Solid-state sodium batteries, a relatively safe and potentially cost-effective energy-storage technology, have attracted increasing scientific attention recently for application in stationary grid-scale energy storage. Identifying solid electrolytes with high electrochemical stability and high Na-ion conductivity at room temperature is critically important to enable high energy densities with enhanced rate capabilities. We evaluated sodium sulfide-silicon sulfide, xNaS + (1- x)SiS, glasses as potential glassy solid electrolytes (GSEs) using molecular dynamics (MD) simulations. We employed ab initio MD to determine ion conduction mechanisms, to calculate energy barriers for ion hops, and to correlate these to the local short-range structure of 0.50NaS + 0.50SiS glass. To simulate much larger systems for accurately calculating the ionic conductivity, we parameterized empirical Buckingham-type potential and performed classical MD simulations. After validating these calculations by comparing the structure obtained from MD to that from X-ray scattering data, we calculated the ionic conductivity of these glasses for the range of 0.33 ≤ x ≤ 0.67 compositions. The calculated ionic conductivities at room temperature were in the range of ∼10 S/cm for the x = 0.50 composition and increased significantly with sodium sulfide ( x) content. These calculations provide theoretical insights into the role of NaS content on the ionic conductivity of GSEs aiding in the selection of specific compositions to enhance the ionic conductivity.

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Batteries based on solid-state electrolytes, including Li7La3Zr2O12 (LLZO), promise improved safety and increased energy density; however, atomic disorder at grain boundaries and phase boundaries can severely deteriorate their performance. Machine-learning (ML) interatomic potentials offer a uniquely compelling solution for simulating chemical processes, rare events, and phase transitions associated with these complex interfaces by mixing high scalability with quantum-level accuracy, provided they can be trained to properly address atomic disorder. To this end, we report the construction and validation of a ML potential that is specifically designed to simulate crystalline, disordered, and amorphous LLZO systems across a wide range of conditions. The ML model is based on a neural network algorithm and is trained using ab-initio data. Performance tests prove that the developed ML potential can predict accurate structural and vibrational characteristics, elastic properties, and Li diffusivity of LLZO comparable to ab-initio simulations. As demonstration of its applicability to larger systems, we show that the potential can correctly capture grain boundary effects on diffusivity, as well as the thermal transition behavior of LLZO. These examples show that the ML potential enables simulations of transitions between well-defined and disordered structures with quantum-level accuracy at speeds thousands of times faster than ab-initio methods.

Ionic liquids are considered promising electrolytes for developing electric double-layer capacitors (EDLCs) with high energy density. To identify optimal operating conditions, we performed molecular dynamics simulations of N-methyl-N-propyl pyrrolidinium bis(trifluoromethanesulfonyl)imide (mppy+ TFSI−) ionic liquid confined in the interstices of vertically aligned carbon nanostructures mimicking the electrode structure. We modeled various surface charge densities as well as varied the distance between nanotubes in the array. Our results indicate that high-density ion storage occurs within the noninteracting double-layer region formed in the nanoconfined domain between charged nanotubes. We determined the specific arrangement of these ions relative to the nanotube surface and related the layered configuration to the molecular structure of the ions. The pitch distance of the nanotube array that enables optimal mppy+ TFSI− storage and enhanced capacitance is determined to be 16 Å.