This article focuses on the approach, design, and performance of the binary-encoded SMARTS (BESMARTS) method for clustering via SMARTS which lends to the automation of force field design. The first priority for this approach is that it is able to work in an automatic, data-driven manner that does not require supervision for determining SMARTS patterns. The process must work by clustering based on an objective, and the SMARTS generated works to minimize the objective in the way the data is clustered. The two general classes of objective functions are based on categorical or continuous data. For clustering categorical data, such as labels, we say the resulting SMARTS hierarchy is accurate if it can replicate the same labeling. The accuracy decreases if a SMARTS pattern groups together chemical environments that are associated with different labels. Therefore perfectly accurate SMARTS hierarchy would be indistinguishable from the original method that produced the labels. For clustering continuous data, we say the resulting SMARTS hierarchy is accurate if the criteria or objective function to insert a SMARTS pattern into the hierarchy is minimal. For example, in the context of force field parameterization, we seek to group chemical environments that can use the same parameters, such as bond lengths and force constants, and deviations between the data and the parameter values are small. The example examined in this work is bond lengths generated from molecular geometries refined by DFT, and we seek to find SMARTS patterns which group the chemical environments by the average bond length of the group. We then assess the quality of the hierarchy by computing the RMSD. We show that such a simple objective of examin-ing bond length differences is able to produce a chemical perception model that is competitive with existing models.