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Here, we use an actively learned machine learning interatomic potential (MLIP) to bridge this scale\u2010accuracy gap in halide\u2010tunable antiperovskite solid electrolytes. By varying the local Cl\n                    <jats:sup>\u2212<\/jats:sup>\n                    \/Br\n                    <jats:sup>\u2212<\/jats:sup>\n                    ratio and quantifying interfacial amorphicity and ion flux, we identify an MLIP\u2010driven volcano relationship between structural disorder and ionic transport. Cl\u2010enriched grain boundaries remain overly ordered, whereas Br\n                    <jats:sup>\u2212<\/jats:sup>\n                    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\u2010engineered grain boundary through selective halide tuning. These results establish controlled anion segregation as a strategy for reducing interfacial resistance and highlight MLIP\u2010enhanced MD as a predictive framework for transport and disorder engineering in solid\u2010state materials.\n                  <\/jats:p>","DOI":"10.1002\/adma.75492","type":"journal-article","created":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T03:30:13Z","timestamp":1792812613000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine\u2010Learning\u2010Potential\u2010Driven Volcano Relationship in Grain Boundary Amorphicity and Ionic Transport of Antiperovskite Solid Electrolytes"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9209-1581","authenticated-orcid":false,"given":"Abdullah","family":"Bin Faheem","sequence":"first","affiliation":[{"name":"School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University  Singapore 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