With structure-based feature vectors now generated from Robocrystallographer and a modified version of mat2vec, we were then able to test their effectiveness as descriptors in machine learning models. We constructed several machine learning models to perform a regression task of predicting a variety of material properties. For this work, we used algorithms such as gradient-boosted trees (GBT), support vector machine (SVM), linear support vector machine, adaptive boosting, and a random forest regressor (RFR). We showed the results from GBT here. Results from other models are in the supplementary information. GBT is an ensemble of weak learners where the predictions of these weak learners are combined to get the final prediction. Each new learner corrects the error of the existing model. We used Extreme Gradient Boosting (XGBOOST) , an implementation of GBT with advanced regularization for regression. Cross-validation scores were calculated using Scikit-Learn framework. Hyperparameters for our machine learning model were tuned via 500 iterations of random searches. The hyperparameters we chose are listed in table .