Phonon DOS-Based Machine Learning Model for Designing High-Performance Solid Electrolytes in Li-Ion Batteries

Figure 6

(a) The ML-predicted total phonon band centers of stable Li compounds ( eV/atom), which are expected to show high ionic conductivity. Those compounds were classified based on the chalcogen into O-based, S-based, and Se-based compounds. (b) PhDOS of LiBiO₂ (), as calculated in the present work using DFT. (c) The structure of LiBiO₂. (d) The total phonon band centers of LiBiO₂, as predicted by the XT-model, and the corresponding values determined by DFT calculations in the present work. A relative error of 3.46% was estimated between the ML-predicted and DFT-calculated values. (e) DFT calculations of the migration enthalpy of Li ions in the promising electrolyte (LiBiO₂), which was discovered by the XT-model. (f) DFT calculations of the migration enthalpy of Li ions in the traditional electrolytes, Li₃PS₄ () [31]. LiBiO₂ demonstrates a lower migration enthalpy for Li ions compared to the Li₃PS₄ electrolyte (0.271 vs. 0.296 eV), which equates to a high ionic conductivity. This suggests that LiBiO₂ holds great potential as an electrolyte material for solid-state LIBs. (g) Migration enthalpy of Li ions as function to the total phonon band center of LiBiO₂, Li₃PS₄ [31], Li₃PO₄ [31], and Li₂CO₃.