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

Figure 2

(a) Cross-validation results of the models (extra random trees (XT), gradient boosting (GB), extreme gradient boosting (XGB), and decision trees (DT)) built in the present work using 3 metrics including [2], mean absolute error (MAE), and root mean squared error (RMSE). The XT-model was found to perform better than the other models. (b) A representative split (training set and testing set) that was used to visualize the testing results from the 4 models. (c) The testing results of the 4 built models conducted on the representative split, confirming that the XT-model is the most reliable among other models.

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