As we seek to automate parameterization, it is important to take note of where human expertise is typically employed, and one key place is in determining which chemical environments (usually treated as atom types) will be treated separately by a force field. This process of distinguishing between different chemical environments in order to assign force fields parameters we call the chemical perception. Chemical perception has been a key ingredient in building general purpose small molecule force fields. Chemical perception in current force fields largely consists of assigning atom types or fragment (nonbonded, bond, angle, torsion) types, defined using human intuition. Ideally, force field fitting should involve adjusting not only the numerical parameters of a force field, but also the chemical perception. For example, we could start a force field parameterization process with only a single type of carbon-carbon bond, but the automated process might propose adding a second bond type, thus allowing distinct parameters to be assigned to single versus double bonds. If this proposal led to improved accuracy sufficient to justify the increased number of parameters, it would become part of the force field definition. Thus, we need a way to sample not only over the numerical parameters of a force field, but also over its atom or fragment types. However, we are not aware of any existing algorithm or software tools to carry out this type of automated parameter sampling.