Accelerating the Structure Exploration of Diverse Bi-Pt Nanoclusters via Physics-Informed Machine Learning Potential and Particle Swarm Optimization

R Vangheluwe and C Clavaguéra and M Truong and D Domin and HC Pham and M Marinica and N Van-Oanh, CHEMPHYSCHEM, 26, 2500268 (2025).

DOI: 10.1002/cphc.202500268

Bimetallic Bi-Pt nanoclusters exhibit diverse structural motifs, including core-shell, Janus, and mixed alloy configurations, due to the unique bonding characteristics between Bi and Pt atoms. Using density functional theory refinements from ChIMES physically machine-learned potential and CALYPSO particle swarm optimization global searches, 34 Bi20-Pt20 nanoclusters are systematically classified. The results reveal that Bi atoms predominantly occupy surface sites, driven by charge transfer effects. Cohesive energy trends alone prove insufficient for structure differentiation, necessitating a data-driven approach employing principal component analysis and K-means clustering. Furthermore, vibrational, electronic, and infrared spectral analyses provide additional insights into structure-property relationships. The findings offer an original framework for the automated classification and analysis of bimetallic nanoclusters, enhancing the understanding of their stability and functional properties.

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