Park, Damdae Chung, Wonsuk Min, Byoung Koun Lee, Ung Yu, Seungho Kim, Kyeongsu 2024-10-04T02:00:17Z 2024-10-04T02:00:17Z 2024-10-02 2024-09 2057-3960 https://pubs.kist.re.kr/handle/201004/150717 All-solid-state Na-ion batteries have emerged 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. Specifically, we evaluate feature vectors encoding 180 structural properties for 12,670 materials containing Na ions. Subsequently, the resulting feature vectors are clustered using hierarchical density-based spatial clustering of applications with noise (HDBSCAN), leading to the discovery of 12 groups including those with experimentally proven Na-ion superionic conductors such as NASICONs and sodium chalcogenides. Post hoc analysis of these clusters reveals that the groups with high conductivity share similar characteristics, including the existence of ion channels for Na ions and the weak interactions between Na ions and the proximate atoms. Ab initio molecular dynamics simulations confirm that the promising groups exhibit exceptional ion diffusivity compared to other groups. By employing decision tree classifiers trained to screen promising groups, we demonstrate the rapid assessment of the potential of a given material. Finally, we offer perspectives and insights for the development of novel Na-ion SSEs for all-solid-state Na-ion batteries. English Nature Publishing Group | Shanghai Institute of Ceramics of the Chinese Academy of Sciences (SICCAS) Computational screening of sodium solid electrolytes through unsupervised learning Article 10.1038/s41524-024-01392-6 1 npj Computational Materials, v.10, no.1 npj Computational Materials 10 1 Y scie scopus 001317443800001 2-s2.0-85204496900 Chemistry, Physical Materials Science, Multidisciplinary Chemistry Materials Science Article ION CONDUCTIVITY SUPERIONIC CONDUCTION CRYSTAL-STRUCTURE LITHIUM POTENTIALS PREDICTION ROBUST GLASS ANION SB