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

Figure 5

(a, b) PhDOS of Li₆PS₅Cl () and Li₂CO₃ (), as calculated in the present work using DFT, and their structures, respectively. (c) A comparison of the total phonon band centers calculated from (a) with those predicted by the XT-model built in the present work for Li₆PS₅Cl and Li₂CO₃. The relative error between the DFT-calculated and ML-predicted values for Li₆PS₅Cl and Li₂CO₃ are presented in (c). (d, e) EIS profiles for Li₆PS₅Cl and Li₂CO₃ recorded at various temperatures. The figure inserted in (d) presents the Arrhenius plot of Li₆PS₅Cl, as constructed from the ionic conductivities determined at the various temperatures in (d), and by this plot, the activation energy was estimated to be 0.195 eV. (f, g) DFT calculations of the migration enthalpy of Li ions in Li₂CO₃ and Li₃PO₄ () [31], respectively.

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