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

Figure 1

The characteristics of the data used in the present learning process. (a) The relationship between the total phonon band center and the activation energy related to the diffusion of Li ions in some compounds including Li₃PO₄ (), Li₃PS₄ (), Li₃PO₄ (), Li₃SO₄ (), Li₄GeO₄ (), Li₃VO₄, Li₄GS₄ (), and Li₄SnS₄ [31]. The activation energy is the summation of energy needed to generate vacancies and the migration energy of Li ions. (b) The histogram of the total phonon band centers, which are determined from the PhDOS diagrams (one example is embedded in this figure) of the compounds included in the initial dataset. The highlighted histogram in (b) is related to the Li-containing compounds (114 compounds) in this dataset. (c, d) The distribution of the compounds used in the initial dataset based on their type and crystal system, respectively, where most of the compounds are oxide-based and most materials possess a cubic crystal structure. (e) A heatmap of the periodic table to visualize the contribution of the elements in the compounds used in the initial dataset, showing that among all elements, O had the most contribution in the included compounds.

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