We use 120 of the structures to train machine learning models to predict the heat capacity, and keep the remaining 110 structures for evaluating the models. The performance of machine learning models on the test set is shown in Figure . The model reaches an excellent accuracy (∼ 3% relative error) in predicting the heat capacity of the materials.