Global Structure Search of Nickel Clusters Using Basin Hopping and Gaussian Approximation Potential

Nickel clusters have gathered significant attention in nanomaterials research due to their definite electronic and catalytic properties that strongly depend on their atomic configuration. Predicting their low-energy structures remains challenging due to the complex potential energy landscape associated with metallic bonding. Accurately predicting the configurations of Ni clusters is essential for understanding their fundamental properties and advancing the development of various nanoscale applications. This study identifies the lowestenergy Ni nanoclusters across various sizes using a machine learning potential called the Gaussian Approximation Potential (GAP), employed in conjunction with the Basin Hopping (BH) structure search optimization technique. The investigations are conducted over sizes ranging from 2 to 150 atoms, showcasing the robustness and accuracy of BH techniques for predicting low-energy minima. The GAP model accelerates the search for stable structures and enables reliable identification of low-energy candidates for 149 cluster sizes. The resulting structures do not exhibit a smooth size-dependent trend but instead display localized transitions where different motifs become competitive. Additional analysis using SOAP descriptors, second-order energy differences, and comparisons with previously reported structures further clarifies the structural evolution and stability patterns across the full size range.

Author dc:creator

• Sanchari Saha (6169274)

• Engineering
• Mechanical

Statement dc:rights

• In Copyright

DOI dc:identifier

https://doi.org/10.25417/uic.32991926.v1

OAI identifier oai:identifier

oai:figshare.com:article/32991926