Researchers developed a computational method to predict the ionic conductivity of sulfide glass solid-state electrolytes by applying a max-flow algorithm to static atomic structures, according to a study published in Nature Communications.
Disordered glassy materials offer potential as electrolytes for solid-state batteries, though existing methods to evaluate their ionic conductivity remain inefficient. The team built a framework that summarizes the topological properties of an electrolyte's atomic structure into a weighted graph using periodic boundary conditions.
The researchers adapted a variation of the max-flow algorithm, commonly used in continuum models for transportation and fluid flow, to run on the graph representations. They validated the method through molecular dynamics simulations across multiple glassy solid-state electrolyte families containing mobile lithium and sodium ions.
Simulations showed a positive correlation between ionic conductivity and the area-normalized maximum flow calculated directly from static glass structures, particularly in high-conductivity sulfide glass electrolytes. Because the approach relies entirely on static atomic data, the authors noted it can accelerate the discovery of conductive glasses for all-solid-state batteries.
Matteo Pegoraro, Rasmus Christensen, and Søren S. Sørensen contributed equally to the study alongside co-authors at Aalborg University, the Institute of Science and Technology Austria, and the University of Sydney. The project used computational resources at Sophia and LUMI-C provided by the Danish e-Infrastructure Cooperation, with funding from the Independent Research Fund Denmark, the European Research Council, the Austrian Science Fund, and the Danish Data Science Academy.
