Computational studies on Mg ion conductivity in Mg2xHf1-x Nb(PO4)3 using neural network potential

Low Mg diffusivity in solid-state oxides is an obstacle for the development of materials for Mg ion batteries, which are expected to have high capacity. In this study, we focused on NASICON-type and beta-iron sulfate-type Mg2xHf1-xNb(PO4)(3) that exhibit relatively high Mg ionic conductivity and investigated the Hf/Nb configuration and composition dependence of phase stability and ion conductivity by atomistic simulation using neural network potentials. The calculations show that the NASICON-type structure is slightly more stable and has higher Mg ionic conductivity than that of the beta-iron sulfate-type. The effect of the Hf/Nb configuration was investigated and showed that the ordered stable structure had much lower ionic conductivity than the disordered structure. Furthermore, as the Mg ion concentration increased, the ionic conductivity increased monotonically at low concentrations but tended to converge to a constant value above a certain concentration. The saturation of the ionic conductivity despite increasing the Mg concentration may be due to the trapping effect of the Mg ions caused by the Hf vacancies as well as the Hf/Nb arrangement.