Spackman, Peter R.Bohman, BjornKarton, AmirJayatilaka, Dylan2022-04-130020-7608http://hdl.handle.net/1885/263022We investigate the success of the quantum chemical electron impact mass spectrum (QCEIMS)method in predicting the electron impact mass spectra of a diverse test set of 61 small moleculesselected to be representative of common fragmentations and reactions in electron impact massspectra. Comparison with experimental spectra is performed using the standard matching algo-rithms, and the relative ranking position of the actual molecule matching the spectra within theNIST-11 library is examined. We find that the correct spectrum is ranked in the top two matchesfrom structural isomers in more than 50% of the cases. QCEIMS, thus, reproduces the distributionof peaks sufficiently well to identify the compounds, with the RMSD and mean absolute differencebetween appropriately normalized predicted and experimental spectra being at most 9% and 3%respectively, even though the most intense peaks are often qualitatively poorly reproduced. Wealso compare the QCEIMS method to competitive fragmentation modeling for electron ionization,a training-based mass spectrum prediction method, and remarkably we find the QCEIMS performsequivalently or better. We conclude that QCEIMS will be very useful for those who wish to iden-tify new compounds which are not well represented in the mass spectral databasesWe gratefully acknowledge financial support from the Australian Research Council (grant DE160101313) to BB, and the financial support of the Danish National Research Foundation (Center for Materials Crystallography, DNRF-93) to PRS.application/pdfen-AU© 2017 Wiley Periodicals, Incmachine learningmass spectrometryquantum chemistrysimulation201710.1002/qua.25460