Theoretical investigation of photochemical processes in molecules is a nontrivial task. Ab initio calculations that completely describe such processes are often intractable to perform given today’s hardware. Thus, to gain insight into common areas of interest, such as in transition metal photochemistry and organic photovoltaics, less accurate but more feasible calculations must be employed. This thesis synthesizes a wide range of techniques and experimental data to glean such insight into the excited state dynamics of transition metal complexes, the photocatalytic mechanisms in nitrogenase mimics, and charge transport in organic photovoltaic devices. Computationally affordable methods such as flavors of density functional theory are used in the treatment of the transition metal complexes mentioned above, while novel machine learning paradigms are explored in the area of organic photovoltaics.

Creator

• Kelley, Matthew

DOI

• https://doi.org/10.21985/n2-kep4-0w28

Subject

• Chemistry

Language

• en

Alternate Identifier

• etdadmin_upload_631424
• http://dissertations.umi.com/northwestern:14486

Date created

• 2018-01-01

Resource type

• Dissertation

Rights statement

• In Copyright