Machine‐Learning‐Potential‐Driven Volcano Relationship in Grain Boundary Amorphicity and Ionic Transport of Antiperovskite Solid Electrolytes

Ionic transport in solid‐state electrolytes is governed by interfacial disorder and local chemical environments, yet their microscopic design principles remain challenging because large‐scale interfacial kinetics are simulated using molecular dynamics (MD), where predictive design requires quantum‐level accuracy. Here, we use an actively learned machine learning interatomic potential (MLIP) to bridge this scale‐accuracy gap in halide‐tunable antiperovskite solid electrolytes. By varying the local Cl − /Br − ratio and quantifying interfacial amorphicity and ion flux, we identify an MLIP‐driven volcano relationship between structural disorder and ionic transport. Cl‐enriched grain boundaries remain overly ordered, whereas Br − enrichment induces excessive disorder that disrupts migration. Intermediate halide compositions maximize ionic transport through moderately amorphous environments. Guided by this relationship, we design an anion‐engineered grain boundary through selective halide tuning. These results establish controlled anion segregation as a strategy for reducing interfacial resistance and highlight MLIP‐enhanced MD as a predictive framework for transport and disorder engineering in solid‐state materials.