Role of Na⁺ Interstitials and Dopants in Enhancing the Na⁺ Conductivity of the Cubic Na₃PS₄ Superionic Conductor

Abstract

In this work, we performed a first-principles investigation of the phase stability, dopant formation energy and Na+ conductivity of pristine and doped cubic Na3PS4 (c-Na3PS4). We show that pristine c-Na3PS4 is an extremely poor Na ionic conductor, and the introduction of Na+ excess is the key to achieving reasonable Na+ conductivities. We studied the effect of aliovalent doping of M4+ for P5+ in c-Na3PS4, yielding Na3+x M x P1–x S4 (M = Si, Ge, and Sn with x = 0.0625; M = Si with x = 0.125). The formation energies in all the doped structures with dopant concentration of x = 0.0625 are found to be relatively low. Using ab initio molecular dynamics simulations, we predict that 6.25% Si-doped c-Na3PS4 has a Na+ conductivity of 1.66 mS/cm, in excellent agreement with previous experimental results. Remarkably, we find that Sn4+ doping at the same concentration yields a much higher predicted Na+ conductivity of 10.7 mS/cm, though with a higher dopant formation energy. A higher Si4+ doping concentration of x = 0.125 also yields a significant increase in Na+ conductivity with an even higher dopant formation energy. Finally, topological and van Hove correlation function analyses suggest that the channel volume and correlation in Na+ motions may play important roles in enhancing Na+ conductivity in this structure.

Development of Sulfide Solid Electrolytes and Interface Formation Processes for Bulk-Type All-Solid-State Li and Na Batteries

“…However, studies of new sulfide Na + ion conductors are few. A first-principles calculation indicates that Sn-doped cubic Na3PS4 is predicted to have a higher Na + ion conductivity of 10 −2 S cm −1 (Zhu et al, 2015). Synthesis of SEs with a favorable structure for Na + ion conduction based on calculation results is important for finding new Na + ion conducting SEs.…”

Li4SnS4and Li4SnSe4: Simulations of Their Structure and Electrolyte Properties

“…The values of s i for Li 4 It is possible to use molecular dynamics results in a more quantitative analysis of ionic conductivity following the approach implemented by Mo, Ong, and others. [32][33][34][35] For a molecular dynamics simulation at temperature T with resultant ion trajectories {r i (t)} as a function of time t, one can calculate the mean squared displacement and use Einstein's expression to determine the diffusion constant D trace (T ):…”

“…The simulation results reported here should be regarded as preliminary, due to the relatively small number of configurations sampled. Previous work of this sort [32][33][34][35] was based on simulation times 10-100 times as long as our 3-8 ps simulations. For these reasons, the least squares fit lines through the simulated results for log(T σ) versus 1000/T should be considered with large error bars.…”

Computational studies of solid-state alkali conduction in rechargeable alkali-ion batteries

“…Nevertheless, there have been recent successes in computational dopant optimizations for solid electrolyte materials. 113,130 Despite significant progress, existing computational methods for probing alkali diffusion-ab initio and classical MDs, transition state theory and kinetic Monte Carlo (KMC)-all suffer from various limitations. Ab initio methods, such as NEB and AIMD, tend to be computationally expensive, whereas methods that depend on empirical or fitted potentials, such as classical MD and KMC, while much cheaper computationally, are difficult to transfer between chemistries and structures.…”

“…[127][128][129] Using AIMD simulations, Zhu et al 130 showed that the pristine c-Na 3 PS 4 is in fact a poor alkali conductor at room temperature, and it is only with the introduction of Na + interstitials that the Na + conductivity approaches the levels observed experimentally. They further proposed Sn doping as a potential strategy to further enhance the conductivity in this material, but this prediction has yet to be verified experimentally.…”