Data-driven simulation and characterisation of gold nanoparticle melting

Efficient theoretical methods for the structural analysis of nanoparticles are very much needed. Here the authors demonstrate the use of machine-learning force fields and of a data-driven approach to study the thermodynamical stability and elucidate the melting process of gold nanoparticles. The simulation and analysis of the thermal stability of nanoparticles, a stepping stone towards their application in technological devices, require fast and accurate force fields, in conjunction with effective characterisation methods. In this work, we develop efficient, transferable, and interpretable machine learning force fields for gold nanoparticles based on data gathered from Density Functional Theory calculations. We use them to investigate the thermodynamic stability of gold nanoparticles of different sizes (1 to 6 nm), containing up to 6266 atoms, concerning a solid-liquid phase change through molecular dynamics simulations. We predict nanoparticle melting temperatures in good agreement with available experimental data. Furthermore, we characterize the solid-liquid phase change mechanism employing an unsupervised learning scheme to categorize local atomic environments. We thus provide a data-driven definition of liquid atomic arrangements in the inner and surface regions of a nanoparticle and employ it to show that melting initiates at the outer layers.

Automated summary

Efficient theoretical methods for the structural analysis of nanoparticles are crucial, and this study demonstrates the use of machine-learning force fields and a data-driven approach to investigate the thermodynamic stability and melting process of gold nanoparticles. By developing machine learning force fields based on Density Functional Theory calculations, the study accurately predicts nanoparticle melting temperatures and characterizes the solid-liquid phase change mechanism.