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

Raphaël Vangheluwe¹, Carine Clavaguéra¹, Minh-Tue Truong¹

¹Université Paris-Saclay, CNRS, Institut de Chimie Physique, UMR 8000, 91405, Orsay, France.

Chemphyschem : a European Journal of Chemical Physics and Physical Chemistry

|September 3, 2025

Summary

Bismuth-platinum (Bi-Pt) nanoclusters show varied structures. A data-driven approach successfully classified these bimetallic nanoclusters, revealing bismuth

Keywords:

bimetallic nanoparticles bismuth‐platinum nanoclusters k‐means clustering machine learning potentials particle swarm optimization principal component analysis

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Area of Science:

Materials Science
Computational Chemistry
Nanotechnology

Background:

Bimetallic nanoclusters, such as bismuth-platinum (Bi-Pt), display complex structural arrangements.
Understanding these structures is crucial for predicting and optimizing their properties.

Purpose of the Study:

To systematically classify Bi-Pt nanoclusters using advanced computational methods.
To establish a framework for analyzing structure-property relationships in bimetallic nanoclusters.

Main Methods:

Density functional theory (DFT) calculations refined by machine-learned potentials (ChIMES).
Global structure searches using CALYPSO particle swarm optimization.
Data-driven classification via principal component analysis (PCA) and K-means clustering.

Main Results:

34 distinct Bi20-Pt20 nanocluster structures were identified and classified.
Bismuth atoms preferentially occupy surface sites due to charge transfer effects.
Cohesive energy alone was insufficient for differentiating structures; a data-driven approach was necessary.

Conclusions:

A novel computational framework enables automated classification of bimetallic nanoclusters.
Insights into stability and functional properties are gained through vibrational, electronic, and spectral analyses.
This work advances the understanding of nanocluster structural diversity and behavior.