ≤ 55) from the open quantum cluster database.},
author = {Khairbek AA and Al-Zaben MI and Alzahrani AYA and Thomas R},
year = {2026},
journal = {Physical chemistry chemical physics : PCCP},
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 (N ≤ 55) from the open quantum cluster database. AU - Khairbek AA AU - Al-Zaben MI AU - Alzahrani AYA AU - Thomas R PY - 2026 JO - Physical chemistry chemical physics : PCCP DO - 10.1039/d6cp01474g UR - https://doi.org/10.1039/d6cp01474g ER -
APA
AA, K., MI, A., AYA, A., & R, T. (2026). Interpretable machine-learning prediction of DFT energies per atom and identification of magic numbers in coinage-metal nanoclusters (N ≤ 55) from the open quantum cluster database.. Physical chemistry chemical physics : PCCP. https://doi.org/10.1039/d6cp01474g
Source records
- pubmed · retrieved 2026-09-25T02:40:45.587Z