Solid-state sodium batteries: Screening for electrolyte materials

Solid-state sodium-ion batteries allow for sustainable and cost-efficient energy storage solutions for stationary applications and electric vehicles. The growing demand for sodium-ion batteries demonstrates the importance of sodium solid electrolytes. The Sosoba project implements theoretical screening of Na-based compounds to accelerate the discovery of novel Na solid-state electrolytes (NSEs), with an unprecedented combination of beneficial properties: high ionic conductivity, high thermodynamic stability, adequate stability for different voltages, improved tolerance for air, co-existence with a Na-rich anode, low electrical conductivity, and adequate mechanical properties. The materials should be safe, sustainable and affordable. Available material databases such as Materials Project and the GNoME dataset, with more than 380,000 new stable materials, will be used in the theoretical screening. The screening of NSEs is supported by generative artificial intelligence, from which new structures with expected beneficial properties are suggested. High-throughput DFT calculations associated with machine learning (ML) are used to provide accurate and efficient down-screening of the candidate materials. This is validated by synthesis, characterization and testing of novel electrolyte materials. Synthesis is based on the latest development within precursor selection and intelligent design of experiments. Advanced materials characterization gives detailed insight into phase purity, homogeneity, decomposition products, chemical states, ionic diffusivity, and other properties of the materials. Half-cell testing is finally used to predict the optimal performance of batteries based on the selected electrolyte. In all steps of the screening, feedback mechanisms improve the efficiency and accuracy of the procedure.

The scope of this project is to develop new electrolyte materials for solid-state sodium-ion batteries. The goal is to discover materials with an unprecedented combination of beneficial properties: high ionic conductivity, high thermodynamic stability, adequate stability for different voltages, tolerance for air, coexistence with a Na-rich anode, low electrical conductivity, and adequate mechanical properties. The materials should at the same time be safe, sustainable and affordable. This is achieved with a unique screening procedure consisting of several theoretical and experimental steps. The study starts with Na-containing compounds in online databases of theoretically stable compounds. Generative artificial intelligence suggests new structures with tailored properties. Contemporary methods from atomistic modelling (density functional theory and accelerated techniques like trained potentials) are combined with machine-learning algorithms to provide accurate and efficient down-screening of candidate materials. Combinatorial expansion defines new alloys with improved properties. Theoretical predictions are validated with materials synthesis techniques based on the latest development within precursor selection and intelligent design of experiments. Advanced materials characterization gives detailed insight into phase purity, homogeneity, decomposition products, chemical states, ionic diffusivity, and other properties of the materials. Half-cell testing is coupled with comprehensive battery testing to predict optimal performance of batteries based on the selected electrolyte. In all steps of the screening, feedback mechanisms improve efficiency and accuracy of the procedure. All aspects of the activities pursue the principles of Responsible Research and Innovation through their integration into all work packages.