There has been considerable research on feature engineering for materials science to predict NMR parameters such as the use of smooth overlap of atomic positions (SOAP) descriptors , Coulomb matrix and Behler-Parrinello symmetrical functions (BFPS) . While these features are capable of describing the variance of geometries for structures with different NMR parameters such as isotropic chemical shifts/shieldings, they were designed to be very general and therefore useful in many different types of applications (beyond NMR) . For specific targets such as NMR spectroscopy, which aims to extract highly localized perturbations, these features may yield suboptimal results. For example, the size of a SOAP kernel scales quadratically with the number of elemental species considered, which makes it slow to process when applied to datasets with a variety of elements. Considering that NMR parameters are strongly correlated to the local environment of the target atom, the above features can be greatly simplified or replaced by customized features with better performance and efficiency.