The Mean MAPE shows the error on training set whereas MAPEV shows the error on validation set. For all the three dataset the similar trend has observed with respect to train error and validation error. Though it is expected lesser error for Na+K data as it has lowest number of data point however, the less error for Li+Na+K data set compared to other two dataset proves a better sampling of the data. The standard deviation for the dataset having higher number of data is low compared to dataset having lower number of data which indicates that in overall full dataset the main deviation or outlier mainly arises from the Na+K data and not from the Li data. The comparison between DFT calculated capacity and ML Predicted capacity has shown in the Figure . We have plotted the DFT calculated capacity vs ML predicted capacity using the best hyperparameters of SVM for all three different datasets. . Among all these three ML algorithm KRR has fitted the Na+K data well compare to the others having MAPEV 0.153 mAh/g. Gamma and alpha are two important parameters for KRR algorithm. The optimization of these parameters is shown in Figure . The DFT calculated capacity and ML predicted capacity for three different datasets has shown in . Optimized hyperparameters and mean absolute percentage error (MAPE) for decision trees regressor has also given in Table .