Machine learning pipelines for the design of solid-state electrolytes

) and provide concrete strategies through transfer learning and active learning frameworks. We bridge conventional computational methods (DFT, molecular dynamics) with modern ML techniques, demonstrating hybrid workflows that overcome individual limitations. The review concludes with actionable recommendations for multi-objective optimization, explainable AI implementation, and physics-informed model development, establishing a comprehensive roadmap for the next generation of AI-accelerated solid-state battery materials discovery.