Controlling factor for fracture resistance and ionic conduction in glassy lithium borophosphate electrolytes

Glasses are promising candidates as solid electrolytes for all-solid-state batteries due to their isotropic ionic conduction, formability, as well as high chemical, thermal and electrochemical stability. However, their mechanical properties and ionic conductivity need to be improved. Here, based on molecular dynamics simulations and classification-based machine learning, we reveal that both fracture behavior and ionic conduction in glassy lithium borophosphate electrolytes are encoded in their static structures. By systematically varying the Li and B content, we demonstrate that the machine learning-based structural descriptor termed “softness” can be used as an indicator for both fracture resistance and ionic conductivity. The “softness” metric is calculated from the static local atomic environment, but well captures the long-term dynamics of individual atoms. Notably, the propensities for B atoms to undergo bond-switching (correlated with fracture) and for Li ions to migrate (correlated with ionic conductivity) increase with an increase in atomic softness. Specifically, the out-of-equilibrium interaction of B and Li with oxygen neighbors enhances the propensity for B and Li to undergo bond-switching or rearrangement when experiencing stimuli. These results enable finding the optimum chemical compositions for glassy solid electrolytes with high mechanical stability and high ionic conductivity.