Machine-learnt potential highlights melting and freezing of aluminium nanoparticles

TL;DR

This study uses a machine-learned potential to simulate aluminium nanoparticles, revealing size-dependent structural stability, phase transition behaviors, and surface phenomena across a wide size range.

Contribution

Introduces a Bayesian Force Field for aluminium, enabling comprehensive molecular dynamics simulations of nanoparticles from 200 to 11000 atoms, with insights into their structural stability and phase transitions.

Findings

Icosahedral stability up to 2000 atoms

Decahedral stability up to 25000 atoms

FCC structures favored beyond 25000 atoms

Abstract

We investigated the complete thermodynamic cycle of aluminium nanoparticles through classical molecular dynamics simulations, spanning a wide size range from 200 atoms to 11000 atoms. The aluminium-aluminium interactions are modelled using a newly developed Bayesian Force Field (BFF) from the FLARE suite, a cutting-edge tool in our field. We discuss the database requirements to include melted nanodroplets to avoid unphysical behaviour at the phase transition. Our study provides a comprehensive understanding of structural stability up to sizes as large as $3~ 10^5$ atoms. The developed Al-BFF predicts an icosahedral stability range of up to 2000 atoms, approximately 2 nm, followed by a region of stability for decahedra, up to 25000 atoms. Beyond this size, the expected structure favours face-centred cubic (FCC) shapes. At a fixed heating/cooling rate of 100K/ns, we consistently observe a hysteresis loop, where the melting temperatures are higher than those associated with solidification. The annealing of a liquid droplet further stabilizes icosahedral structures, extending their stability range to 5000 atoms. Using a hierarchical k-means clustering, we find no evidence of surface melting but observe some mild indication of surface freezing. In any event, the liquid droplet's surface shows local structural order at all sizes.