Each linear regression model is solved by ordinary least squares method of sklearn with the default convergence criteria. Each random forest model is consisted of 100 decision tress where each tree could grow up to 15 layers. The minimum number of samples in a leaf is set to 1, and the minimum number of samples in a branch is set to 2. The minimum of impurity is set to 0 for the stop of the branch growth. For the SchNET model, exactly same parameter setup was used as the original report, except the interatomic distance cutoff is set to 10Å .