  When confronted with a substance of unknown identity, researchers often
perform mass spectrometry on the sample and compare the observed spectrum to a
library of previously-collected spectra to identify the molecule. While
popular, this approach will fail to identify molecules that are not in the
existing library. In response, we propose to improve the library's coverage by
augmenting it with synthetic spectra that are predicted using machine learning.
We contribute a lightweight neural network model that quickly predicts mass
spectra for small molecules. Achieving high accuracy predictions requires a
novel neural network architecture that is designed to capture typical
fragmentation patterns from electron ionization. We analyze the effects of our
modeling innovations on library matching performance and compare our models to
prior machine learning-based work on spectrum prediction.
