期刊
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
卷 118, 期 2, 页码 -出版社
WILEY
DOI: 10.1002/qua.25460
关键词
machine learning; mass spectrometry; quantum chemistry; simulation
类别
资金
- Australian Research Council [DE160101313]
- Danish National Research Foundation (Center for Materials Crystallography) [DNRF-93]
- Australian Research Council [DE160101313] Funding Source: Australian Research Council
We 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 molecules selected to be representative of common fragmentations and reactions in electron impact mass spectra. Comparison with experimental spectra is performed using the standard matching algorithms, and the relative ranking position of the actual molecule matching the spectra within the NIST-11 library is examined. We find that the correct spectrum is ranked in the top two matches from structural isomers in more than 50% of the cases. QCEIMS, thus, reproduces the distribution of peaks sufficiently well to identify the compounds, with the RMSD and mean absolute difference between appropriately normalized predicted and experimental spectra being at most 9% and 3% respectively, even though the most intense peaks are often qualitatively poorly reproduced. We also compare the QCEIMS method to competitive fragmentation modeling for electron ionization, a training-based mass spectrum prediction method, and remarkably we find the QCEIMS performs equivalently or better. We conclude that QCEIMS will be very useful for those who wish to identify new compounds which are not well represented in the mass spectral databases.
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