Prior approaches to the automated analysis of NMR spectra employ machine learning models to analyze spectra. The majority of current models analyze the spectrum of a pure sample of an unknown compound to determine its identity. These models can be employed only on spectra that contain a single compound, but full workflows capable of analyzing multi-component spectra are becoming more commonplace. Although powerful for their trained tasks, these machine-learning models require a database containing spectra of each potential component, and cannot identify novel compounds.