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This thesis explores the design, synthesis, optoelectronic properties, and photocatalytic testing of copper(I) complexes with a two- or three-coordinate geometry bearing N-heterocyclic carbene ligands. Our design strategies aim to avoid the failings of four-coordinate copper(I) complexes which are prone to Jahn-Teller distortion in the excited state. The first chapter provides an overview of the fundamental photophysical properties of transition metal complexes and an introduction to bimolecular energy and electron transfer processes. A mini-review of Earth-abundant photocatalysts is presented. Special attention will then be devoted to describing the design principles of copper(I) based photocatalysts (PCs). We begin by describing the photophysical and electrochemical properties of four-coordinate copper(I) complexes, followed by the design and applications of copper(I) complexes as PCs. Next, we provide a summary of the development of two- and three-coordinate copper(I) complexes, describing their photophysical properties and applications. Chapter 2 describes the synthesis, characterisation and optoelectronic properties of seven three-coordinate copper(I) N-heterocyclic carbene (NHC) complexes bearing N^N ligands, of the form of [Cu(IPr)(N^N)]PF₆ (where IPr is 1,3-Bis(2,6-diisopropylphenyl)imidazolium, and N^N is phen = 1,10-phenanthroline, bpy = 2,2'-bipyridine, dpa = 2,2'-dipyridylamine, mdpa = N-methyl-2,2'-dipyridylamine, dpym = di(pyridin-2-yl)methane, phdpym = 2,2'-(phenylmethylene)dipyridine, fludpym = 2,2'-(9H-fluorene-9,9-diyl)dipyridine). Six complexes underwent successful preliminary photocatalytic testing in two mechanistically distinct photocatalysis reactions: an Atom Transfer Radical Addition (ATRA) photoinduced electron transfer (PET) reaction between an alkyl halide and an alkene; and photoinduced energy transfer (PEnT) reaction of a vinyl azide to a vinyl nitrene, which subsequently rearranges to form a pyrrole. The complex [Cu(IPr)(phen)]PF₆ performed best in the ATRA reaction (77% NMR yield) out of six complexes tested, while complex [Cu(IPr)(phdpym)]PF₆ performed best in the PEnT reaction (59% NMR yield vs 66% obtained with [Cu(dmp)(BINAP)]BF₄). Chapter 3 reports the synthesis, characterisation and optoelectronic properties of a series of three-coordinate copper(I) NHC complexes bearing a dpa ligand, of the form [Cu(NHC)(dpa)]PF₆. Three complexes underwent preliminary photocatalytic testing, however their performance as PCs was poor. Chapter 4 reports the first example of a neutral linear two-coordinate Cu(I) complex Cu(IPrBIAN)(Cz) bearing an IPrBIAN NHC ligand, and after investigation of its optoelectronic properties, it was successfully applied as a PC in a proof-of-concept PET ATRA reaction (52% NMR yield). Cu(IPrBIAN)(Cz) also exhibited interesting dual emission properties in solution; this is seen as separate emission depending on excitation source for most solvents, and is especially strong in chloroform solution. The synthesis, characterisation, and optoelectronic properties of further derivatives of this complex bearing 3,6-substituted carbazolate ligands were also investigated. The final conclusions chapter summarises the results of each chapter and brings focus to the overarching aims and themes of the thesis. This chapter identifies key areas for future work in the field of two- and three-coordinate copper(I) complexes as photocatalysts, specifically the need to improve PC stability, investigate reaction mechanisms, and correlate structure-property relationships with photocatalytic performance. A need for standardisation of photocatalysis reaction set-ups is identified as a requirement for reproducibility of results within the field of photocatalysis.

Climate change due to greenhouse gas build up in the earth’s atmosphere is an existential threat to humanity. To mitigate climate change, a significant shift from fossil fuels is necessary. Over the years, several renewable energy sources like solar, wind, geothermal etc. have been explored with the aim providing carbon-free energy. In this work, we focus on using density functional theory (DFT) methods to investigate key functional properties of materials of interest for applications in solar cells and catalytic conversion for energy generation. We show geometric effects of carboxylic acid binding on a transition metal surface to impact the deoxygenation reaction mechanism. Using insights from binding energy calculations and transition state theory, we elucidate the reaction pathway. We then use the same methods to investigate the surface of perovskites substituted with organic ligands and show the effect of fluorination of the phenyl ring of anilinium on the relative surface energy, relative binding energy, surface penetration, work function, and surface electronic properties. Lastly, we turn to DFT studies of molecular systems to investigate the impact of a perylene diimide (PDI) chromophore substituted on a rhenium-based organometallic complex. We show that unfolding of the PDI away from the organometallic complex is a key step in the catalytic reduction of CO2 and demonstrate how the PDI acts as an electronic reservoir during this process.

Organic semiconductors have gained widespread attention due to their potential applications in flexible, low-cost, lightweight electronics, energy storage and generation technologies, and sensing applications. However, developing new organic semiconductors with improved performance remains a significant challenge due to the vast chemical space of possible molecular and materials structures. Furthermore, the high cost and time-consuming nature of experimental synthesis and characterization hinder the rapid discovery of new materials. To overcome these challenges, this dissertation presents a data-driven approach to organic semiconductor discovery. The primary focus of this work is the development of data-driven tools, namely machine learning models, to predict critical electronic and structural properties of molecular organic semiconductors. These models are trained on a large dataset of quantum-chemical calculations, enabling the efficient screening of thousands of candidate molecules. In addition to developing the predictive models, this work includes creating a user-friendly web platform. The platform enables access to the models and rapid exploration of the chemical space to design new materials. The platform also includes visualization and analysis tools to guide the design process and facilitate research collaboration. The data-driven tools developed in this research have the potential to significantly accelerate the discovery and development of new molecular organic semiconductors, paving the way for the next generation of flexible electronics and renewable energy technologies. Overall, this dissertation offers a practical and innovative framework for designing organic semiconductors, leveraging data-driven approaches to overcome the challenges of the traditional experimental trial-and-error process.

The immense synthetic design space and material versatility have driven the exploration and development of organic semiconductors (OSC) over several decades. While many OSC designs focus on the chemistries of the molecular or polymer building blocks, a priori, multiscale control over the solid-state morphology is required for effective application of the active layer in a given technology. However, molecular assembly during solid-state formation is a complex function interconnecting the building block chemistry and the processing environment. Insufficient knowledge as to how these aspects engage, especially at the atomistic and molecular scales, has so far limited the ability to predict OSC solid-state morphology, leaving Edisonian approaches as the stalwart methods. Therefore, through multiscale simulations combining atomistic quantum scale modeling and state-of-the-art molecular dynamics (MD) techniques, we aim to establish first principles understanding required to synthetically regulate solid-state morphology of organic semiconductors (OSC) as a function of molecular chemistry and processing. In turn, we try to understand the deceivingly simple yet complex mechanisms behind molecular aggregation and crystallization of OSC. Simultaneously, we develop semi-to-fully automated high-throughput schemes to automate the complex and labor-intensive analyses to generate data based on various crystal structures in different crystallization environments. Ultimately, we aim to bridge molecular-scale information revealed on solid-state physical organization, understood in the context of chromophore chemistry and the molecular environment, with the macro scale properties to uncover useful guidelines for rational design and morphology regulation of OSC systems.

Although the activation of inert C–H bonds by metal-oxo complexes has been widely studied, important questions remain, particularly regarding the roles of oxygen spin population (i.e., unpaired electrons on the oxo ligand), the antiferromagnetic coupling and low lying excited states in facilitating C–H bond cleavage. In order to investigate these factors, we have utilized experimental and computational methods. Pulse EPR experiments were performed on isotopically labelled heme enzyme compound I intermediates to measure spin populations. Semiempirical modeling was utilized to relate spectroscopically obtained parameters to information about the excited states.

Nuclear magnetic resonance (NMR) chemical shifts play a large role in the structuralcharacterization of amorphous or disordered solids. When combined with X-ray diffraction, NMR-assisted crystallography can routinely generate angstrom resolution of crystals structures. However, the solid-state NMR spectrum can be complicated and often requires chemical shift prediction models to refine and/or validate candidate structures. While density functional theory (DFT) methods provide a reasonable computational ``cost-to-accuracy'' ratio for chemical shift prediction, current methods are (1) limited in accuracy by the usage of generalized gradient approximation functionals in plane wave basis sets, or (2) become a computational bottleneck when applied to numerous structures. In this work, we describe our efforts to improve chemical shift prediction accuracy and computational cost. First, we examine a simple monomer correction to the de facto planewave DFT NMR method, GIPAW. We show that one can improve accuracy by refining the intramolecular contribution and incorporating a more accurate description, such as that obtained by hybrid functionals like PBE0 that include a fraction of exact Hartree-Fock exchange. However, not all systems are neatly described by periodic unit cells, such as biomolecular systems. In those cases, one can use a cluster approximation which models the atoms of interest and their neighbors with local atomic basis sets. To further reduce the computational cost, fragment methods, which decompose the system into many smaller/manageable calculations, can be used. We explore how polarizable continuum models (PCM) can be used on highly-charged fragments to better mimic the accuracy of cluster models with lower cost. Lastly, we developed machine learning (ML) methods to reduce the computational cost of ab initio calculations. We demonstrate the use of ML methods to reproduce PBE0/6-311+G(2d,p) predictions of solution-phase organic molecules through ∆-ML. We show that ∆-ML “corrects'' an inexpensive calculation and is 2-3 orders of magnitude faster than legacy calculations without sacrificing accuracy. Finally, we investigate a new class of ML models called graph neural networks for solid-state NMR predictions. We use convolutional and attentional graph operators for chemical shift prediction, and show the best accuracy for 15 N and 17 O compared to literature precedents.

In recent years, collaborative studies that leverage experimental synthesis, spectroscopic characterization, and electronic structure analysis have enabled significant advances in f-element chemistry through the discovery of newly accessible metal oxidation states and novel electronic configurations for lanthanide (Ln) and actinide (An)-containing species. With the purpose of extending the utility of computational approaches to maintain this positive trajectory, the present thesis discusses recent developments and applications of computational chemistry methods, with a focus on density functional theory (DFT), towards the accurate prediction and characterization of new f-element complexes. A DFT methodology based on (meta)-generalized gradient approximation (mGGA) density functionals, triple-ζ quality basis sets for metal atoms, and effective core potentials (ECPs) is shown to provide the electronic structure insights necessary to understand the unexpected stability of fully linear Dy and Tb-based metallocene species, Dy(CpiPr5)2 and Tb(CpiPr5)2. Calculations reveal that such unorthodox molecular geometry, which is rarely observed for lanthanides, is facilitated by a 4fn 5d1 electronic configuration of the metal center, which gives rise to a σ−bonding interaction between the 5d/6s HOMO and cyclopentadienyl ligand system. Further calculations using this DFT methodology predict the existence of stable, linear An-based metallocenes, and the results of this study are used to guide synthetic efforts towards the experimental isolation of U(CpiPr5)2, the first An-based “ferrocene” analog. A similar computational methodology which replaces the ECP with an all-electron approach is applied towards the characterization of Ln-based spin molecular qubits, [Lu(OAr*)3]−, [La(OAr*)3]−, and [Lu(NR2)3]−, revealing the importance of 6s orbital contributions to the spin density to facilitate large Fermi-contact and thus hyperfine interactions. This thesis concludes by describing the prediction of accurate EPR parameters, such as the hyperfine coupling constant, electronic g-tensor, and quadrupole coupling constant in relativistic DFT for the broader study of candidate molecular qubits. An implementation of these quantities is presented within the relativistic exact two-component theory (X2C) method, and benchmark calculations on transition metal and f-element complexes are provided to evaluate choice of the relativistic Hamiltonian, basis set, and density functional approximation (DFA). A recommended set of parameters based on the results of these benchmarks is presented, and subsequently used to calculate the EPR parameters for the previous series of Lu and La molecular qubit systems. These predictions are found to reduce errors by roughly one order of magnitude when compared with the unrefined methodology, and are highly accurate when compared to experimental data - representing an advance for in-silico characterization of EPR spectra. Present challenges and future directions for the development of electronic structure methods for the study of the f-elements are assessed.

Density-functional theory (DFT) has gained popularity because of its ability to predict the properties of a large group of materials a priori. Even though DFT is exact, there are inaccuracies introduced into the theory due to the approximations in the exchange-correlation (XC) functionals. Over the 50 years of its existence, scientists have tried to improve the design of the XC functionals. The errors introduced by these functionals are not consistent across all types of solid-state materials. In this project, a high throughput framework was utilized to compare the theoretical DFT predictions with the experimental results available in the Inorganic Crystal Structure Database (ICSD). We analyzed the accuracy of over 1500 structures with different XC functionals, ranging from the most basic (local density approximation) to the recently designed meta-GGA functionals. Afterward, we focus on strongly correlated systems, where the triumphant ability of DFT stops short and the non-universality of the XC functionals becomes substantial. One solution to this problem is to introduce a Hubbard correction (+U) for the treatment of the strongly correlated electronic states, used in the so-called DFT+U approaches. Unfortunately, this correction turns the theory into a semi-empirical method as the exact values of the correction parameters are unknown and their parameterization can vary considerably from one material to another composed of the same strongly correlated atoms. In this work, we select a group of iron-based compounds to explore the space of the correction parameters that simultaneously improve the prediction for all the studied materials. We perform this exploration using a Bayesian calibration assisted by Markov chain Monte Carlo sampling to determine the distribution of the correction parameters for three widely used XC functionals. Finally, we use the insight gained from the previous studies to design a machine learning approach to the problematic XC functional approximations. We propose a streamlined route to generating data needed for a learner to produce personalized XC functionals (material specific) for any DFT calculation. This approach capitalizes on the unwanted non-universality of XC functionals. Further, we demonstrate a machine-driven unbiased approach to finding the global reaction coordinate. As an example, we use the azobenzene molecule to thoroughly describe a reaction mechanism for its photoisomerization. Our global reaction coordinate includes all of the internal coordinates of azobenzene contributing to the photoisomerization reaction coordinate. This method quantifies the contribution of each internal coordinate of the system to the overall reaction mechanism. Finally, we provide a detailed mapping on how each significantly contributing internal coordinate changes throughout the energy profile (in our example from trans to transition state and subsequently to cis).

In the following work, we discuss the development and application of efficient techniques either aimed at extending the applicability of the currently available methods in the liquid phases to larger system sizes, or at eliminating the artifacts with the existing techniques for several open-shell systems encountered in condensed phases. First, we have investigated the local solvation structure of aqueous hydroxyl radical and absorption spectrum by employing both the mixed quantum mechanics\slash molecular mechanics (QM/MM) framework and periodic density functional theory (DFT) framework. Theoretically, the presence of a hemibond (a two-center, three-electron bond) in this system has been debated for a long time. This structural motif has been explained as either an artifact arising from the self-interaction error (SIE) in DFT or an artifact because of the finite-size effects of the simulation cell but shown to play an important role in the absorption spectrum based on some theoretical studies on smaller representative clusters. Our investigations based on simulations with various DFT simulations suggest that a pseudo-hemibonded motif still persists in this system. We have also demonstrated that the population of hemibonds is extremely sensitive to the amount of exact Hartree-Fock (HF) exchange employed in the simulation. However, we have concluded that these hemibonded motifs play an outsized role in the absorption spectrum, even when present as a rare configuration, due to an intense charge-transfer transition in the hemibonded structures. To eliminate the artifacts arising from this SIE, we have then implemented the density-corrected DFT (DC-DFT) formalism along its analytical gradient, which has been proven to be powerful for the systems with larger density-driven errors. Afterward, we have applied this technique for studying the electronic structure descriptions of the hole defects in Al-doped silica and electron-polarons in anatase titanium oxide where most of the standard DFT functionals fail. Using DC-DFT with some cluster models, we have been successful in describing the proper localized nature of the hole in Al-doped as well as the proper structural distortion around the defect centers. This method has also removed a lot of density-driven error in the case of anatase. Next, we have applied this technique to study the stability ordering of different polymorphs of several organic crystals where standard DFT functionals perform poorly, but the ordering gets improved with the density-corrected versions. Furthermore, we have also done some method development with the QM/MM-Ewald framework by using CM5 charges (charge-model 5) in an effort to push the existing framework in simulating systems with larger QM sizes and longer timescales.

Nanoscale molecular structures are central to many current and emerging technologies. Integrating different materials into hybrid structures allows for combining the individual materials' strengths while compensating for deficits. However, an in-depth characterization of such heterogeneous structures is a significant challenge. Their physical and chemical properties vary on the scale of a few chemical bonds, which is extremely difficult to access by most existing spectroscopic techniques. "Raman scattering" is an optical characterization tool that gives precise chemically specific information about molecules' vibrational energies and provides unique spectral signatures for any chemical species. The development of Surface Enhanced Raman Spectroscopy (SERS)-like techniques drastically increased the scattering Raman cross-section, which made this approach applicable at the nanoscale. A new aspect that was not present in the conventional Raman is that SERS reports not only about the molecule identity, but also about its local chemical environment, encoded in the Chemical Enhancement (CE). This new information remains a complex theoretical problem. However, with good computational insight, this phenomenon may become a unique tool for studying the chemistry of interfaces.The first part of this work presents an example of a theoretical-computational study that contributes to a real-life problem that demands a precise understanding of the atomic-scale chemical interface. The project utilizes the power of the Density Functional Theory (DFT) to represent a complex chemical system with great detail. The computational data employs to explain new experimental data based on the well - understandable parameters of the interfacial electronic structure.The second part of this dissertation focuses on the Chemical Enhancement mechanism in SERS by using a two-state model. The results of this work show a stronger electronic coupling between an organic molecule and graphene quenches the Raman activity of the organic molecule, an opposite effect with previously observed on metals and semiconductors. The quenching attributes to the coexistence of two damping mechanisms. One is related to the orbital relaxation or the environment's response to the electron perturbed by the external electric field (electron damping). Another mechanism is the damping of molecular vibrations caused by the dynamic interfacial charge transfer driven by the vibrations (phonon damping).

With the advent of modern computing, the use of simulation in chemistry has become just as important as experiment. Simulations were originally only applicable to small molecules, but modern techniques, such as density functional theory (DFT) allow extension to materials science. While there are many valuable techniques for synthesis and characterization in chemistry laboratories, there are far more materials possible than can be synthesized, each with an entire host of surfaces. This wealth of chemical space to explore begs the use of computational chemistry to mimic synthesis and experimental characterization. In this work, genetic algorithms (GA), for the former, and DFT calculations, for the latter, are developed and used for the in silico exploration of materials chemistry. Genetic algorithms were first theorized in 1975 by John Holland and over the years subsequently expanded and developed for a variety of purposes. The first application to chemistry came in the early 1990’s and surface chemistry, specifically, appeared soon after. To complement the ability of a GA to explore chemical space is a second algorithmic technique: machine learning (ML) wherein a program is able to categorize or predict properties of an input after reviewing many, many examples of similar inputs. ML has more nebulous origins than GA, but applications to chemistry also appeared in the 1990’s. A history perspective and assessment of these techniques towards surface chemistry follows in this work. A GA designed to find the crystal structure of layered chemical materials given the material’s X-ray diffraction pattern is then developed. The approach reduces crystals into layers of atoms that are transformed and stacked until they repeat. In this manner, an entire crystal need only be represented by its base layer (or two, in some cases) and a set of instructions on how the layers are to be arranged and stacked. Molecules that may be present may not quite behave in this fashion, and so a second set of descriptors exist to determine the molecule’s position and orientation. Finally, the lattice of the unit cell is specified, and the structure is built to match. The GA determines the structure’s X-ray diffraction pattern, compares it against a provided experimental pattern, and assigns it a fitness value, where a higher value indicates a better match and a more fit individual. The most fit individuals mate, exchanging genetic material (which may mutate) to produce offspring which are further subjected to the same procedure. This GA can find the structure of bulk, layered, organic, and inorganic materials. Once a material’s bulk structure has been determined, surfaces of the material can be derived and analyzed by DFT. In this thesis, DFT is used to validate results from the GA regarding lithium-aluminum layered double hydroxide. Surface chemistry is more directly explored in the prediction of adsorbates on surfaces of lithiated nickel-manganese-cobalt oxide, a common cathode material in lithium-ion batteries. Surfaces are evaluated at the DFT+U level of theory, which reduces electron over-delocalization, and the energies of the surfaces both bare and with adsorbates are compared. By applying first-principles thermodynamics to predict system energies under varying temperatures and pressures, the behavior of these surfaces in experimental conditions is predicted to be mostly pristine and bare of adsorbates. For breadth, this thesis also presents an investigation of the electronic and optical properties of organic semiconductors via DFT and time-dependent DFT calculations.

Acid dissociation constants, often expressed as pKa values, afford vital information with regards to molecular behavior in various environments and are of significance in fields of organic, inorganic, and medicinal chemistry. Several quantitative structure-activity relationships (QSARs) were developed that correlate experimental pKas for a given class of compounds with a descriptor(s) calculated using density functional theory at the B3LYP/6-31+G** level utilizing the CPCM solvent model. A set of carbon acids provided a good final QSAR model of experimental aqueous pKas versus ΔEH2O (R2 = 0.9647) upon removal of three aldehydes as outliers. A study of saturated alcohols offered a final QSAR model with R2 = 0.9594, which was employed to confirm the behavior of the three aldehydes as hydrated species in aqueous solution. Finally, a study restricted to ketones was conducted to estimate their pKas in dimethyl sulfoxide solution. QSAR models of experimental pKas versus ΔEDMSO for the keto and enol tautomers were modest at best (R2 = 0.8477 and 0.7694, respectively). A binary linear regression was employed to incorporate descriptors representing both the keto and enol tautomers, improving the final R2 to 0.9670 upon removal of one outlier. The QSAR models presented may be utilized to estimate pKas for related compounds not offered in the existing literature or that are challenging to measure experimentally.

We report a joint anion photoelectron spectroscopy (PES) and theoretical study to investigate the effect of O2-binding on the mid-sized even-numbered gold clusters, Aun− (n = 20−34), a special size region of bare gold clusters that entail rich forms of structural evolution and transformation. Specifically, within this size range, bare Au20− is a highly-symmetric pyramidal cluster, bare Au21-25− are flat-planar or hollow-tubular clusters, bare Au26− is the smallest core-shell gold cluster, while bare Au34− is a magic-number/fluxional core-shell cluster with the high-symmetry tetrahedral Au4 core. In light of the strong size-dependent structural evolution of bare gold clusters in the n = 20−34 size range, we focused especially on the chemical interplay between the O2 binding and the structure of the host gold clusters. The global minima of the O2-bound gold clusters AunO2− are searched using the basin-hopping global optimization technique in conjugation with density functional theory calculations. Vertical detachment energies are computed for the low-lying isomers with the inclusion of spin−orbit effects for gold to generate simulated photoelectron spectra and to compare with the experimental PES spectra. Based on the global−minimum structures identified, a series of structural transitions, from the pyramidal to fused−planar to core-shell structures, are identified for the AunO2− clusters, where the O2 binding is found to be in either superoxo or peroxo fashion, depending on the size and shape of the host gold clusters. Advisor: Xiao Cheng Zeng

We discuss the development and application of multiple methodologies which will either make the traditional electronic structure methods more efficient or reveal the structural insight of condensed or gas-phase systems. The main idea revolves around the development and application of \textit{ab initio} resonance-Raman (RR) spectroscopy and how to achieve the efficiency to simulate the resonance-Raman spectra for biomolecules.To tackle this, the first step was the development of new quadrature grids for high precision integration of modern density functionals, as the choice of density functional for RR simulation stems from the former's accuracy and cost-effectiveness. Our pruned integration grids, SG-2 and SG-3 work well for modern difficult-to-integrate functionals alongside finding a balance between accuracy and computational cost.To calculate the vibrational spectra for a biomolecule, getting the optimized structure is important as normal mode analysis can be erroneous at a non-stationary point. All quantum-mechanical optimization of enzyme active sites can be tricky geometric constraints that need to be introduced to prevent the structural collapse of the model system during geometry optimizations that do not contain a full protein backbone. We introduce a simple alternative in which terminal atoms of the model system are placed in soft harmonic confining potentials rather than being rigidly constrained. The new approach is more efficient for optimizing minima and transition states, as compared to the use of fixed-atom constraints, and also more robust against unwanted imaginary frequencies. To calculate the RR intensities using all-electron quantum chemistry, we used the excited state gradient method under the independent mode displaced harmonic oscillator (IMDHO) approximation. Using the RR spectroscopy we get insightful information about the structure of the hydrated electron, which caused a decade long debate. Furthermore, we have integrated the \textit{ab-initio} molecular dynamics (AIMD) simulation of excited states along with the resonance Raman calculation to substantiate the experimental femtosecond stimulated Raman spectra (FSRS) spectra. This formalism helps us to understand the time-dependent evolution of specific vibrational modes.

Though organic semiconductors have illustrated potential as industry-relevant materials for electronics applications, there are few guidelines that can take one from molecular design to functional materials. This limitation is, in part, due to incomplete understanding as to how the atomic-scale construction of the π-conjugated molecules that comprise the organic semiconductors determines the nature and strength of both the noncovalent intramolecular interactions that govern molecular conformation and noncovalent intermolecular interactions that regulate the energetic preference for solid-state packing. Hence, there remain several fundamental questions that need to be resolved in order to design organic semiconductors from a priori knowledge, including: What is the relevance of the relatively weak noncovalent intramolecular interactions on determining molecular structure, are current hypotheses put forward as to important interactions valid, and how does chemical substitution as various positions along the π-conjugated backbone impact these interactions? How do the intermolecular noncovalent interactions regulate solid-state packing, are there features of the molecular structure – e.g. the π-conjugated backbone, heteroatoms, or pendent alkyl chains – that play a more important role? What connections can be made between the structures/properties of the π-conjugated molecules and the resulting organic semiconductors? In this dissertation, Chapter 1 provides an introductory discussion of these questions and a brief review of previous studies. Chapter 2 details the computational approaches that were implemented throughout the course of the thesis work. Chapter 3 describes the investigation of a series of pyrene-acene molecules to illustrate the importance of choosing the right molecular structure in π-conjugated chromophores. In Chapter 4, S...F noncovalent intramolecular interactions are systematically investigated in two separate cases to highlight the varied impact that these interactions can have on molecular and solid-state packing structures. Chapter 5 describes the investigation of an oscillatory crystal packing structure observed for a series of oligothiophenes that follow the odd-even carbon-atom counts of the pendant alkyl chains. In Chapter 6, the polymorphism of functionalized pentacene molecules is studied to reveal how seemingly simple atomic substitutions can drastically alter solid-state packing. To systematically address the aforementioned fundamental questions, Chapter 7 describes the construction and application of a database of crystalline molecular organic semiconductors. Finally, perspectives regarding future research are provided in Chapter 8.

While optoelectronic devices built from organic semiconductors (OSC) continue to find their way into the commercial landscape, there remain numerous challenges to overcome to supplant traditional semiconductors in many technologies. Chief among these are low performance metrics relative to devices with conventional semiconductors and device stability. In order to overcome these challenges, a wide range of new OSC and processing technologies have been developed. However, there remains limited fundamental understanding of the relationship between molecular structure, packing in the solid state, and the resulting materials properties. Here, we make use of multiscale molecular models and utilize classical all-atom molecular dynamic (MD) simulations and quantum-chemical density functional theory (DFT) to explore the molecular structure-function relationships. Our results have provided distinctive insights into how the chemistries and processing of the OSC impact the material characteristics.

The selective conversion of alkanes to high value products remains a monumental catalytic challenge. Significant gaps in our knowledge remain regarding the mechanistic details of alkane activation and the chemical nature of the catalytically active sites for this class of reactions. In this thesis, systematic and thorough first principles studies of alkane activation on several major classes of promising materials, ranging from metal oxide surfaces to metal clusters and single metal atom sites, have provided us with the insight to tackle these open questions. To further expand these findings towards a comprehensive search of catalytically active materials, we developed chemical and catalytic descriptors which can predict these properties at a fraction of the computational cost. We find on oxide surfaces, the homolytic C-H activation pathway is favored, and correlated with the reducibility of the lattice oxygen. We identify hydrogen adsorption energy and vacancy formation as strongly correlated energetic descriptors for C-H activation energy and CH3 adsorption energy, which we use to predict catalytic properties of doped Co3O4 and compositionally diverse perovskite surfaces. To provide a structure-activity link between the oxygen and alkane activation, we developed a generally applicable coordination number descriptor, ‘ACN,’ which can quickly predict C-H activation barriers on various oxides. Furthermore, we identify a bulk descriptor, the metal-oxygen crystal orbital Hamilton population, which is correlated to the adsorption energy of radical adsorbates such as hydrogen and methyl. Meanwhile on metal clusters, structural fluxionality plays a pivotal role in catalytic performance. In the model system of Pt10-13, we utilize various structural features to identify Pt10 as a magic number cluster with low fluxionality which can explain its experimental inertness to alkanes. On single atoms, we demonstrate how heterolytic C-H cleavage pathway can be tuned through the metal d-orbital occupation and the substrate electronic band gap. These findings have allowed us to identify promising single atom catalysts with the elusive low-temperature methane activation ability. The work in this thesis aims to provide valuable descriptors and design principles for alkane activation and theoretical predictions of promising catalysts.

In bulk form, gold is a well-known noble metal. However, gold nanoparticles exhibit remarkable catalytic behavior towards various types of chemical reactions. These catalytic properties are highly dependent upon the size and shape and therefore knowledge of the geometries of these nanoparticles is of prime importance. The main goal of this dissertation is, therefore, going to be towards the elucidation of the structures of novel gold clusters using a combined experimental and theoretical approach and subsequent modeling to explore the binding of simple substrates like carbon monoxide with gold clusters. The theoretical method used to study the structural evolution of these nanoparticles involves global optimization. Basin-hopping (BH) algorithm in conjugation with Density Functional Theory (DFT) has been extremely successful in exploring the potential energy surface of novel pure and mixed gold clusters. The combination of photoelectron spectroscopy with theoretical methods is an important tool for studying the various types of pure as well as alloy gold nanoclusters. Apart from the structure elucidation of medium-sized gold nanoclusters and small-sized gold-aluminum alloy clusters, this method has also enabled us to unravel new geometries of medium-sized gold nanoclusters when they bind to small molecule substrates such as carbon monoxide and therefore help in better understanding of the catalytic activities and in illustrating the reaction pathways. Adviser: Xiao Cheng Zeng

redictive modeling of fluid phase and sorption equilibria for reacting systems presents one of the grand challenges in the field of molecular simulation. Difficulties in the study of such systems arise from the need (i) to accurately model both strong, short-ranged interactions leading to the formation of chemical bonds and weak interactions representing the environment, and (ii) to sample the range of time scales involving frequent molecular collisions, slow diffusion, and infrequent reactive events. This thesis showcases some of my efforts in developing and applying advanced simulation methods to a variety of important systems. Chapters 2 and 3 describe how a novel Monte Carlo method (reactive first principles Monte Carlo or RxFPMC) can be used to overcome some limitations of existing methods for simulation of reactive systems. Chapter 4 shows how advanced sampling techniques in combination with sophisticated interatomic potentials can be used to elucidate nucleation pathways. Chapters 5 and 6 manifest how first principles simulations can be leveraged to understand liquid structure of novel complex solvents as well as reactive processes in such solvents. Finally, the last chapter discusses the use of smart sampling algorithms to study chemisorption of mixed ligands on nanoparticles.

Supercapacitors, such as electric double-layer capacitors (EDLCs) and pseudocapacitors, are becoming increasingly important in the field of electrical energy storage. Theoretical study of energy storage in EDLCs focuses on solving for the electric double-layer structure in different electrode geometries and electrolyte components, which can be achieved by molecular simulations such as classical molecular dynamics (MD), classical density functional theory (classical DFT), and Monte-Carlo (MC) methods. In recent years, combining first-principles and classical simulation to investigate the carbon-based EDLCs has become increasingly popular due to the importance of quantum capacitance in graphene-like 2D systems. More recently, the development of joint density functional theory (JDFT) enables self-consistent electronic-structure calculation for an electrode being solvated by an electrolyte. In contrast with the large amount of theoretical and computational effort on EDLCs, theoretical understanding of pseudocapacitance is very limited. In this dissertation project, we first study the carbon-based EDLCs and focus on several important aspects of EDLCs including quantum capacitance, nitrogen doping, dielectric screening, and edge effect. Then, based on our physical understanding on EDLCs, we designed novel 2D boron supercapacitors, which exhibit promising capacitive performance comparing with conventional carbon electrodes. Finally, we also introduced how to study pseudocapactive mechanism through first principle calculation with solvation model technique. Two typical pseudocapacitor systems are investigated: RuO2(110) and Ti3C2Tx (T=O,OH) in H2SO4 electrolyte. Based on our study, the relation between EDL and redox behavior were revealed. We summarize and conclude with an outlook for the future of materials design and discovery for capacitive energy storage.

Hydrogen-bonded systems often exhibit very broad and unusually shaped features in their vibrational spectra. The origin of these features is oftentimes unclear. Consequently, computational methods are frequently used to model these features in order to better understand their origin. However, reproducing these features with computational methods is often quite challenging. Many methods have been developed each of which has its own advantages and disadvantages. The research presented in this thesis focuses on the development and application of new computational methods to reproduce the multi-hump (broad peak) vibrational features found in many hydrogen-bonded dimers. These methods utilize density functional theory to calculate the potential energy surface and a quantum variational approach to calculate vibrational spectra from these surfaces. One of the methods developed involves adiabatically separating lower frequency vibrational modes, which modulate the hydrogen bond length, from higher frequency modes that contribute to the structure. Another method that was developed uses a classical molecular dynamics simulation, in place of low-frequency modes, to more accurately sample configurations. The results of the calculations performed with these methods indicate that the broadness of these multi-hump features originate from low-frequency modes modulating the hydrogen bond length, while the multi-hump lineshape is derived from strong Fermi resonances between the OH stretch and the OH bending modes.

We present a study of isotopically pure He-4 systems evaluated using helium density functional theory (He-DFT) with the intent of better understanding their ground state structural and energetic properties, particularly within the scope of singularly-doped helium droplets. We self-consistently solve for the density profiles and chemical potentials for a wide range of pure helium droplet sizes (up to 9500 atoms) via an imaginary time propagation method, and fit the resultant energetic data to a power law formula to be able to extrapolate values for even larger droplets. Subsequent calculations on singularly-doped droplets within the same size range yield accurate binding energies for atomic dopants. We then suggest a method of predicting droplet size distributions after pickup of a dopant atom based on an initial distribution and a particular quantity of imparted energy using chemical potential values predicted from our He-DFT calculations, with the intent of providing a means for understanding data generated by helium droplet calorimetry experiments. Along the way, we also rigorously analyze four of the most popular helium density functionals published in the literature in terms of their robustness, computational cost, and accuracy. To conclude, we perform He-DFT simulations of systems that deviate far from bulk helium behavior in order to evaluate the capabilities of the He-DFT method in such scenarios. We find that a density functional treatment of both small and strongly bound droplets is a surprisingly adequate and efficient alternative to more powerful quantum Monte Carlo calculations.

In this dissertation, I introduce both ground-state and thermal density functional theory. Throughout I use the asymmetric two-site Hubbard model, called the Hubbard dimer for short, to better understand and/or develop these theories. This model is used because it can be solved analytically and it contains all the necessary physics while still being conceptually simple enough to tease apart the various aspects of density functional theory. Ground-statedensity functional theory has seen broad use in many disciplines including physics, chemistry, geology, and material science and has led to a number of important physical and technological successes. In the first two chapters I elucidate the behavior of the ground-state theory using the Hubbard dimer. The simplicity of the model allows me to showcase aspects of the theory that are common points of confusion within the electronic structure community, e.g. the fundamental gap problem. The next two chapters focus on thermal density functional theory which has been coming to prominence as the study of warm dense matter has becomea growing interest at the national laboratories and in the astronomical body community. The Hubbard dimer allows me to do the first ever exact thermal density functional theory calculation. In this work I am better able to understand the approximations used in thermal density functional theory and can point to why they succeed and fail. This also allows me to illustrate old conditions and derive new ones. I conclude with an overview of the work and a few different directions in which the asymmetric Hubbard dimer could be used further.

This thesis is primarily concerned with the development of tools aiming to improve the efficiency of electronic structure calculations using modern density functional theory (DFT), and to utilize the results of DFT-based energy decomposition analysis (EDA) for probing and modeling intermolecular interactions. It covers three distinct while also related topics: (i) development of a novel scheme to perform self-consistent field (SCF) calculations using a minimal adaptive basis (MAB) that is optimized on-the-fly; (ii) development and assessment of energy decomposition analysis (EDA) methods, including a new EDA scheme that is capable of probing the observable effects of intermolecular interactions, and a thorough discussion on the definition of the charge-transfer term in EDA methods; (iii) application of EDA to the development of potential energy surfaces for molecular simulations, including an assessment of the AMOEBA polarizable force field, and the formulation, implementation, and analysis of a mutually polarizable QM/MM scheme employing AMOEBA.

We discuss the development and application of a number of fragmentation methods focused on understanding of intermolecular interactions in different systems. The advantage of fragmentation methods is to avoid the exponential growth of required computational power for the most advanced and accurate quantum chemistry theories which preclude the application in systems with large number of atoms and molecules. In those fragmentation methods, the full chemical system is partitioned into different subsystems, circumventing the exponential scaling computational cost. How this partitioning is performed and applied appropriately is the principal emphasis of this work. One of the fragmentation methods developed by our group, called extended XSAPT, combines an efficient, iterative, monomer-based approach to computing many-body polarization interactions with a two-body version of symmetry-adapted perturbation theory (SAPT). The result is an efficient method for computing accurate intermolecular interaction energies in large non-covalent assemblies such as molecular and ionic clusters, supramolecular complexes, clathrates, or DNA--drug complexes. As in traditional SAPT, the XSAPT energy is decomposable into physically-meaningful components. Dispersion interactions are problematic in traditional low-order SAPT, and the empirical atom-atom dispersion potentials are introduced here in an attempt to improve this situation. Comparison to high-level ab initio benchmarks for biologically-related dimers, water clusters, halide--water clusters, supremolecular complexes, methane clathrate hydrates, and a DNA intercalation complex illustrate both the accuracy of XSAPT-based methods as well as their limitations. The computational cost of XSAPT scales as third to fifth order with respect to monomer size, depending upon the particular version that is employed, but the accuracy is typically superior to alternative ab initio methods with similar scaling. Moreover, the monomer-based nature of XSAPT calculations makes them trivially parallelizable, such that wall times scale linearly with respect to the number of monomer units. XSAPT-based methods thus open the door to both qualitative and quantitative studies of non-covalent interactions in clusters, biomolecules, and condensed-phase systems.

Pyrimidines and related heterocycles are an important class of compounds with a wide variety of applications. As a result, there is great interest in the chemical properties of these compounds, specifically their pKa’s. Despite the importance of these compounds their reported pKa’s are often only approximations or are completely absent from literature. Experimentally measuring pKa’s can be challenging, especially if the pKa is below zero. Alternatively, pKa’s can be estimated computationally using a Quantitative Structure-Activity Relationship (QSAR). However, most of the compounds included in this study exist as two or more forms that arise from tautomerism. Tautomerism complicates the estimation of pKa’s because proton transfers result in a rearrangement of electron density that alters chemical behavior. As a result, pKa’s are unique to each tautomeric species. This research identifies the most stable tautomer associated with each of the compounds included in this study. From here QSAR models have been constructed using a variety of molecular descriptors. The resulting QSAR models are then used to estimate the pKa’s of compounds for which there are currently no literature values.

In the exploration of chemical systems through quantum mechanics, accurate treatment of the electron wavefunction, and the related electron density, is fundamental to extracting information concerning properties of a system. This work examines challenges in achieving accurate chemical information through manipulation of the one-electron basis set.

Zeolites containing Brønsted or Lewis acid sites are extensively used catalysts in industrial processes. In this study, force field parameters are derived for the quantum mechanics/molecular mechanics (QM/MM) simulations of reactions in zeolites by reducing the deviation between QM/MM calculations and experimental data over a range of adsorption energies. The accuracy of the thermal correction for adsorption enthalpies determined by the rigid rotor-harmonic oscillator approximation (RRHO) is examined and shown to be improved by treating low-lying vibrational modes as free translational and rotational modes via a quasi-RRHO model. With the quasi-RRHO scheme, the QM/MM simulations can accurately reproduce experimental adsorption energies for both nonpolar and polar molecules adsorbed in MFI, H-MFI, and H-BEA. The anharmonic effects of intramolecular nuclear motions, namely torsions and vibrations, are also examined in this study. Comparing with the harmonic oscillator approximation, the accuracy of the calculation of molecular partition functions, heat capacities, entropies, and enthalpies can be improved with an uncoupled mode (UM) model, where the full-dimensional potential energy surface for internal motions is modeled as a sum of independent one-dimensional potentials for each mode. However, the extent of improvements is very limited if the one-dimensional potentials are determined by the energy as a function of displacement along each normal mode. Significant improvement can be achieved by constructing the potentials for internal rotations and vibrations separately using the energy surfaces along the torsional coordinates and the remaining vibrational normal modes. Three reactions catalyzed by BEA zeolite are investigated in this study using the QM/MM model, including the formation of p-xylene from ethylene and 2,5-dimethylfuran (DMF) in H-BEA as well as the isomerization of glucose to fructose and the synthesis of 4-(hydroxymethyl)benzoic acid (HMBA) from ethylene and 5-(hydroxymethyl)furoic acid (HMFA) in Sn-BEA. The pathways and energy barriers of these reactions derived by QM/MM simulations agree well with the available experimental results, which validates the realism of the QM/MM model. The influence of solvents and the effect of active site structures and heteroatoms on reaction barriers are investigated to derive criteria for future catalyst design.

This thesis is concerned with the development of minimally-parameterized and highly-transferable density functionals. A methodology for searching a given functional space is developed and used to parameterize three novel functionals: ωB97X-V -- a 10-parameter, range-separated hybrid, generalized gradient approximation density functional with VV10 nonlocal correlation, B97M-V -- a 12-parameter, local meta-generalized gradient approximation density funcitonal with VV10 nonlocal correlation, and ωB97M-V -- a 12-parameter, range-separated hybrid, meta-generalized gradient approximation density functional with VV10 nonlocal correlation. These three functionals are validated by comparisons to the best existing density functionals in their class, and their proper usage (with respect to basis sets and integration grids) is documented to facilitate use in chemical applications.

Phthalocyanines (Pc) have gained intense research attention in many diverse application areas due to their highly tunable electronic and structural properties through modification of the molecular periphery and metal center. Throughout this work a series of novel perfluoro-isopropyl substituted MPc have been investigated through theoretical methods. First, the synthetic mechanisms of these Pcs will be explored to gain insight into the experimentally observed Pc product distribution. By examining the electronic structure and formation energies of the various Pc precursors, we explain the product distribution as well as propose the formation of additional Pcs, which were not currently believed to form. The effect of metal center and peripheral modification on the Pc structural and electronic properties is also determined through a systematic investigation of several Pcs with varying degree of peripheral modification as well as several different metal centers. Increased modification of the Pc periphery with strongly electron withdrawing groups lowers the energy of the molecular frontier orbitals; increasing the chemical stability of the Pc. Open d-shell metal centers also introduce several partially occupied states near the top of the Pc valence band, which have electron density localized on the metal center. The bulky groups on the periphery of the Pc also act to mitigate molecular aggregation. To access the degree of aggregation as a function of peripheral modification, a molecular dynamics forcefield within the CHARMM parameterization model was developed specific to these Pcs. This also allows for the simulation of bulk and thin film properties important to various application areas. Finally, we propose a completely solid state dye sensitized solar cell (DSSC) design in which these chemically robust modified Pcs are sandwiched between n-TiO2 and p-NiO, acting as both photosensitizer and electron shuttle. Through analysis of the electronic structure of the Pc|semiconductor systems, the free energy associated with hole injection into the valence band of NiO upon photoexcitation of the sensitizer and electron injection into the conduction band of TiO2 from the reduced form of the Pc are calculated. Significant molecular orbital coupling between the Pc and semiconductors results in estimated charge transfer lifetimes on the femtosecond time scale on both NiO and TiO2. Additionally, the calculated excited state lifetimes of the Pc is found to be on the nanoseconds time scale, allowing ample time for charge transfer prior to the spontaneous relaxation of the Pc excited state. In the absence of a liquid electrolyte solution, the Pc molecule will need to also act as electron shuttle in our cell design. The charge transfer properties within the Marcus-Hush electron transfer theoretical framework are calculated. Results indicate that intermediate modification of the Pc periphery leads to high hole and electron mobilities. This is a promising result for our proposed DSSC design, but also makes these Pcs a viable semiconducting material in other application areas, such as light emitting diodes (LEDs) or organic field effect transistors (OFETs).

The Scanning Tunneling Microscope (STM) is well known as one of the best spatially resolved microscopes today. Due not only to its microscopic ability, but also to its capability to manipulate individual atoms or molecules, it has been used as a very powerful tool in furthering the miniaturization of nanodevices. In my thesis, molybdenum difulfide(MoS2) and its derivatives on Cu(111) at low temperature in ultra-high vacuum(UHV) conditions are systematically investigated using a home-built STM. By slightly changing the preparation methods, we can synthesize MoS2 monolayer, Mo2S3 monolayer or Mo6S6 nanowires. Density Functional Theory (DFT) calculations were also performed to support and interpret experimental results.

Organic solar cells have gained much attention as an inexpensive alternative to traditional inorganic cells. While experimental efforts have steadily improved the efficiency of organic devices, a large portion of the improvements have been the result of trial-and-error. Therefore, it would be ideal to be able to use theory to predict which types of materials would produce the most efficient devices. This dissertation presents a series of theoretical studies designed to improve understanding of what makes certain solar cells perform well and to serve as a predictive tool to screen potential new materials.First, a study of electron transfer in pentacene dimers is presented. The study compares several methods for calculating the electronic transfer integral, including time-dependent density function theory, a time dependent semi-empirical method, and several static calculations. The results demonstrate that at large separations, static calculations can underestimate the strength of coupling. Next, electronic coupling in fullerenes is calculated. In this section, a method for mimicking bulk chemical environments in film through the use of solvation and application of electric fields is developed. The method is a applied to a number of fullerenes used in organic solar cells, and compared with experimental data on local and bulk electron mobilities. Comparing the theory and experiment allows one to identify beneficial self-assembly behavior in the fullerenes studied. This method is then extended to a calculation method we have termed direct delocalization. In this method, a field is applied directly to the Fock matrix in order to delocalize frontier orbitals across a dimer. Once this is accomplished, electronic transfer time is calculated within the standard Marcus theory framework. The results are compared to the more thorough methods described above, and found to be in agreement.Next, the formulation of a stochastic approach to the GW approximation is presented. In this section, a method for calculating the polarization self-energy with stochastic orbitals is introduced. The method is highly efficient, achieving near linear scaling with respect to system size, compared with the theoretical fourth order scaling. The method is applied to large silicon clusters and several fullerenes to accurately calculate quasiparticle energies. Finally, in the last two chapters, several methods for studying plasmons are presented. The first presents a method for studying the interaction between molecules and plasmonic materials. The method interfaces a semiempirical quantum mechanical calculation (to study the molecules) with a finite- ifference time-domain (FDTD) calculation (to study the plasmonic material). The study shows that plasmon propagation can be heavily influenced by the presence of a molecule. In the last section, an alternative FDTD method is presented. This method, labeled near-field, is a time-dependent version of the quasistatic frequency-dependent Poisson algorithm. This approach is advantageous in that it allows for much large time steps in the propagation, greatly expediting the calculation.

The potential energy surfaces (PES) of 28 simple rare-gas (Rg)-linear molecule van der Waals (vdW) complexes were calculated using four dispersion-corrected density functionals (HFPBE-XDM, PW86PBE-XDM, a PW86PBE-XDM hybrid functional, and B97-D) and compared with accurate coupled cluster CCSD(T) reference data. In this benchmark study, the quality of the PES are assessed based on the values of the binding energies (BEs), and the geometry at the global minimum and the overall anisotropies and shapes of the PES. All functionals perform adequately on Rg-H2, Rg-N2, Rg-CO, Rg-OCS, and Rg-CO2 dimers. The functionals display various problems for the remaining vdW molecules. In particular, B97-D predicts overbound global minima and incorrect geometries for He,Ne,Ar,Kr-HCN and Ne,Ar,Kr-C2H2, PW86PBE-XDM and the hybrid functional massively overbind Rg-HF, and HFPBE-XDM predicts different global minima than the reference PES for Rg-HCl and Rg-HBr complexes. The error trends of the functionals are assessed relative to the size of the Rg atoms and the polarity of the linear molecules. Based on this assessment, we find that delocalization error affects the performance of the functionals and depends on the polarity of the linear molecule. The hybrid functional, which we denote as PW1PBE-XDM, provides lower error statistics than other functionals, which leads to a possibility that this functional can be applied to study other classes of vdW molecules in future PES studies.

We discuss the development and application of a number of theoretical physical models focused on improving our understanding of quantum chemical phenomena in condensed phase environments, especially aqueous solutions. The large number of atoms and molecules present in such systems precludes the application of the most advanced and accurate quantum chemistry theories available due to their exponential growth of required computational power with respect to the number of electrons in a system. As a feasible alternative, we opt to take a ``multi-layer" approach, wherein the full chemical system is partitioned into different layers treated with varying levels of approximation, circumventing the exponential scaling computational cost. How this partitioning is performed and applied appropriately is the principal emphasis of this work.Our main chemical system of interest is aqueous DNA and its excited electronic states. We examine applications of mixing quantum mechanics and classical molecular mechanics models, a multi-layer approach known as ``QM/MM," to simulate the electronic absorption spectrum of aqueous uracil as computed with Time-Dependent Density Functional Theory (TDDFT). We encounter a major issue of spurious charge-transfer (CT) states in TDDFT even at small uracil--water clusters. Applying Long-Range Corrected TDDFT (LRC-TDDFT), however, alleviates this issue and allows us to investigate the absorption spectrum of aqueous DNA systems of up to as much as 8 nucleobases, providing some important clues to the initial dynamics of aqueous DNA excited by ultraviolet light and its possible ensuing damage. Then, to overcome certain computational limitations in modeling solvent by QM/MM alone, we turn to the methodology of polarizable continuum models (PCMs), which can be added on top of the QM/MM multi-layer approach as an ``implicit" solvent model (in the sense that the average solvent charge density is approximated as a dielectric medium). We find that several extant PCM techniques are prone to numerical instabilities and discontinuous potential energy surfaces, and we propose ways to overcome such. Furthermore, we develop insights into the theory of PCM that yield an entirely new PCM for modeling the electrostatic effects of salty solutions. The culmination of our efforts is a cutting-edge QM/MM/PCM multi-layer approach for modeling quantum chemistry in the condensed phase.

Abstract Detailed in this manuscript is a methodology to model ground state properties of 4He droplets at zero pressure and zero Kelvin using a density functional theory of liquid helium. The density functional approach examined here consists of two noted functionals from the literature and corresponding mean field definitions. A mean field and trial density are defined for each system and optimized to self-consistency using a matrix diagonalization technique. Initial calculations of planar slabs are performed and demonstrate reasonable agreement with experiment and with prior studies using density functional theory. Quantum properties of droplets and droplets containing atomic dopants are calculated. Three different He-dopant potentials are examined to test the limits of the functional methods. For each impurity interaction, an average of 12 atoms were found to reside in the first solvation shell with an atomic dopant placed at the droplet center. Maximum densities in the first solvation shell reached those of solid helium as predicted by DF methods.