arXiv subjects

Jérôme Creuze

Publications and source records attributed to Jérôme Creuze.

6 recordsLinked to original sources

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↗

arXiv2025

Metallurgy at the nanoscale: domain walls in nanoalloys

In binary alloys, domain walls play a central role not only on the phase transitions but also on their physical properties and were at the heart of the 70's metallurgy research. Whereas it can be predicted, with simple physics arguments, that such domain walls cannot exist at the nanometer scale due to the typical lengths of the statistical fluctuations of the order parameter, here we show, with both experimental and numerical approaches how orientational domain walls are formed in CuAu nanoparticles binary model systems. We demonstrate that the formation of domains in larger NPs is driven by elastic strain relaxation which is not needed in smaller NPs where surface effects dominate. Finally, we show how the multivariants NPs tend to form an isotropic material through a continuous model of elasticity.

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↗

arXiv2023

Atomic Scale Surface Segregation in Copper-Gold Nanoparticles

In this work, we combine electron microscopy measurements of the surface compositions in Cu-Au nanoparticles and atomistic simulations to investigate the effect of gold segregation. While this mechanism has been extensively investigated within Cu-Au in the bulk state, it was never studied at the atomic level in nanoparticles. By using energy dispersive X-ray analysis across the (100) and (111) facets of nanoparticles, we provide evidence of gold segregation in Cu$_{3}$Au and CuAu$_{3}$ nanoparticles in the 10 nm size range grown by epitaxy on a salt surface with high control of the nanoparticles morphology. To get atomic-scale insights into the segregation properties in Cu-Au nanoparticles on the whole composition range, we perform Monte Carlo calculations employing $N$-body interatomic potentials. These simulations confirm this effect by showing a complete segregation of Au in the (100) and (111) faces of a truncated octahedron for gold nominal composition of the alloy above 70\% and 60\% respectively. Furthermore, we show that there is no size effect on the segregation behaviour since we evidence the same oscillating concentration profile from surface to the nanoparticles core as in the bulk. These results can shed new lights in the interpretation of the enhanced reactivity, selectivity and stability of Cu-Au nanoparticles in various catalytic reactions.

cond-mat.mtrl-sci↗

arXiv2021

Mass transport computations via correlation splitting and a law of total diffusion

Directly computing mass transport coefficients in stochastic models requires integrating over time the equilibrium correlations between atomic displacements. Here, we show how to accelerate the computations via \green{correlation splitting and conditioning, which statistically amounts to estimating the mass transport coefficients} through a law of total diffusion. We illustrate the approach with kinetic path sampling simulations of atomic diffusion in a \green{random alloy model} in which percolating solute clusters trap the mediating vacancy. There, Green functions serve to generate first-passage paths escaping the traps and to propagate the long-time dynamics. When they also serve to estimate mean-squared displacements via conditioning, colossal reductions of statistical \green{errors} are achieved.

cond-mat.mtrl-sci↗

arXiv2021

Absorption kinetics of vacancies by cavities in Aluminum: numerical characterization of sink strengths and first-passage statistics through Krylov subspace projection and eigenvalue deflation

Modeling the microstructural evolution of metal and alloys, specifically under irradiation, is essential to predict the aging properties of materials. Many models are based on a transition rate matrix describing the jump frequencies of defects and involve a master equation governing the time evolution of a state probability vector. Here, we present non-stochastic numerical techniques to characterize the motion of individual defects migrating over long distances prior to recombining or being absorbed by another defect, resorting to the theory of absorbing Markov chains. These important events are fully determined by their first-passage time distribution to distant locations, no-passage distribution ,and walker fluxes to the sinks. We show that these functions can be efficiently computed using a method combining Krylov subspace projection and eigenvalue deflation. For a model system describing the absorption of a vacancy by a cavity in aluminum, the use of a small Krylov subspace deflated by the unique eigenmode corresponding to the quasi-stationary distribution is sufficient to capture the kinetics of the defect absorption faithfully. This method can be used in kinetic Monte Carlo simulations to perform stochastic non-local moves or in cluster dynamics simulations to compute sink strengths.

cond-mat.mtrl-sci↗