The final test that we performed examined the viability of clustering continuous data with SMARTS. Here we describe the performance of SMARTS clustering on QM bond lengths. This type of hierarchy could be used, for example, to create a custom force fields from scratch. The SMARTS patterns in the hierarchy represent chemical environments where the bond lengths should be similar. In the force field context, chemical environments that match a given SMARTS in the hierarchy are assigned the same parameter. Success in this case would mean that a SMARTS hierarchy can be automatically generated to cluster physical data in an unsupervised, data-driven manner. We then assess and validate the hierarchy by examining the bond length RMSD over all clusters. Because the RMSD is not sensitive to the num-TABLE III. BESMARTS accuracy (%) against GAFF atom types. Accuracy was measured as the percentage of SMARTS patterns that mapped to exactly one GAFF atom type. The SMARTS hierachies were built using a limit on both b , the number of bits search, and the depth graph which determines how far the chemical environment can extend. Accuracies are reported per binary reduction level, where the number of molecules corresponds to O(2 k ) for k = log 2 N where the number of molecules N = 70. Search parameters that were too computationally expensive are listed as not detected (ND). ber of clusters, we additionally calculated a reduced χ 2 score (Eqn. 23). We note that it is possible to improve the score by including more SMARTS patterns in the hierarchy, with the limit being a unique SMARTS pattern for each chemical environment, i.e. each chemical environment gets a unique force field parameter. Such a SMARTS hierarchy would not generalize well as the SMARTS patterns would be quite complex and specialized. On the other hand, human experts could possibly derive specialized SMARTS which result in a lower bond RMSD; however, this would require a large investment of human effort. The goal of our approach is to find SMARTS hierarchies that are competitive with expertdesigned chemical perception models. A significant advantage to an automated, data-driven approach is that it can be used to find further improvements of preexisting SMARTS-based models and therefore opens path to continuous development and improvement of force fields.