Research Projects

Sodium-ion batteries (SIBs) have recently appeared as a viable alternative to lithium-ion batteries for large-scale energy storage where cost is of prime importance, particularly for applications. Sodium, unlike lithium, is much more readily available in the Earth's crust and less costly to mine and process. This renders SIB technology very desirable for utility-scale energy storage applications, enabling renewable energy integration, rural electrification, and other giant-format stationary energy storage applications. One of the key challenges in moving SIB technology forward is the generation of high-performance solid electrolytes. Solid electrolytes have great advantages over traditional liquid electrolytes in safety, thermal stability, and the capability to suppress dendrite growth. Among all solid-state electrolyte classes, disordered materials - amorphous or glassy phases - have become more and more interesting. In contrast to crystalline electrolytes, glassy or glass-ceramic electrolytes generally show isotropic ion conduction and are devoid of grain boundaries. This makes them well-suited for their integration into solid-state sodium-ion batteries. It has been demonstrated through recent experimental and theoretical work that structural disorder makes a positive contribution to ion transport. For example, glassy phases such as Na₃PS₄ and Na₃SbS₄ have displayed unexpectedly high ionic conductivities over their completely crystalline analogues. This enhancement is usually traced back to increased free volume and flexible local bonding environments, due to the absence of long-range order. However, ion transport modeling in such disordered materials is a significant computational task. Although ab-initio molecular dynamics (AIMD) is the most accurate method to simulate atomic-scale ion motion, its excessive computational cost restricts timescales and system sizes of simulations, particularly for non-crystalline phases involving large supercells. Recent developments in machine learning (ML) trained interatomic potentials offer an efficient solution to this limitation. By training ML potentials against high-fidelity AIMD databases, researchers are able to conduct large-scale molecular dynamics simulations with higher accuracy at a significantly lower computational cost. Coupled with open-source automation toolkits such as AiiDA and strong structure-generation tools such as PYMATGEN, this method allows systematic exploration and discovery of new glassy solid electrolytes. This proposal will use these cutting-edge computational and ML methods to gain insight into how structural disorder influences Na-ion transport in model glassy and glass–ceramic solid electrolytes. In addition, it aims to expedite the development of new candidate materials for safe, low-cost, high-performance sodium-ion batteries.