The training set employed for development of the machine learning model was previously reported by This set contains 121 pure silica zeolites which are a subset of the Database of Zeolite Structures. The elastic data was obtained from optimised structures using the B3LYP hybrid exchange-correlation functional with empirical correction for dispersive interactions and all electron basis sets for silicon and oxygen. To construct our predictive model, we used a gradient boosting regressor (GBR) which is part of the Python scikit-learn package. This method trains regression trees as an additive model in stepwise approach by optimising arbitrary loss functions. At each stage, a regression tree is fit on the negative gradient of the loss function. GBR is both an accurate and effective method that has been used in many areas including web-search ranking. In particular, this method was chosen over other methods, such as support vector machines, as GBR models are considered robust, interpretable and applicable for the small dataset present in this study. Our implementation of GBR uses well-established selection criteria with 3-fold crossvalidation, which was repeated 100 times to give a representation of the model accuracy. This approach gives the variance in accuracy. Hyperparameters, given in Table , were chosen to provide high prediction accuracy and flexibility to minimise overfitting. Particularly, this was achieved by using a small learning rate and a shallow parameter for maximum depth of trees. Training deviance plots constructed during the learning procedure, as presented in the Supporting Information, demonstrate the accuracy and generality of the model.