Molecular dynamics study on magnesium hydride nanoclusters with machine-learning interatomic potential
Ning Wang, Shiping Huang
Abstract
We introduce a machine-learning (ML) interatomic potential for Mg-H system based on Behler-Parrinello approach. In order to fit the complex bonding conditions in the cluster structure, we combine multiple sampling strategies to obtain training samples that contain a variety of local atomic environments. First-principles calculations based on density functional theory (DFT) are employed to get reference energies and forces for training the ML potential. For the calculation of bulk properties, phonon dispersion, gas-phase ${\mathrm{H}}_{2}$ interactions, and the potential energy surface (PES) for ${\mathrm{H}}_{2}$ dissociative adsorption on Mg(0001) surfaces, our ML potential has reached DFT accuracy at the level of GGA-PBE, and can be extended by combining the DFT-D3 method to describe van der Waals interaction. Moreover, through molecular dynamics (MD) simulations based on the ML potential, we find that for ${\mathrm{Mg}}_{n}{\mathrm{H}}_{m}$ clusters, $\mathrm{Mg}/\mathrm{Mg}{\mathrm{H}}_{x}$ phase separation occurs when $m<2n$, and for a cluster with a diameter of about 4 nm, the Mg part of the cluster forms a hexagonal close-packed (hcp) nanocrystalline structure at low temperature. Also, the calculated diffusion coefficients reproduce the experimental values and confirm an Arrhenius type temperature dependence in the range of 400 to 700 K.