Structural features have been used for solid state materials with ML frameworks. Numerical features generated by the DScribe package offer structural representations of molecules and materials. These features are used for transferable ML interatomic potential determining from bond dissociation energy prediction of drug-like molecules, to reactivity of single-atom alloy nanoparticles. However, their vectorized representation and lack of human interpretability do not align the current need for human interpretable approaches. Additionally, Lattice convolutional networks (LCNN) by Jung and Vlachos, which calculate surface graph features in two dimensions with six different permutations have been used for predicting properties including surface composition and surface reaction kinetics, ground states, catalyst properties, and phases. While these features are evidently optimized for deep neural networks, they do not address the requirements for interpretability and explainability in solid state materials studies. High-throughput identification and characterization of 2D materials using DFT 39 *Thermodynamic properties of the Nd-Bi system via EMF measurements, DFT, ML, and CALPHAD modeling Screening Sn2M(III)Ch2X3 chalcohalides for photovoltaic applications 41 Atom2vec 42 N/A Predicting the synthesizability of crystalline inorganic materials ML-based prediction of crystal systems and space groups from inorganic materials compositions Evaluating prediction power of ML algorithms for materials discovery using k-fold crossvalidation Mat2vec 46 200 total