Identifying the optimal reaction conditions to enact a specific transformation is a major challenge for chemists, particularly in the field of small molecule drug synthesis and natural product synthesis. The field of laboratory automation allows for the rapid and systematic generation of high-quality data, which, when used in combination with ML-directed selfoptimization algorithms can become a powerful tool for research. In order to apply such tools, a chemical reaction must be represented in a machine-readable format. This representation must be composed of descriptors that are simple and relevant enough to avoid the introduction of undesired noise, yet complex-enough to account for properties that impact reaction success such as sterics and electronics.