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

Ali A. Khairbek; Maha I. Al-Zaben; Abdullah Yahya Abdullah Alzahrani; Renjith Thomas

doi:10.1039/d6cp01474g

January 1, 2026Physical Chemistry Chemical Physics

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

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Key Points

This research aims to use machine learning to predict DFT energies per atom in coinage-metal nanoclusters and identify their magic numbers.
Applied machine-learning algorithms to analyze data from the open quantum cluster database.
Focused on Cu, Ag, and Au nanoclusters with sizes up to 55 atoms.
Evaluated size-dependent stability using DFT calculations.
Identified specific magic numbers correlating with enhanced stability in the nanoclusters.
Predicted DFT energies per atom showing significant variation across sizes, aiding future screenings.
Results indicate the utility of machine learning in simplifying the prediction process of complex nanocluster behaviors.

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Implication

Identifies magic numbers and predicts DFT energies in coinage-metal nanoclusters, suggesting new insights for stability.

Abstract

Atomically precise coinage-metal nanoclusters (Cu, Ag, and Au) exhibit size-dependent stability critical for catalysis, plasmonics, and photocatalysis, yet first-principles screening becomes prohibitive beyond ∼55 atoms.

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Khairbek et al. (2026) studied this question.

synapsesocial.com/papers/6a1bd21d5783ba022b6fd8f0 https://doi.org/10.1039/d6cp01474g

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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

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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