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Parametrization of Gaussian Approximation Potential for the Global Optimization of Magnesium Clusters MgN (N≤100)

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GridSearch and active learning optimize Gaussian approximation potential for magnesium clusters, revealing stable structures.

Global optimization identified new energetically favorable clusters Mg51-Mg53, emphasizing structural reliability.
The study reports new global minima candidates for 'magic' clusters Mg59, Mg69, Mg74, and Mg99, suggesting significant findings.
A two-stage GridSearch approach enhanced optimization of the GAP model parameters, improving parameterization outcomes.
The presence of hcp-like motifs did not affect structural stability in clusters of less than 100 magnesium atoms, indicating consistency.

Steshin et al. (2025) studied this question.

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