Revolutionizing All-Solid-State Sodium Batteries with Advanced Computational Tools and Mixed Glass Former Effects

Amorphous solid electrolytes (aSEs) such as sodium thiophosphates and oxysulfides are promising for All-Solid-State Sodium Batteries (ASSSBs), ideal for large-scale energy storage. Their current development relies on a time-consuming trial-and-error approach due to limited atomic-level understanding of composition-structure-property relationships. This project aims to revolutionize the field by using in-silico modeling and Machine Learning (ML) techniques to accelerate the discovery and optimization of innovative aSEs. We propose developing High-Fidelity Density Functional Theory (DFT)-based ML potentials for Na-phosphorous sulfides and oxysulfides, enabling robust simulations for both academic and industrial applications. Project Objectives: 1. Developing DFT-based ML Potentials: Create & disseminate ML interatomic potentials for Na-thiophosphates and oxysulfide SEs. 2. Accelerated MD Simulations: Use DFT and ML-accelerated Molecular Dynamics (MD) Simulations to predict the structure and properties of sodium-based aSEs. 3. Investigating Mixed Glass Former Effects (MGFE): Explore the effects of mixing anion (O and S) and cation (B, P, Si) former networks on the structure and ionic conductivity of sodium oxysulfides. 4. ML-based QCSPR Models: Build ML-based Quantitative-CSPR models for the computer-aided design of novel aSEs for ASSSBs. Significance of MGFE: High ionic conductivity and improved properties of oxysulfide glasses can be achieved by mixing glass former cations (e.g., B & P) or anions (e.g., O & S) at a constant fraction of the mobile cation, such as Na+. Optimized mixed glass former electrolytes based on Na could lead to cheaper, safer, and more energy-dense grid-scale batteries. Understanding both positive and negative MGFEs is crucial, as they offer insight into ionic conduction mechanisms. This project will investigate these dual behaviors in oxysulfide glasses, paving the way for a deeper understanding and improved design of SEs. Collaborative Expertise: This project brings together two leading research units from the Department of Chemical and Geological Sciences (DSCG) and the Department of Physics, Informatics, and Mathematics (FIM). The DSCG team excels in force-field development, classical MD simulations, and QSPR methods, while the FIM group specializes in ab initio MD simulations of materials and interfaces. Impact: Our project will significantly contribute to both fundamental and applied research in materials science. By developing ML potentials for Na oxysulfide glasses, we will enable the study of long timescale phenomena in aSEs, such as ionic conductivity. Our work will establish efficient computational procedures for material studies. The investigation of MGFEs will elucidate the dual behavior of ionic conduction in mixed systems, offering transformative insights. The knowledge gained on the composition-structure-properties relations will pave the way for the design of Na-based aSEs with customized properties, accelerating the deployment of ASSSBs in grid-scale energy storage solutions.