Ab Initio Computation of Energy Deposition During Electron Ionization of Molecules

A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics

Metabolomics, the comprehensive study of the metabolome, and lipidomics-the large-scale study of pathways and networks of cellular lipids-are major driving forces in enabling personalized medicine. Complicated and error-prone data analysis still remains a bottleneck, however, especially for identifying novel metabolites. Comparing experimental mass spectra to curated databases containing reference spectra has been the gold standard for identification of compounds, but constructing such databases is a costly and time-demanding task. Many software applications try to circumvent this process by utilizing cutting-edge advances in computational methods-including quantum chemistry and machine learning-and simulate mass spectra by performing theoretical, so called in silico fragmentations of compounds. Other solutions concentrate directly on experimental spectra and try to identify structural properties by investigating reoccurring patterns and the relationships between them. The considerable progress made in the field allows recent approaches to provide valuable clues to expedite annotation of experimental mass spectra. This review sheds light on individual strengths and weaknesses of these tools, and attempts to evaluate them-especially in view of lipidomics, when considering complex mixtures found in biological samples as well as mass spectrometer inter-instrument variability.

Rapid Prediction of Electron-Ionization Mass Spectrometry Using Neural Networks

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 from candidate molecules using machine learning. We contribute a lightweight neural network model that quickly predicts mass spectra for small molecules, averaging 5 ms per molecule with a recall-at-10 accuracy of 91.8%. 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.

Total and partial electron impact ionization cross sections of fusion-relevant diatomic molecules

We report calculations of total (and absolute) electron-impact ionization cross sections (EICSs) for the fusion-relevant diatomic molecular species BeH, BeN, BeO, WH, WBe, WN, WO, O₂, and N₂ by means of the Deutsch-Märk and the binary-encounter-Bethe methods in the energy range from threshold to 10 keV. In addition, we discuss an empirical scheme to estimate partial cross sections from the total ones based on reaction energetics and empirical threshold laws and explore its accuracy by assessing available experimental data on total and partial EICSs. Finally, we also report parameters obtained by fitting the calculated cross sections to an expression commonly used in fusion edge plasma modeling.