  In this study, we present a novel approach along with the needed
computational strategies for efficient and scalable feature engineering of the
crystal structure in compounds of different chemical compositions. This
approach utilizes a versatile and extensible framework for the quantification
of a three-dimensional (3-D) voxelized crystal structure in the form of 2-point
spatial correlations of multiple atomic attributes and performs principal
component analysis to extract the low-dimensional features that could be used
to build surrogate models for material properties of interest. An application
of the proposed feature engineering framework is demonstrated on a case study
involving the prediction of the formation energies of crystalline compounds
using two vastly different surrogate model building strategies - local Gaussian
process regression and neural networks. Specifically, it is shown that the top
25 features (i.e., principal component scores) identified by the proposed
framework serve as good regressors for the formation energy of the crystalline
substance for both model building strategies.
