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

Figure 4

(a, b) PhDOS of LiYO₃ (), Li₄CO₄ (), LiNiO₃ (), LiGeO₃ (), and LiSiO₃ (), 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. This comparison was expanded to cover 84 Li-containing compounds that have PhDOS calculations, and those were obtained from Materials Project database but not included in the initial dataset. Good matching between the ML-predicted total phonon band centers (ML-center) and the DFT-calculated ones (DFT-center) with . (d) The total phonon band centers predicted by the XT-model for the LiNiO₃ (), LiGeO₃ (), and LiSiO₃ () as a function of the volume of the unit cell, supporting the hypothesis that the total phonon band center is a suitable proxy to represent ionic conductivity in the learning processes used in this work. (e) The total phonon band centers of ~17 K Li-containing compounds taken from MP, as predicted by the XT-model built in the present work. These compounds had no experimental or computational calculations related to the PhDOS beforehand. (f) The ML-predicted total phonon band centers of the compounds that are thermodynamically stable (<50 meV/atom). The red data points are expected to show high ionic conductivity according to their total phonon band centers, and this was additionally illustrated by the gradient color bar included in the figure.

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