Optics are a powerful probe of chemical structure that can often be linked to theoretical predictions, providing robustness as a measurement tool. Not only do optical interactions like second harmonic generation (SHG), single and two-photon excited fluorescence (TPEF), and infrared absorption provide chemical specificity at the molecular and macromolecular scale, but the ability to image enables mapping heterogeneous behavior across complex systems such as biological tissue. This thesis will discuss nonlinear and linear optics, leveraging theoretical predictions to provide frameworks for interpreting analytical measurement. In turn, the causal mechanistic understanding provided by these frameworks will enable structurally specific quantitative tools with a special emphasis on application in biological imaging. The thesis will begin with an introduction to 2nd order nonlinear optics and the polarization analysis thereof, covering both the Jones framework for polarization analysis and the design of experiment. Novel experimental architectures aimed at reducing 1/f noise in polarization analysis will be discussed, leveraging both rapid modulation in time through electro-optic modulators (Chapter 2), as well as fixed-optic spatial modulation approaches (Chapter 3). In addition, challenges in polarization-dependent imaging within turbid systems will be addressed with the discussion of a theoretical framework to model SHG occurring from unpolarized light (Chapter 4). The application of this framework to thick tissue imaging for analysis of collagen local structure can provide a method for characterizing changes in tissue morphology associated with some common cancers (Chapter 5). In addition to discussion of nonlinear optical phenomena, a novel mechanism for electric dipole allowed fluorescence-detected circular dichroism will be introduced (Chapter 6). Tackling challenges associated with label-free chemically specific imaging, the construction of a novel infrared hyperspectral microscope for chemical classification in complex mixtures will be presented (Chapter 7). The thesis will conclude with a discussion of the inherent disadvantages in taking the traditional paradigm of modeling and measuring chemistry separately and provide the multi-agent consensus equilibrium (MACE) framework as an alternative to the classic meet-in-the-middle approach (Chapter 8). Spanning topics from pure theoretical descriptions of light-matter interaction to full experimental work, this thesis aims to unify these two fronts. <br>

The question posed in this work is how one would model and predict the rotational spectrum of open-shell open-shell van der Waals complexes. There are two secondary questions that arise: the nature of radical-radical interactions in such systems and the modelling of the large amplitude motion of the constituent molecules. Four different systems were studied in this work, each providing part of the answer to the main question. Starting with the large amplitude motion, there are two theoretical approaches that may be adopted: to either model the whole complex as a semi-rigid molecule, or to perform quantum dynamical calculations. We recorded and analysed the rotational spectrum (using Fourier transform microwave spectroscopy) of the molecule of tertiary butyl acetate (TBAc) which exhibits a high degree of internal rotation; and of the weakly-bound complex between a neon atom and a nitrogen dioxide molecule (Ne-NO2). We used the semi-rigid approach for TBAc and the quantum dynamical approach for Ne-NO2. We also explored the compatibility of these two approaches. Moreover, we were able to predict and analyse the fine and hyperfine structure of the Ne-NO2 spectrum using spherical tensor operator algebra and the results of our dynamics calculations. To explore the nature of the interactions in an radical-radical van der Waals complex we calculated the PESs of the possible states that the complex may be formed in, when an oxygen and a nitrogen monoxide molecule meet on a plane using a number of high level ab initio methods. Finally, our conclusions were tested and applied when we performed the angular quantum dynamics to predict the rotational spectrum of the complex between an oxygen and a nitrogen dioxide molecule, and account for the effect of nuclear spin statistics in that system.

A major problem in the quantitative analysis of AC voltammetric data has been the variance in results between laboratories, often resulting from a reliance on "heuristic" methods of parameter estimation that are strongly dependent on the choices of the operator. In this thesis, an automatic method for parameter estimation will be tested in the context of experiments involving electron-transfer processes in solution-phase. It will be shown that this automatic method produces parameter estimates consistent with those from other methods and the literature in the case of the ferri-/ferrocyanide couple, and is able to explain inconsistency in published values of the rate parameter for the ferrocene/ferrocenium couple. When a coupled homogeneous reaction is considered in a theoretical study, parameter recovery is achieved with a higher degree of accuracy when simulated data resulting from a high frequency AC voltammetry waveform are used. When surface-confined reactions are considered, heterogeneity in the rate constant and formal potential make parameter estimation more challenging. In the final study, a method for incorporating these "dispersion" effects into voltammetric simulations is presented, and for the first time, a quantitive theoretical study of the impact of dispersion on measured current is undertaken.

Spin dynamics simulations are used to gain insight into important magnetic resonance experiments in the fields of chemistry, biochemistry, and physics. Presented in this thesis are investigations into how to accelerate these simulations by making them more efficient. Chapter 1 gives a brief introduction to the methods of spin dynamics simulation used in the rest of the thesis. The `exponential scaling problem' that formally limits the size of spin system that can be simulated is described. Chapter 2 provides a summary of methods that have been developed to overcome the exponential scaling problem in liquid state magnetic resonance. The possibility of utilizing the multiple processors prevalent in modern computers to accelerate spin dynamics simulations provides the impetus for the investigation found in Chapter 3. A number of different methods of parallelization leading to acceleration of spin dynamics simulations are derived and discussed. It is often the case that the parameters defining a spin system are time-dependent. This complicates the simulation of the spin dynamics of the system. Chapter 4 presents a method of simplifying such simulations by mapping the spin dynamics into a larger state space. This method is applied to simulations incorporating mechanical spinning of the sample with powder averaging. In Chapter 5, implementations of several magnetic resonance experiments are detailed. In so doing, use of techniques developed in Chapters 2 and 3 are exemplified. Further, specific details of these experiments are utilized to increase the efficiency of their simulation.

The past two decades have witnessed tremendous advances in the field of high accuracy, high mass resolution data acquisition of complex samples such as crude oils and the human proteome. With the development of Fourier transform ion cyclotron resonance mass spectrometry, the rapidly growing field of petroleomics has emerged, whose goal is to process and analyse the large volumes of complex and often poorly understood data on crude oils generated by mass spectrometry. As global oil resources deplete, oil companies are increasingly moving towards the extraction and refining of the still plentiful reserves of heavy, carbon rich and highly contaminated crude oil. It is essential that the oil industry gather the maximum possible amount of information about the crude oil prior to setting up the drilling infrastructure, in order to reduce processing costs. This project describes how machine learning can be used as a novel way to extract critical information from complex mass spectra which will aid in the processing of crude oils. The thesis discusses the experimental methods involved in acquiring high accuracy mass spectral data for a large and key industry-standard set of crude oil samples. These data are subsequently analysed to identify possible links between the raw mass spectra and certain physical properties of the oils, such as pour point and sulphur content. Methods including artificial neural networks and self organising maps are described and the use of spectral clustering and pattern recognition to classify crude oils is investigated. The main focus of the research, the creation of an original simulated annealing genetic algorithm hybrid technique (SAGA), is discussed in detail and the successes of modelling a number of different datasets using all described methods are outlined. Despite the complexity of the underlying mass spectrometry data, which reflects the considerable chemical diversity of the samples themselves, the results show that physical properties can be modelled with varying degrees of success. When modelling pour point temperatures, the artificial neural network achieved an average prediction error of less than 10% while SAGA predicted the same values with an average accuracy of more than 85%. It did not prove possible to model any of the other properties with such statistical significance; however improvements to feature extraction and pre-processing of the spectral data as well as enhancement of the modelling techniques should yield more consistent and statistically reliable results. These should in due course lead to a comprehensive model which the oil industry can use to process crude oil data using rapid and cost effective analytical methods.

Molecular dynamics (MD) simulations are used to compute the equilibrium dynamics of a single component fluid with Yukawa interaction potential v(r) = (Ze)^2 exp(−r/λs )/4π eps_0 r. This system, which is known as the Yukawa one-component plasma (YOCP), represents a simplified description of a non-ideal plasma consisting of ions, charge Ze, and electrons. For finite screening lengths λs, the MD results are used to investigate the domain of validity of the hydrodynamic description, i.e., the description given by the Navier-Stokes equations. The way in which this domain depends on the thermodynamic conditions of the YOCP, as well as the strength and range of the interactions, is determined. Remarkably, it is found that the domain of validity is completely determined by the range of the interactions (i.e., λs); this alone determines the maximum wave number k_max at which the hydrodynamic description is applicable. The dynamics of the YOCP at wavevectors beyond k_max are then investigated; these are shown to be in striking agreement with a simple and well known generalisation of the Navier-Stokes equations. In the extreme case of the Coulomb interaction potential (λs = ∞), the very existence of a hydrodynamic description is a known but unsolved problem [Baus & Hansen, 1980]. For this important special case, known as the one-component plasma (OCP), it is shown that the ordinary hydrodynamic description is never valid. Since the OCP is the prototypical system representing a non-ideal plasma, a number of different approaches for modelling its dynamics have been formulated previously. By computing the relevant quantities with MD, the applicability of a number of models proposed in the literature is examined for the first time.

The outcome of chemical reactions proceeding via radical pair (RP) intermediates can be influenced by the magnitude and direction of applied magnetic fields, even for interaction strengths far smaller than the thermal energy. Sensitivity to Earth-strength magnetic fields has been suggested as a biophysical mechanism of animal magnetoreception and this thesis is concerned with simulations of the effects of such weak magnetic fields on RP reaction yields. State-space restriction techniques previously used in the simulation of NMR spectra are here applied to RPs. Methods for improving the efficiency of Liouville-space spin dynamics calculations are presented along with a procedure to form operators directly into a reduced state-space. These are implemented in the spin dynamics software Spinach. Entanglement is shown to be a crucial ingredient for the observation of a low field effect on RP reaction yields in some cases. It is also observed that many chemically plausible initial states possess an inherent directionality which may be a useful source of anisotropy in RP reactions. The nature of the radical species involved in magnetoreception is investigated theoretically. It has been shown that European Robins are disorientated by weak radio-frequency (RF) fields at the frequency corresponding to the Zeeman splitting of a free electron. The potential role of superoxide and dioxygen is investigated and the anisotropic reaction yield in the presence of a RF-field, without a static field, is calculated. Magnetic field effect data for Escherichia coli photolyase and Arabidopsis thaliana cryptochrome 1, both expected to be magnetically sensitive, are satisfactorily modelled only when singlet-triplet dephasing is included. With a view to increasing the reaction yield anisotropy of a RP magnetoreceptor, a brief study of the amplification of the magnetic field experienced by a RP from nearby magnetite particles is presented. Finally in a digression from RPs, Spinach is used to determine the states expected to be immune from relaxation and therefore long-lived in NMR experiments on multi-spin systems.

In this thesis, the response of DNA and RNA to linear and torsional mechanical stress is studied using coarse-grained models. Inspired by single-molecule assays developed over the last two decades, the end-to-end extension, buckling and torque response behaviour of the stressed molecules is probed under conditions similar to experimentally used setups. Direct comparison with experimental data yields excellent agreement for many conditions. Results from coarse-grained simulations are also compared to the predictions of continuum models of linear polymer elasticity. A state diagram for supercoiled DNA as a function of twist and tension is determined. A novel confomational state of mechanically stressed DNA is proposed, consisting of a plectonemic structure with a denaturation bubble localized in its end-loop. The interconversion between this novel state and other, known structural motifs of supercoiled DNA is studied in detail. In particular, the influence of sequence properties on the novel state is investigated. Several possible implications for supercoiled DNA structures in vivo are discussed. Furthermore, the dynamical consequences of coupled denaturation and writhing are studied, and used to explain observations from recent single molecule experiments of DNA strand dynamics. Finally, the denaturation behaviour, topology and dynamics of short DNA minicircles is studies using coarse-grained simulations. Long-range interactions in the denaturation behaviour of the system are observed. These are induced by the topology of the system, and are consistent with results from recent molecular imaging studies. The results from coarse-grained simulations are related to modelling of the same system in all-atom simulations and a local denaturation model of DNA, yielding insight into the applicability of these different modelling approaches to study different processes in nucleic acids.

In this work, we wish to determine the free energy landscape and the nucleation rate associated with the process of homogeneous ice nucleation. To do this, we simulate the homogeneous nucleation of ice with the mW monatomic model of water and with all-atom models of water using primarily the umbrella sampling rare event method. We find that the use of the mW model of water, which has simpler dynamics compared to all-atom models of water, but is nevertheless surprisingly good at reproducing experimental data, results in very reasonable agreement with classical nucleation theory, in contrast to some previous simulations of homogeneous ice nucleation. We suggest that previous simulations did not observe the lowest free energy pathway in order parameter space because of their use of global order parameters, leading to a deviation from classical nucleation theory predictions. Whilst monatomic water can nucleate reasonably quickly, all-atom models of water are considerably more difficult to simulate, primarily because of their slow dynamics of ice growth and the fact that standard order parameters do not work well in driving nucleation when such models are being used. In this thesis, we describe a local, rotationally invariant order parameter that is capable of growing ice homogeneously in a biassed simulation without the unnatural effects introduced by global order parameters, and without leading to non-physical chain-like growth of 'ice' clusters that results from a naïve implementation of the standard Steinhardt-Ten Wolde order parameter. We have successfully used this order parameter to force the growth of ice clusters in simulations of all-atom models of water. However, although ice growth can be achieved, equilibrating simulations with all-atom models of water is extremely difficult. We describe several approaches to speeding up the equilibration in all-atom models of water to enable the computation of free energy profiles for homogeneous ice nucleation.

Mechanisms of three reactions catalyzed by microporous materials were investigated computationally; the reactions investigated include Friedländer and Knoevenagel reactions catalyzed by Cu3BTC2 metal organic framework (MOF) and an intramolecular cyclisation of unsaturated alcohols catalyzed by zeolite H-ZSM-5. It was found that the reaction mechanisms of all three reactions are controlled by a high concentration of active sites in materials. Reaction intermediates interact with more than one active site simultaneously. This novel concept of "multiple-site" interactions is described. The concerted effect of two catalytic sites leads to a decrease of activation barriers on reaction paths of Friedländer and Knoevenagel reactions. On the contrary, a simultaneous interaction of reactants with two active sites has a negative effect on reaction rate in case of alcohol cyclization catalyzed by H-ZSM-5; it was found that the interaction with dual sites results in the increase of activation barriers and diffusion limitations. In case of Knoevenagel reaction catalyzed by CuBTC, the adsorption of reaction precursor to the reaction site allows the creation of a dynamic defect in the MOF framework that subsequently catalyses the reaction. Both, the multiple sites effect and the dynamical defect formation effect...