The clustering process is performed with the scikit-learn packages . The pipeline was initially developed using the K-means clustering algorithm. After finalizing other parts of the pipeline, a benchmarking test was performed. Several clustering algorithms were tested using the default hyperparameters, including mini batch K-means clustering, agglomerative clustering, BIRCH and Gaussian mixture. Overall, changes in the clustering algorithms do not alter the performance significantly (Figure ). However, the time required is noticeably different between clustering methods. The pipeline using the agglomerative clustering algorithm requires the least computational resource. The average time from extracting descriptors to the end of the clustering process is approximately a second (Figure ). Hyperparameter tuning on the agglomerative clustering process was performed. Combinations of the linkage criterion and affinity metrics were tested in a grid searching style. Altering the hyperparameters does not change the result significantly (Figure ). The agglomerative clustering method with the default hyperparameters (affinity = Euclidean and linkage = Ward) was chosen for the clustering approach pipeline. The inputs required are the list of dihedral angles, and n_clusters, the number of clusters that is required, which we discuss in the next section.