Size-Dependent Melting Temperature of Rubidium: Thermodynamic Integration Based on First-principles Calculations

It is well known that a maximum exists in the pressure dependence of melting temperature of alkali metals such as Rubidium (Rb). Thermodynamic integration (TI) based on quantum molecular dynamics (QMD) enables us to estimate the phase-transition temperature accurately, but with a very high computational cost. Therefore, we focused on Artificial Neural Network (ANN). By training QMD data using ANN, it is possible to create high accuracy interatomic potential (ANN potential). The computational cost of TI can be greatly reduced by using ANN potentials, while retaining first-principles accuracy. However, existing ANN potentials were trained with rather small QMD simulations, and it is imperative to systematically study the size dependence of melting temperature.

We performed QMD simulations using Rb systems consisting of 54, 128, 250, and 432 atoms. The radial distribution functions obtained by MD simulations based on ANN potentail (ANN-MD) is in good agreement with that obtained by QMD, while the computational cost is decreased by 3000-fold. The size dependence of melting temperature obtained by ANN-MD-based TI shows that the melting temperature converges to a value close to experimental data.

In the presentation, we will also discuss how to create the ANN potential.