journal article·2020-07-16Open access

An Intuitively Understandable Quality Measure for Theoretical Vibrational Spectra

Henning Henschel, David van der Spoel

Abstract

Computational chemistry has become a central tool in spectroscopic studies in most of chemical science. The quality of a calculated vibrational spectrum is commonly expressed as the deviation of the peak position from the experimental reference. With the increasing application of vibrational spectroscopy to complex (biological) systems, this is likely not sustainable. Here we present a quality measure for theoretical vibrational spectra based on matching the spectra to a reference database with the help of correlation coefficients. This approach can easily be applied to large sets of data and complex spectra without easily identifiable peak positions. We demonstrate this on a database of infrared spectra of 670 compounds using six different theoretical (DFT and force field) methods. Most importantly, it is intuitively understandable by both theoreticians and experimentalists.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Spectroscopy and Chemometric Analyses · Spectroscopy and Quantum Chemical Studies · Molecular spectroscopy and chirality

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Uppsala Center for Computational Chemistry, Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Husargatan 3, Box 596, SE-75124 Uppsala, Sweden

Funding

Vetenskapsr?det · eSSENCE-The e-Science Collaboration (Uppsala-Lund-Ume?, Sweden)

References (21)

Show source-provided references
1. 10.1002/anie.20170116310.1002/anie.201701163 ↗
2. 10.1016/S0003-2670(97)00238-910.1016/S0003-2670(97)00238-9 ↗
3. 10.1038/nature0565310.1038/nature05653 ↗
4. 10.1002/anie.19970885110.1002/anie.199708851 ↗
5. 10.3389/fchem.2019.0004810.3389/fchem.2019.00048 ↗
6. 10.1021/acs.jctc.0c0012610.1021/acs.jctc.0c00126 ↗
7. 10.1021/acs.jctc.0c0012710.1021/acs.jctc.0c00127 ↗
8. 10.1039/C3CP53413H10.1039/C3CP53413H ↗
9. 10.1039/C9SC05103A10.1039/C9SC05103A ↗
10. 10.1007/s00216-016-9375-510.1007/s00216-016-9375-5 ↗
11. 10.1016/j.microc.2019.10429510.1016/j.microc.2019.104295 ↗
12. 10.1155/2019/812964810.1155/2019/8129648 ↗
13. 10.1007/s10812-019-00800-w10.1007/s10812-019-00800-w ↗
14. 10.3390/cancers1201011510.3390/cancers12010115 ↗
15. 10.3389/fchem.2019.0080910.3389/fchem.2019.00809 ↗
16. Wallace W. E. · 2018
17. 10.1038/sdata.2018.6210.1038/sdata.2018.62 ↗
18. 10.1021/ac60351a04110.1021/ac60351a041 ↗
19. 10.1007/s00249-013-0927-810.1007/s00249-013-0927-8 ↗
20. 10.1002/jrs.497810.1002/jrs.4978 ↗
21. 10.1093/bioinformatics/bts02010.1093/bioinformatics/bts020 ↗