Spectral Prediction Accuracy Measures. We used three separate measures of spectral similarity to evaluate the accuracy of NEIMS (i) Root Squared Mean Error (ii) Spectral Contrast Angle and (iii) Weighted Cosine Similarity. All accuracy metrics below were calculated in Python 33 (version 3.12.2) along with libraries rdkit-pypi 34 (version 2022.9.5), pandas RMSE is a common metric especially when determining the accuracy of machine learning models. Here, we used a RMSE calculation as one metric of aligned, or 'zero-padded', spectral similarity (Eq. Spectral contrast angle (Eq. 2) determines spectral similarity by representing two spectra in an N-dimensional space by two vectors. Where "ai and bi are the relative intensities of product-ion peaks at m/z value i for isomers A and B." Thus, the measured angle between these two vectors quantifies spectral similarity from 0 to 90 degrees (where 0°indicates indistinguishable spectra). SCA, given the focus on the angle between, rather than the magnitude of vectors, is less sensitive to differences in peak intensity. In other words, SCA weighs each peak equally when calculating the similarity between two spectra. When using SCA to measure accuracy, angles above ~26 degrees often are not adequate for library search algorithms .