GAUSSIAN APPROXIMATION POTENTIALS FOR FUNCTIONALIZED Pt–Cu NANOPARTICLES

Abstract

Bimetallic Pt–Cu nanoparticles are promising catalysts for oxidation and hydrogenation reactions due to their tunable electronic and geometric properties. However, first-principles simulations of realistic nanoparticle sizes remain computationally prohibitive. In this study, Gaussian Approximation Potential (GAP) models were developed for Pt–Cu nanoparticles functionalized with a single O2 or CO molecule, achieving near-DFT accuracy in energies and forces while drastically reducing computational cost. The training dataset, derived from ab initio molecular dynamics (AIMD) trajectories at 300–1000 K, spans various morphologies (pure, core–shell, Janus, and ordered alloys) and particle sizes (38–260 atoms), capturing both thermal and structural fluctuations representative of realistic catalytic conditions. The resulting GAP models successfully reproduce DFT-level energetics and atomic forces with root-mean-square errors below 0.4 meV atom-1 for energies and 70 meV Å-1 for forces, without overfitting to any specific morphology. AIMD simulations reveal that alloying Pt with Cu enhances thermal and mechanical stability, with core–shell and Janus configurations maintaining ordered atomic coordination up to 1000 K. Radial distribution function (RDF) analysis confirms that short-range order persists at elevated temperatures, ensuring structural integrity under reactive conditions. These results demonstrate that machine-learning-based interatomic potentials provide a robust and transferable framework for exploring adsorption-driven restructuring, morphology evolution, and catalytic stability of Pt–Cu nanoparticles beyond the accessible limits of conventional DFT.

Keywords

• PtCu nanoparticles
• Bimetallic alloys
• Machine learning
• Gaussian Approximation Potential
• Molecular dynamics

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