Our second type of model is a Message-Passing Neural Network, which provides an automatic path for learning the similarity between different functional groups and their impact on a material property. A MPNN, as formalized by Gilmer et al. and described in the Methods, learns interactions between atoms/functional groups through "message-passing" steps that update the description of each atom based on the types of neighboring atoms and the types of bonds connecting to the neighboring atoms. In this work, we use an MPNN architecture implemented by St. John et al. Our MPNN describes atoms initially based on their type, degree, number of bonded hydrogen atoms, and whether it is in an aromatic ring; updates the atom description with messages based on sums of those from its nearby atoms; and generates a description of the entire molecule as a sum of all atoms. Full details are available in Methods.