Machine Learning Guided Cobalt‐doping Strategy for Solid‐state NASICON Electrolytes

Abstract

The solid‐state battery (SSB) is a promising direction to address the inherent safety problems in commercial batteries and energy storage systems. However, the development of SSBs is still hider of the low ionic conductivity of solid‐state electrolytes. Based on a machine learning (ML) method, a cobalt‐doping strategy was developed for the Na3.2Zr2Si2.2P0.8O12 (NASICON) compound by training on NASICON‐type solid electrolyte data. The cobalt‐doping strategy efficiently improves the NASICONs’ ionic conductivity to ∼2.63 mS/cm with low activation energy at ∼0.245 eV. The grain‐boundary ionic conductivity reaches ∼11.00 mS/cm without extra densification of the pellet. The NASICON's structures were studied by the Rietveld and the bond‐valence methods. The calculations and observed structural transitions confirm that the cobalt‐doping strategy promotes the structural transition and adjusts the structure to a better performance state. The doping strategy predicted by the ML model is consistent with our experimental results, providing very useful guidance for improving ionic conductivity of NASICON electrolytes.