Please use this identifier to cite or link to this item:
https://knowledgecommons.lakeheadu.ca/handle/2453/4876
Full metadata record
DC Field | Value | Language |
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dc.contributor.advisor | Mawhinney, Robert | - |
dc.contributor.author | Lefrancois-Gagnon, Kevin | - |
dc.date.accessioned | 2021-10-12T17:17:50Z | - |
dc.date.available | 2021-10-12T17:17:50Z | - |
dc.date.created | 2021 | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | https://knowledgecommons.lakeheadu.ca/handle/2453/4876 | - |
dc.description.abstract | This work focuses on determining the suitability of functional group properties from the Quantum Theory of Atoms in Molecules (QTAIM) as descriptors for use in machine learning and linear regression analyses. These were studied as an alternative to the conventionally used proxies to substituent properties. QTAIM integrated group properties and local critical point properties were studied for their suitability of use in these applications. The sensitivity of these properties to the choice of Density Functional Theory functional and basis set used for determining the density was examined. The properties at other levels of theory were compared to high-level B2PLYPD3-BJ/aug-cc-pV5Z data. Integrated properties are well conserved between the different model chemistries, both in an absolute sense and in linear relationships to the reference values. Local critical point properties are more sensitive to the level of theory, and hybrid functionals with triple-ζ basis sets are recommended for their evaluation. [...] | en_US |
dc.language.iso | en_US | en_US |
dc.title | Substituent descriptors from the topology of the electron density | en_US |
dc.type | Dissertation | en_US |
etd.degree.name | Doctor of Philosophy | en_US |
etd.degree.level | Doctoral | en_US |
etd.degree.discipline | Chemistry and Materials Science | en_US |
etd.degree.grantor | Lakehead University | en_US |
dc.contributor.committeemember | Floriano, Wely | - |
dc.contributor.committeemember | Gottardo, Christine | - |
Appears in Collections: | Electronic Theses and Dissertations from 2009 |
Files in This Item:
File | Description | Size | Format | |
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LefrancoisGagnonK2021d-1a.pdf | 4.84 MB | Adobe PDF | View/Open |
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