We examined regression by random forest regression, a machine learning algorithm that predicts an objective variable while checking the importance of descriptors, using scikitlearn library. As a result of examining descriptors for various hosts, we succeeded in predicting them by using four descriptors for each regression (eight descriptors in total). Detailed condition for the random forest regression and the values of the descriptors are described in Supporting Information. In addition, the generalization performance of each model was evaluated by 5-fold cross validation, and the results are shown in Table . While there are some variations in R 2 for 𝛼 M-Host and 𝛾 M-Host , the generalization performance is good for 193 sets of regression coefficients. Looking at the permutation importance of each model, it can be seen that the value of (1) formal valence of the atom coordinating to intercalant is the most important descriptor. This value is considered to contain the basic information about whether the host is acid-like or base-like, and since it directly contacts the intercalant, it is reasonable to say that it greatly affects the compatibility with the intercalant. For 𝛽 M-Host , (5) formal valence of host, is also important, which is consistent to the meanings of the second term of eq. 4, that is the Coulombic interaction. For 𝛼 M-Host and 𝛽 M-Host , the contribution of (2) coordination number to intercalants, tends to be high. This reflects the geometric information around the intercalant. Actually, other important factors for 𝛼 M-Host and 𝛽 M-Host , ( ) and ( ), are commonly related to radius of the coordinating atoms. The last important descriptor for 𝛼 M-Host is (4) weighted average of first ionization energies of the elements constituting the host, which is considered to incorporate the effect of electron transfer as shown in Figure . As for 𝛾 M-Host , (7) binding energy of host, (6) ionic radius of the coordination atom, and (8) area per intercalant are the important descriptors, which also shows that the model successfully incorporates the effects of Ebinding, Host and Estraining, Host as shown in Figure .