Developing informative, discriminating, and independent features that represent a material is of crucial importance to materials informatics. Feature engineering is often based on leveraging domain knowledge in the creation and selection of useful features. Given sufficiently large data sets, feature engineering can be avoided through deep learning. However, most materials informatics use case scenarios involve sparse data sets where domain knowledgeinspired feature engineering has proven a valuable tool for improving predictive accuracy. The field of material science is founded on the concept of identifying and exploiting structureproperty-processing relationships. However, the material structure is a very broad term that encompasses atomic and electronic structure, crystal structure, amorphous characteristics, microstructure, and mesoscale aspects. To date, a number of different approaches have been developed in an attempt to encode representations with structural or compositional information . However, these approaches still fall short of capturing and featurizing important structural aspects. In fact, most structural descriptions are generated via human analysis by subject matter experts and stored in scientific publications as text entries.