Recent progress in applying machine learning to this problem has enabled models of amorphous single element structures such as silicon, carbon, and phosphorus , as well as progress in some two-element systems including HfO2 and LixSi . In these methods, machine learning is applied to describe the atomic level interactions using training sets from first principles energy and force calculations; machine learned models therefore have comparable radial distribution factors, bond-lengths, and structure factors to first principles models with improved simulation time and length scales . However, their ability to predict spectral properties such as NMR and XAS with first principles accuracy is lacking; although ShiftML predicts NMR shifts for molecular solids, its nuclei are limited to C, H, N, O, and S . Similarly, XAS spectral lines for transition metal oxides are predicted using a random forest method but applied only to a set of known, crystalline, transition metal oxides . Clearly, there is a need for spectral predictions with first principles accuracy, which can be applied to amorphous materials.