Browsing by Subject "Electronic Structure"

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    Development of Multistate Density Functional Theory for Photochemistry and Vibrational Dynamics using Quantum Vibration Perturbation Theory
    (2018-01) Grofe, Adam
    This dissertation is contains two separates areas of research. The first three Chapters focus on the development and several proof-of-concepts for multistate density functional theory (MSDFT) regarding excited state chemistry. When performing configuration interaction (CI) on density functional theory (DFT), there is a danger of double-counting with regard to correlation. It is shown that it is possible to remove double counting in a systematic way by combining wave function theory with DFT, and that MSDFT has the correct topology with regard to conical intersection because it fundamentally includes coupling between the excited state densities and the ground state density, which is not true for time-dependent DFT. This was revealed for the dissociation of ammonia, and Jahn-Teller conical intersections. Block localized DFT was used to assemble diabatic states that can be optimized separately and permits the use of chemical intuition by defining the states in a relatively straightforward manner. Finally, the MSDFT formalism was used to optimize spin-multiplet states with the correct degeneracy, which can be difficult in DFT due to the nonlinearity of the current exchange-correlation functionals. The second section of this dissertation regard vibrational dynamics. There is a multitude of methods for computing the vibrational frequency, but very few that simultaneously model anharmonicity and nuclear quantum effects in a manner that is efficient enough for computing frequency trajectories. Quantum vibration perturbation (QVP) theory satisfies all three of these criteria. This is accomplished by utilizing two approximations: (1) A discrete variable representation of the nuclear wave function is used that only requires single point energy calculations to optimize the wave function, (2) Perturbation theory is used to update the wave function across a set of configurations, which circumvents the need to solve the Schrödinger equation. The first application of this model is on hydrochloric acid in minimal solvation shells (water), and acetone bulk solvation dynamics. An implementation strategy is presented that allows for a reference normal mode to be applied across a trajectory. Then QVP is used to probe the condensed phase solvation dynamics of a carbonyl stretch and two silane stretches.
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    Machine Learning Description of Excited State Dynamics in Small Organic Molecules
    (2024-06) Johannesen, Andrew
    Machine learning offers a method to assess systems at a highly accurate level comparable to electronic structure methods for a fraction of the computational cost. This work focuses on the sampling of molecular potential energy surfaces for the creation of data sets to train machine learning models. Chapter 2 seeks to model equilibrium between species in the nitric oxide formation reaction and use grand canonical Monte Carlo to model this reaction. While nitrogen and oxygen molecules were successfully sampled, discontinuities in the density functional theory and complete active space self-consistent field potential energy surfaces prohibited successful modeling of nitric oxide. Chapter 3 seeks to model pathway-based intramolecular reactivity between ethylene and ethylidene in their first excited state. This was approached by using normal mode sampling along nudged elastic band paths, along with configurations from network-driven molecular dynamics simulations selected via query-by-committee combined with a relative energy cutoff. It was found that these techniques were a useful supplementary data-gathering technique that successfully described reaction barrier energies to within 1.5 kcal/mol, but were unable to sample relevant regions of phase space required to reproduce correct molecular motion. Chapter 4 uses a classical force field in molecular dynamics simulations to provide theoretical insight into thermodynamic drives of a modified histidine substrate for Histidine Kinase that would be able to probe enzyme activity directly. Findings supported proteomics surveys indicating glutamate residue 253 provides the most thermodynamically accessible target for the modified histidine in diazirine form.