Abstract
This thesis focuses on the application of Density Functional Theory (DFT) to manganese catalysed hydrogenation reactions (Part I) and organocatalytic reactivity (Part II).Part I: A DFT benchmarking study was performed to reproduce experimentally determined values of hydricity, the heterolytic metal hydride bond strength of 3d transition metal (TM) complexes (Chapter 4) to ensure an accurate methodology for work on these systems. This methodology was employed for the modelling of manganese catalysed enantioselective ketone hydrogenation (Chapter 5). Rational design led to a catalyst with improved stereocontrol by introducing steric bulk in the active site (Chapter 5). Further modification of the ligand backbone led to the identification of routes to improve selectivity and activity by varying sterics and electronics on the ligand (Chapter 5). Using the dataset generated, Machine Learning was performed to predict DFT level barrier heights from GFN2-xTB level descriptors, though the predictive power was limit
Date of Award | 4 Dec 2024 |
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Original language | English |
Awarding Institution |
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Supervisor | Michael Buehl (Supervisor) |
Keywords
- Computational chemistry
- Density Functional Theory
- Homogeneous catalysis
- Organocataysis
- Enantioselectivity
- Conformational sampling
- Benchmarking
- Machine learning
Access Status
- Full text embargoed until
- Restricted until 30 Sep 2026