Machine learning pipelines for the design of solid-state electrolytes
Vinamr Jain, Zhilong Wang, Fengqi You
Abstract
) 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.
Topics & Concepts
Computer scienceArtificial intelligenceDeep learningWorkflowMachine learningKey (lock)Generative grammarArtificial neural networkBridge (graph theory)Feature (linguistics)Systems engineeringFeature engineeringGraphPipeline transportData scienceComputational modelBenchmark (surveying)Transfer of learningDeep neural networksNetwork scienceMachine Learning in Materials ScienceInorganic Chemistry and MaterialsAdvanced Battery Materials and Technologies