One of the most critical aspects of a successful machine learning model lies in "feature engineering." In terms of materials science, features are usually properties related to the materials or values that can be derived or calculated based on materials' structural or chemical information . In terms of these chemical entities, our effort is to select features that provide a means for recognizing patterns in the data, and to correlate an NMR measurement with one or more specific chemical (or structural) properties. When successfully identified, features, either singly or combined, can form a numerical representation of the material, usually expressed in form of a 1D vector. For machine learning prediction, these numerical representations need to capture the variance of the target parameter across different materials to be successful. The process of feature engineering can be as simple as collecting the atomic numbers (i.e., the chemical identity of an atom participating in a bond), while for many data sets, more complex constructed features are needed .