Our primary goal is to derive custom chemical perception models for force field design; however, we first must establish an approach that can efficiently search chemical space and automate the task of chemical perception. Previous work towards automatic chemical perception using SMARTS resulted in the ChemPer program , which mutated SMARTS patterns using Monte Carlo sampling to discover differences in chemical environments, thus providing a path to generate and fit new force field parameters . However, this approach proved to be computationally expensive and was useful only for relatively limited regions of chemical space. Some applications benefit from such an approach, though, for example in the context of fitting custom parameters covering a limited chemical space. This approach has successfully been put into practice in the BespokeFit program to automatically generate and fit custom torsion parameters . Here, we describe a deterministic approach that iterates over SMARTS to find distinguishing features that split a group of chemical environments. Differences in chemical environments are directly searched and computed to find SMARTS patterns for new force field parameters. As far as we know, this is the first work to define an iteration scheme over SMARTS to directly distinguish differences between chemical environments. The work of Schmidt et. al describes a similar method , but the approach here is specifically focused on manipulating SMARTS for force field chemical perception models.