We have also confirmed that Eint can be predicted by machine learning methods, although the physics cannot be interpreted as well as the regression model we have developed. The results are shown in Figure in Supporting Information. ) as descriptors by Random Forest, and (b) shows the results of prediction using Crystal Graph Convolutional Neural Networks (CGCNN). It can be seen that both methods are able to predict Eint with good accuracy. However, these models lack physical support and are not validated with respect to extrapolative application to unknown material systems. This stands in contrast to our regression model, which relies on a simple and physically reasonable formulation. Through this study, we succeeded in extracting underlying physics to estimate the intercalation energy only from two intercalant properties and eight host-derived descriptors by random forest and regression equations without the firstprinciples calculations.