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• W4386935619 abstract "Li-ion solid-state electrolytes (Li-SSEs) hold promise to solve critical issues related to conventional Li-ion batteries (LIBs), such as the flammability of liquid electrolytes and dendrite growth. In this study, we develop a platform involving a high-throughput screening process and machine learning surrogate model for identifying superionic Li-SSEs among 19,480 Li-containing materials. Li-SSE candidates are selected based on the screening criteria, and their ionic conductivities are predicted. For the training database, the ionic conductivities and crystal systems of various inorganic SSEs, such as Na SuperIonic CONductor (NASICON), argyrodite, and halide, are obtained from previous literature. Subsequently, a chemical descriptor (CD), crystal system, and number of atoms are used as machine-readable features. To reduce the uncertainty in the surrogate model, the ensemble method, which considers the two best-performing models, is employed; the mean prediction accuracies are found to be 0.887 and 0.886, respectively. Furthermore, first-principles calculations are conducted to confirm the ionic conductivities of the strong candidates. Finally, three potential superionic Li-SSEs that have not been previously investigated are proposed. We believe that the platform constructed and explored in this work can accelerate the search for Li-SSEs with satisfactory performance at a minimum cost." @default.
• W4386935619 created "2023-09-22" @default.
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• W4386935619 date "2023-09-21" @default.
• W4386935619 modified "2023-10-16" @default.
• W4386935619 title "Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries via Machine Learning" @default.
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• W4386935619 doi "https://doi.org/10.1021/acs.jpcc.3c02908" @default.
• W4386935619 hasPublicationYear "2023" @default.
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