摘要
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.
基金
supported by the National Supercomputing Center with supercomputing resources including technical support(KSC-2024-CRE-0013)
the DACU Program(RS-2023-00259920)
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).
