All-solid-state Na-ion batteries haveemerged as alternatives to all-solid-state Li-ion batteries owing to the global abundance of Na element.However,finding a commercially viable Na-ion solid-state electrolyte(SSE)rem...All-solid-state Na-ion batteries haveemerged as alternatives to all-solid-state Li-ion batteries owing to the global abundance of Na element.However,finding a commercially viable Na-ion solid-state electrolyte(SSE)remains challenging due to the relatively poor understanding of the structures effective for conduction compared to those for Li-ion SSE.In this study,we develop a screening framework based on an unsupervised machine learning technique to characterize Na-ion SSEs according to their lattice structures.展开更多
基金supported by the National Supercomputing Center with supercomputing resources including technical support(KSC-2024-CRE-0013)the DACU Program(RS-2023-00259920)+1 种基金the institutional research program of Korea Institute of Science and Technology(2E32581 and 2E33252)through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(MSIT)supported by Korea Environment Industry&Technology Institute(KEITI)through Center of plasma process for organic material recycling Program,funded by Korea Ministry of Environment(MOE)(RS-2022-KE002490).
文摘All-solid-state Na-ion batteries haveemerged as alternatives to all-solid-state Li-ion batteries owing to the global abundance of Na element.However,finding a commercially viable Na-ion solid-state electrolyte(SSE)remains challenging due to the relatively poor understanding of the structures effective for conduction compared to those for Li-ion SSE.In this study,we develop a screening framework based on an unsupervised machine learning technique to characterize Na-ion SSEs according to their lattice structures.
