Marthe Bideault | Searcharxiv

arXiv2026

Unveiling and quantifying the topology-dependent pre-melting of nanoparticles

The melting of metallic nanoparticles is governed by surface premelting, a phenomenon traditionally modeled as the isotropic growth of a uniform liquid shell. Challenging this classical view, we report facet-dependent premelting in hexagonal close-packed Co nanoparticles, arising from the structural heterogeneity of their surface. In molecular dynamics simulations (587 to 13047 atoms), the onset of surface mobility is observed as low as 20% of the bulk melting point, driven by the early disordering of stepped $\{01\bar{1}1\}$ facets. These facets consistently melt nearly 150 K below flat $\{0001\}$ facets, regardless of particle size. We show that both surface and facet melting temperatures scale with nanoparticle size through the Gibbs-Thomson effect, and determine a size-dependent critical liquid layer thickness that triggers complete melting of the nanoparticle, which saturates near three atomic layers. Our results confirm recent experimental observations of surface premelting and extend the framework to anisotropic particles with facet-orientation-dependent behavior.

cond-mat.mtrl-sci↗

arXiv2024

Polyvalent Machine-Learned Potential for Cobalt: from Bulk to Nanoparticles

We present the development and applications of a quadratic Spectral Neighbor Analysis Potential (q-SNAP) for ferromagnetic cobalt. Trained on Density Functional Theory calculations using the Perdew-Burke-Ernzerhof (DFT-PBE) functional, this machine-learned potential enables simulations of large systems over extended time scales across a wide range of temperatures and pressures at near DFT accuracy. It is validated by closely reproducing the phonon dispersions of hexagonal close-packed (hcp) and face-centered cubic (fcc) Co, surface energies, and the relative stability of nanoparticles of various shapes. An important feature of this novel potential is its numerical stability in long molecular dynamics simulations. This robustness is exploited to compute the heat capacity of nanoparticles containing up to 9201 atoms, showing convergence to less than 2 J.K-1.mol-1 after 100 ns. Computations of the melting temperature of nanoparticles as a function of their size revealed a convergence to the bulk limit in excellent agreement with the experimental value. Thus, the new, highly accurate machine-learned potential for Co opens exciting opportunities for further applications such as the dynamics of nanoparticles in catalytic reactions.

cond-mat.mtrl-sci↗