An added benefit of using linear models and our topological characterization of MD simulation data is interpretability, which in the physical sciences is often as important as prediction accuracy. Linear models provide a level of interpretability that is not accessible when using more complex machine learning models, such as the previously-trained CNN. For example, we can train a non-linear SVM using a radial basis function (RBF) kernel on this same dataset. The RBF SVM model performs slightly worse on the testing set than the linear SVM model (RMSE 6.9 k B T -details found in supplementary code).