A dual-stage ML workflow combining fine-tuned CHGNet and an interpretable structure-to-diffusion model screens 4575 high-entropy LZSP compositions and identifies Li2.625Zr0.25Hf0.1875Sn0.1875Ti0.1875Nb0.1875Si2PO12 with predicted high ionic conductivity.
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High-Entropy Solid Electrolytes Discovery: A Dual-Stage Machine Learning Framework Bridging Atomic Configurations and Ionic Transport Properties
A dual-stage ML workflow combining fine-tuned CHGNet and an interpretable structure-to-diffusion model screens 4575 high-entropy LZSP compositions and identifies Li2.625Zr0.25Hf0.1875Sn0.1875Ti0.1875Nb0.1875Si2PO12 with predicted high ionic conductivity.