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Selected configuration interaction plus perturbation theory (SCI+PT) methods are an importantclass of electronic structure theory methods. By taking advantage of the fact that typically only a tiny fraction of the Hilbert space contributes significantly to the ground and low-lying excited states in \textit{ab initio} quantum chemistry calculations, these methods can provide high-precision solutions for both single- and multi-reference systems for which exact diagonalization, or full configuration interaction (FCI), becomes prohibitively expensive. In this dissertation, I present my work on increasing the scalability of SCI+PT methods to larger system sizes. I focus on a particular version of such methods, the semistochastic heat-bath configuration interaction (SHCI) method developed in our research group, and address two aspects that limit the method's scalability. First, I show how orbital optimization can be used to produce more compact variational wavefunctions in order to reduce the memory usage of SCI calculations. Here, the orbital optimization problem is framed as the simultaneous optimization in two subspaces: configuration interaction coefficients and orbital rotation parameters. Starting from natural orbitals, I present three classes of optimization methods based on how they treat coupling between these subspaces, namely uncoupled, fully coupled, and quasi-fully coupled methods. I show that taking the coupling into account is crucial for fast convergence and recommend two quasi-fully coupled methods: accelerated diagonal Newton, and Broyden–Fletcher–Goldfarb–Shanno with block matrix inversion. Second, I show how density-based basis-set corrections can be used to accelerate the slowconvergence of SHCI energies with respect to basis size, a problem plaguing all accurate wave function theory methods operating in finite basis sets. These correction methods map the divergent Coulomb interaction in a finite basis set to a non-divergent effective interaction in the infinite basis limit and use density functionals from range-separated density functional theory to recover the dominant part of the short-range correlation effects missing from finite basis wave functions. Two schemes are used in this study, differing in the functional used. They provide basis-set corrections that can be added \textit{a posteriori} to SHCI energies. Finally, I perform benchmark studies on two suites of realistic systems: 1) the Gaussian-2 set of55 first- and second-row molecules, and 2) seven transition metal atoms and their corresponding ions and monoxide molecules. The methodological improvements discussed in this dissertation help produce highly accurate results for these benchmark systems.

Self-assembled monolayers (SAMs) of alkanethiol molecules have been widely studied over the last three decades because of their diverse applications in the biomedical, nanotechnology, surface science, and electronics. It is also regarded as the model system to study the binding of organic molecules on the metal surfaces via thiol functional group. The robustness of the SAM structure combined with the ease of preparation makes it an ideal candidate for both fundamental and applied research. The structure of the alkanethiol SAMs on the Au(111) substrate is determined by the interplay between the alkyl chain packing and the interaction at the Au-S interface. Although our understanding of the SAM structure has significantly advanced over the last 35 years, an unambiguous atomic description of the Au-S interface and its influence on the chain packing remains elusive. In order to have better control of the SAM structure for different applications, we require a better understanding of the alkanethiol monolayer. In this work, we use a reductionist approach to determine the preferred head group positions driven by the chain packing, and by the interaction at the Au-S interface, separately. We use molecular dynamics (MD) to study the chain packing of the dense phase saturation coverage decanethiol SAM, and density functional theory (DFT) to study the interaction at the Au-S interface using an isolated methanethiol adsorbate. Alkane chains prefer a close-packed structure for the efficient interlocking of the methylene groups that minimizes the energy of the system. The molecular plane adjusts its orientation (molecular twist) depending on the spacing and the symmetry of the head groups to achieve a close packing of the chains. We first constrain the head groups at the high symmetry (√3 × √3)R30° sites to study the preferred combination of the molecular twists. We use this result as our baseline to study the effect of chain packing on the head group offset from the (√3 × √3)R30° sites. The position of the head groups also depends on the interaction at the Au-S interface. The preferred sites are determined by the tetrahedral coordination and the sp3 hybridization of the sulfur head groups. Relaxation and reconstruction (involve adatoms and/or surface vacancies) of the substrate also has significant influence on the preferred adsorption sites. We begin by determining the preferred positions of the head group on the unrelaxed substrate driven by the interaction at the Au-S interface alone. We then use the unrelaxed substrate as our reference to study the effect of substrate relaxation on the head group positions. To simulate a realistic model of the technologically interesting long-chain dense-phase alkanethiol SAM, we need to combine the effect of the chain packing and the interaction at the Au-S interface. Currently, we do not have a site-dependent force field for the Au-S interface to simulate the SAM structure using MD. On the other hand, the size of the problem is computationally too large for the DFT method. We demonstrated an approach to bridge the computational gap by using the atomic structure at the Au-S interface predicted by the DFT to study the effect on the chain packing. Using our DFT results, we predicted the symmetry of the adsorption site dependent dihedral force fields that can be used in MD to improve the prediction of the SAM structure.

Low-dimensional materials including carbon nanotubes (CNTs), graphene, transition metal carbides and nitrides (MXenes), transition metal dichalcogenides (TMDs) and beyond have recently become the focus of intense research because of their outstanding physical and chemical properties. Amongst those characteristics, the mechanical properties of the low-dimensional systems play a significant role in exploring their potential applications in industrial sectors. Nevertheless, the mechanical characteristics of the low-dimensional material systems have not yet been fully understood. This is because direct experimental measurements of mechanical properties of these materials face great difficulties due to the critical availability of the high-quality crystals and sophisticated facilities. To this end, the numerical methods become a promising alternative for investigating such properties of the low-dimensional materials. Firstly, this thesis presents the background and objectives of this project (Chapter 1). The recent progresses in numerically exploring the in-plane mechanical properties of low-dimensional material systems, including first-principles density functional theory (DFT), force-field based classical molecular dynamics (MD), and the finite element method (FEM) are then discussed. Some recent cases have been discussed to show the advantages and disadvantages of these multiscale simulation methods (Chapter 2). Since carbon-based materials are considered to be one of the most important family of low-dimensional materials, a comprehensive numerical study was conducted to investigate the mechanical properties (e.g. critical buckling load and vibrational properties) of carbon-based structures, such as carbon nanotubes (CNTs) and their modifications. The FEM was employed because it is able to analyse the systems consisting thousands of atoms (Chapter 3), and investigate macroscopic mechanical properties. Afterward, the mechanical properties of molybdenum disulfides (MoS2), as one of the most important TMDs, were examined by means of DFT calculations. The impacts of the structural polytypes, as well as external pressure on the mechanical response of the layer structured MoS2 were also investigated (Chapter 4). In addition, a comprehensive study was conducted to explore the energy-dependent anisotropic mechanical properties of different types of bulk TMDs using the first principles DFT calculations. Different analyses such as Density of States (DOS) and crystal orbital Hamilton population (COHP) were performed to fully understand the behaviour of the layer-structured systems (Chapter 5). Investigating the in-plane mechanical properties of monolayer two-dimensional (2D) materials was also one of the main objectives of this research. Hence, the mechanical characteristics of the lateral TMDs were thoroughly examined with consideration of the impact of the heterostructure configurations on the stability of the 2D systems (Chapter 6). Finally, the recently discovered MXenes were numerically investigated, and the mechanical properties of the functionalized systems were systematically explored. The DFT results reveals that the studied MXenes exhibit significant mechanical strength, which in-plane Young’s moduli are even larger than that of graphene. It may open the avenue for promising applications of such novel 2D materials (Chapter 7). It is worthy to note that the studies on the mechanical properties of 2D materials are still in early stages. As such, it is vital that future studies focus on exploring plausible numerical approaches for engineering the mechanical characteristics and eventually expanding the application of the low-dimensional materials.

Accelerating materials design & discovery will be critical to reducing society's dependence on fossil fuel-based energy sources. Computational methods such as density-functional theory, high-throughput workflow automation, and machine learning help to enable the understanding, discovery, and optimization of functional energy materials. I will demonstrate how these broad classes of tools may be applied to various important problems in the computational study of catalysts in three case studies. First, I will explain how computational probes of mechanical stability and catalytic suitability can be used to discover new forms of two-dimensional CO2 reduction photoelectrocatalysts. Second, I will show how random forests combined with feature engineering can be used to automate materials characterization efforts from X-ray absorption spectroscopy and discover new interpretable fingerprints in transition metal oxide K-edge spectra. Finally, I will detail ongoing efforts in developing a benchmarking dataset which can be used to probe the sensitivity of varying levels of theory for machine learning based interatomic potentials, which may then be used to study the stability of transition metal catalysts on experimentally relevant timescales.

The ABO3 perovskite oxide system is known to exhibit many technologically relevant materials properties, including ferroelectricity, ferromagnetism and antiferromagnitism, ferroelasticity, colossal magnetoresistance, and ultra-low thermal conductivity. Additionally, the wide choice of candidates for A and B, and extensive development of successful strain-engineering methods through epitaxial growth, provides a large design space through which these properties can be enhanced, suppressed, or controlled. In this dissertation, I explore the thermal and elastic properties of perovskite oxides, primarily ferroelectric PbTiO3, using theory and first-principles computation. In Chapter 1, I outline many the basic theoretical definitions techniques used throughout the text, covering thermal expansion, the theory of phonons, and density functional theory. In Chapter 2, I use first-principles theory to show that the ingredients assumed to be essential to the occurrence of negative thermal expansion (NTE) – rigid unit phonon modes with negative Grüneisen parameters– are neither sufficient nor necessary for a material to undergo volumetric NTE. Instead, I find that NTE in PbTiO3 involves a delicate interplay between the phonon properties of a material (Grüneisen parameters) and its anisotropic elasticity. These unique insights open new avenues in our fundamental understanding of the thermal properties of materials, and in the search for NTE in new materials classes. In Chapter 3, I explore thermal expansion behavior further. While it has certainly been recognized that mismatch in the thermal expansion coefficients of the bulk and substrate material will contribute to the misfit strain, the significance of this contribution for ferroelectric perovskite thin-films has not been systematically explored. I use first-principles density functional theory and the example of ferroelectric PbTiO3 thin-films on various substrates to show that ignoring the thermal expansion of the substrate (that is, assuming that the in-plane lattice parameter of the film remains roughly constant as a function of temperature) results in ferroelectric transition temperatures and structural trends that are completely qualitatively different from calculations in which thermal expansion mismatch is properly taken into account. This work suggests that the concept of a misfit strain defined as a single number is particularly ill-defined for PbTiO3 and invites further study of the interplay between thermal expansion mismatch and structural and functional properties in other thin-filmmaterials. In Chapter 4, I build off this work by using the Grüneisen theory of thermal expansion in combination with density functional calculations and the quasiharmonic approximation to uncover mechanisms of thermal expansion in PbTiO3 thin-films in terms of elastic and vibrational contributions to the free energy. Surprisingly, I find that although the structural parameters of PbTiO3 thin-films evolve with temperature as if they are dominated by linear elasticity, PbTiO3 thin-films are strongly anharmonic, with large changes in the elastic constants and Grüneisen parameters with both misfit strain and temperature. I show that a fortuitous near-cancellation between different types of anharmonicity gives rise to the behavior. My results illustrate the importance of high-order phonon-strain anharmonicity in determining the temperature-dependent structural parameters of PbTiO3 thin-films, and highlight the complex manner in which thermal expansion, misfit strain and elastic and vibrational properties are intertwined. In Chapter 5, I attempt to explore the chemical origins of the materials properties that play a role in the previous chapters. While DFT can be used to calculate what values these properties take in a given material, it does not tell us the origins of those properties in terms of chemistry and bonding, the language we use to both explain the driving physics behind existing materials properties, as well as to synthesize new materials with desired properties. Even for “routine” calculations of, for example, elastic properties or vibrational phonon frequencies, translating the quantitative results of a simulation into physical insights or design rules for enhancing or adjusting those properties remains challenging. Here, I discuss a new computational technique I have developed to rigorously relate the elastic, vibrational, and phase behavior of materials to specific chemical bonds in the crystal. The goal of this project is to gain chemical intuition with respect to controlling macroscale material properties. For example, if each bond in a crystal could be rigorously and sensibly assigned a portion of the total bulk modulus, such that the total stiffness of the system could be expressed as a sum over bonds, then we can understand how each bond is contributing to the macroscale behavior under hydrostatic stress, as well as develop an under- standing as to why that compressibility would evolve given changes in structure, pressure, or through chemical substitution. I have implemented a proof- of-concept of this method in software, building off of the open-source Quantum Espresso and Wannier90 projects. Finally, in Chapter 6, I discuss current directions and future work based to further explore and build off of the the concepts I have established in this dissertation.

Polymeric facilitated transport membranes (FTMs) incorporate reactive carriers (e.g., amino groups) in the polymeric matrix and uniquely utilize the carrier–CO2 reaction to enhance the CO2 permeation. Consequently, FTMs offer both high CO2 permeance and large CO2/N2 selectivity. In this study, a computational methodology was developed to elucidate how carriers influence the intricate interplay of underlying physical and chemical processes that determine the separation performance of FTMs.Commonly adopted amine carriers, including the poly(N-vinylformamide-co-vinylamine) (PNVF-co-VAm) fixed-site carrier and piperazine glycinate (PZ-Gly) and 2-(1-piperazinyl)ethylamine sarcosinate (PZEA-Sar) mobile carriers, were first studied. By using density functional theory (DFT) calculations, molecular dynamics (MD) simulations, and Monte Carlo methods, the carriers were evaluated from the perspectives of reaction chemistry, guest molecule (i.e., mobile carrier, CO2, and N2) diffusion, and N2 sorption. The computational observations were found in good agreement with experimental results, validating the reliability of the computational methodology.In order to improve the CO2 loading capacity, sterically hindered amine carriers were designed and shown to offer improved reaction chemistry. A new parameter of steric hindrance, N exposure, was proposed and validated to facilitate future design of carriers. Another class of reactive carriers with strong basicity have also been evaluated and demonstrated desirable reaction chemistry. These carriers are therefore expected to offer higher CO2 loading capacities than the amine carriers.The diffusion and N2 sorption in promising mobile carriers were then investigated using MD and Monte Carlo simulations, respectively. Sterically hindered amine mobile carriers typically demonstrated lower mobile carrier and CO2 diffusivities. Other candidates were found to offer fast guest molecule diffusion but suffered from larger N2 permeabilities.The water uptake of the FTM was identified to strongly influence gas permeation. Therefore, the computational prediction of water sorption in FTMs was also explored. For the first time to our knowledge, the equilibrium water uptake values of FTMs with PNVF-co-VAm and PZEA-Sar mobile carrier were computationally predicted and found in good agreement with experimental results. This is achieved using the chemical potential of adsorbed water calculated with the continuous fractional component Monte Carlo (CFCMC) method. The higher PZEA-Sar content in the FTM was shown to increase the water uptake value, which promoted the diffusion of CO2 and suppressed N2 sorption. The water sorption behavior, guest diffusion, and N2 sorption in FTMs with sterically hindered amines mobile carriers were subsequently discussed.The proposed computation methodology paves the way toward the future screening studies and design of better-performing carriers in FTMs. The insights generated herein can help navigate future experimental efforts.

We have developed a dynamical density functional method based on the time-dependent projection operator technique and local equilibrium approximation. We used the method to predict density profile for three types of non-isothermal fluids: hard spheres, repulsive Lennard-Jones and truncated and shifted Lennard-Jones. The accuracy of the predictions is examined by comparison with results from reverse non-equilibrium molecular dynamics simulations. Results show that our method is good at predicting the density profile for these fluid systems in even highly non-isothermal conditions in single-phase cases, and can be at least qualitatively correct for non-isothermal vapor-liquid coexistence systems.

Historically, fossil fuels have dominated the energy generation domain and it is projected that they will continue to remain a dominant source of energy for the foreseeable future. Although essential, the usage of fossil fuels has deemed to be deleterious for both human health and the surrounding environment as the release of anthropogenic CO2 because of burning fossil fuels has been asserted as one of the leading causes of global warming and climate change. It is important to understand that fossil fuels are not only used for energy generation, but they also contribute significantly towards the production of various industrially important chemicals and derivatives. Syngas and hydrogen, two of the petrochemical industry’s most sought-after commodities are typically generated using fossil fuels, wherein both gaseous and solid fossil fuels can be employed for syngas and hydrogen generation. Naturally occurring carbonaceous sources (fossil fuels) such as natural gas, shale gas, coal, etc. are reformed/gasified in the presence of steam/CO2/molecular O2 to generate syngas, which is processed further for generating hydrogen. These processes however collectively suffer from certain limitations including high endothermic heat requirement, placement of amine-based units for capturing CO2 generated across various operations in the process, catalyst deactivation due to coking, and the requirement of a highly energy and cost-intensive cryogenic air separation unit. Consequently, new technologies are being explored that can facilitate syngas/hydrogen generation in an efficient and clean manner such that the parasitic energy and cost of syngas generated per unit carbon is reduced. Chemical looping is one such technology platform that has the potential to replace the conventional syngas generating processes. The chemical looping process utilizes metal oxide-based oxygen carriers that react with carbonaceous feedstocks at elevated temperatures to carry out their partial oxidation for syngas generation through the transfer of lattice oxygen. The lattice oxygen lost in this step is made available to the reduced oxygen carriers using suitable oxidizing agents such as steam, CO2, or air, where the selection of the oxidizing agent is contingent to the desired process.The work presented here aims to use the chemical looping technological platform for generating syngas using calcium-iron based oxygen carriers. When mixed in stochiometric proportions, the oxides of Ca and Fe tend to form a unique brownmillerite phase, which has exceptional thermodynamic selectivity for syngas production coupled with the occurrence of sufficient oxygen vacancies that promote rapid rates of ionic diffusion for enhanced reaction kinetics. These oxygen carriers also exhibit another unique trait as they tend to re-oxidize fully in the presence of CO2 or steam while generating CO and H2, respectively. The effect of adding MgO as an inert support on the reactivity of Ca2Fe2O5 has been ascertained while also investigating the regeneration performance in the presence of CO2. A detailed set of density functional theory calculations has been presented that investigates the reaction mechanism pathway and compares the activation energy barrier for syngas formation in Ca2Fe2O5 and pure Fe2O3. Extensive thermodynamic simulations have been conducted to carry out a systematic modeling study for various reactors such as reducer, oxidizer, and combustor associated with the methane chemical looping reforming system. Both isothermal and adiabatic modes of operation have been investigated under a wide range of operating pressures ranging from 1 to 30 bar. Comparison has been drawn between the various proposed chemical looping schemes and the industrial standard of autothermal reforming for generating syngas. The use of pure Ca2Fe2O5 for generating syngas using the chemical looping route is limited by its high endothermic heat requirement. To overcome this limitation, modification of the oxygen carrier has been envisioned through the addition of transition metal oxides (NiO, CuO, and Co3O4). The effect of adding varying concentrations of these metal oxides to Ca2Fe2O5 on process parameters such as a methane conversion, syngas selectivity, and endothermic heat requirement has been assessed through computational simulations carried out using thermochemical software. As already established, Ca2Fe2O5 possesses the capability to generate high quality syngas and get re-oxidized in CO2 to generate CO. Apart from this, it possesses another trait wherein any sulfur (typically found in solid fuels such as coal) entering the reactor can be selectively captured and removed using CaO present in the carrier. Using this principle, a process scheme has been devised for producing acetic acid from petcoke where syngas is generated in one reactor for methanol synthesis and CO along with H2S is produced in others , wherein methanol and CO further react with one another catalytically to produce acetic acid. The effect of solid circulation rates on syngas quality, parasitic energy requirement, and the sulfur capture capability has been studied using process simulations. Also, a comparison has been made for acetic acid generation using the proposed process and syngas generated through the conventional petcoke gasification route. One of the challenges of operating the chemical looping system for syngas generation from methane in adiabatic mode is the drop in temperature along the length of the reducer reactor in the absence of external heat source. As a result, methane conversion is high near the reactor top, but it decreases substantially as the temperature drops. To overcome this limitation, the Ca2Fe2O5 carrier is doped with Ni, where this doping is found to create oxygen vacancies that lower the energy barrier for methane adsorption and enable its rapid conversion to syngas. A detailed density functional theory- combined experimental approach has been utilized to study the effect of Ni doping on methane reactivity as well as the tendency of the carrier to be regenerated fully in CO2.The work done as a part of this dissertation provides a holistic view of syngas generation using the Ca-Fe oxygen carriers, such that it provides a detailed account of the various aspects of technology development to propel the proposed chemical looping scheme towards commercialization pathway as a cost and energy efficient alternative to the state-of-the-art technologies already in practice. The work here covers a wide range of aspects for chemical looping process development that includes design and development of the oxygen carrier, understanding the reaction thermodynamics, and extensive process modeling studies for obtaining the optimum operating conditions, investigating the reaction kinetics and syngas generation performance through thermogravimetric and fixed bed analysis, and lastly, conducting density functional theory calculations to gain a mechanistic insight into the underlying reaction chemistry.

From long-hauling vehicles operating in marine environments to reusable rockets aiming to take civilization beyond earth, from oil and gas pipelines transporting fossil fuels to offshore wind turbines and solar panels generating renewable energy, from chemical refinery operations to long-term storage of nuclear waste in underground cannisters, corrosion is one of the common denominators that play a critical role in the operational safety and performance limitations of the respective industries. Practicing corrosion control to minimize material degradation to within a desired safety threshold and a well-understood controllable margin that can be readily managed relies on mechanistic understanding of the science behind corrosion and the application of that scientific understanding to engineer solutions. Through decades of experimental investigations, empirical knowledge has accumulated, corrosion mechanisms proposed, and mitigation strategies practiced by the corrosion scientists and engineers. In the era of Integrated Computational Materials Engineering, the emergence of the first principles approach, as formulated in the density functional theory, provides an atomic level “virtual microscope” for probing corrosion mechanisms that occur at the interfaces that terminate materials and initiate interactions with environment. Applying DFT to investigate corrosion science presents unique challenges and opportunities. A multi-physics framework must be developed to systematically address these challenges. The frameworks proposed in this thesis require the electronic work functions and the species adsorption energies as inputs, which are readily computable in the state-of-the-art DFT code. The purpose of the framework is to bridge the electrochemical properties of alloys at the interface to the macroscale observable parameters measured in a laboratory setting. Two classes of alloys will serve as the framework testing ground: precipitation hardened aluminum alloys; and Ni-Cr-Mo corrosion resistant alloys. Specifically, in the former case of aluminum alloys, the kinetic framework predicts the corrosion potentials and corrosion current density for the commonly identified intermetallic particles and its aluminum matrix. In the latter case of Ni-Cr-Mo corrosion resistant alloys, the thermodynamic framework predicts the surface coverage of adsorbed species as a function of potential, pH, and chloride concentration. From which, a series of single-valued metrics assessing different aspects of corrosion susceptibility are derived. It is shown that this susceptibility indices can be reliably used to screen for alloying elements and optimize alloy composition with respect to corrosion resistance.

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).

Although band theory is about a century old, it remains relevant today as a tool for the treatment of electrons in solids. The confluence of mathematical ideas like geometry and topology with band theory has proven to be a ripe avenue for research in the past few decades. The importance of Fermi surface geometry, especially in conjunction with electronic correlation, has been well recognized. One particular thread in this direction is probing the occurrence of non-trivial Fermi surface geometry, and its influence on macroscopic properties of materials. A notable example of exotic Fermi surface geometry arises from singular points of the dispersion, and these have been known since 1953. The investigation into these was reignited recently, culminating in the work presented in this thesis. In this dissertation, I investigate two broad categories of singular points in bands. At a singular point, either the dispersion or the Fermi surface fail to be smooth. This may cause distinct signatures in transport and spectroscopic properties when the singular point occurs close to the Fermi level. In the two dimensional setting, I classify using catastrophe theory, the point singularities arising from higher order saddles of the dispersion. These are the more exclusive cousins of the regular van Hove saddle that cause, among other things, a power law divergence in the density of states. The role of lattice symmetries in aiding or preventing the occurrence of these singularities is also carefully explored. In the case of three dimensional bands, I investigate the spectroscopic properties of the nodal point singularity, arising from a linear band crossing. In particular, I determine the distinct signature of nodal points in the analytic, momentum resolved, joint density of states (JDOS) and the numerically calculated resonant inelastic x-ray scattering (RIXS) spectrum, within the fast collision approximation that ignores core hole effects. The results presented here will be the stepping stone towards a careful future calculation, incorporating the potential edge singularity effects through core hole potential. Such a calculation may be directly comparable with ongoing experiments.

A computationally efficient approach to perform systematically convergent real-space all-electron Kohn-Sham density functional theory calculations using an enriched finite element (FE) basis is presented. The enriched FE basis is constructed by augmenting the classical FE basis with atom-centered numerical basis functions, comprising of atomic solutions to the Kohn-Sham problem. Notably, to improve the conditioning, the enrichment functions are orthogonalized with respect to the classical FE basis, without sacrificing the locality of the resultant basis. In addition to improved conditioning, this orthogonalization procedure also renders the overlap matrix block-diagonal, greatly simplifying its inversion. Furthermore, the treatment of electrostatics adopted in this work ensures that the basis is seamlessly applicable to both periodic and non-periodic calculations. A Chebyshev polynomial based filtering technique is used to efficiently compute the occupied eigenspace in each self-consistent field iteration. The basis offers excellent parallel scalability with 92% efficiency at 22 times speedup for a system with 620 electrons. Additionally, a formulation to compute nuclear forces and the stress tensor needed to perform structural relaxation in this basis is also presented. This is done using the notion of configurational forces which arise from the variational derivative of the Kohn-Sham free energy functional with respect to the position of the material point. This approach, originally proposed in the context of classical FE by Motamarri and Gavini [Phys. Rev. B, 97 , 16 (2018)] , enables computation of variationally consistent nuclear forces and the stress tensor in a unified framework without having to explicitly account for Pulay contributions. In this work, we demonstrate the accuracy of the formulation by comparing the computed forces and stresses for various benchmark systems with those obtained from finite-differencing the ground-state energy. Moreover, our calculations are benchmarked against the Gaussian basis for isolated systems and LAPW+lo basis for periodic systems. Lastly, the applicability of the basis is extended to solve the time-dependent Kohn-Sham equations in real time. The formulation is based on a recent work by Kanungo and Gavini [Phys. Rev. B, 100 , 11 (2019)], wherein classical finite elements were used for spatial discretization. The presence of enrichment functions in the basis accelerates all-electron TDDFT calculations. Moreover, the block-diagonal nature of the overlap matrix afforded by the orthogonalized enriched finite element basis enables fast and convenient inversion of the overlap matrix that features in the evaluation of the discrete time-evolution operator. The formulation utilizes the second-order Magnus operator, which contains an exponential of a large sparse matrix, as the time propagator. The action of this operator on an orbital is efficiently performed by using a Krylov-subspace projection method. Calculations show close to optimal rates of convergence of the dipole moment with respect to spatial and temporal discretization. Overall, the work presents an efficient, scalable and accurate basis to perform all-electron DFT calculations. This would be instrumental in the study of defects in crystalline systems where calculations on large systems are inevitable. Moreover, the systematic improvability afforded by the basis makes it a suitable tool to conduct pseudopotential transferability studies.

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.

In this work, we present a comprehensive computational and theoretical study of the structural phase space of multiferroic Bi-Fe-O and the influence of the structure on functional properties. Complex oxides are known for their many different functionalities, including ferroelectricity, ferromagnetism, or piezoelectricity, which are relevant for technological applications. Multiferroicity, in which at least two different ferroic orders (ferromagnetism, ferroelectricity, and ferroelasticity) coexist and couple in a single phase is of particular interest and can enable, for example, control of magnetic properties using an applied electric field. BiFeO3 (BFO) is one of the most studied multiferroic materials because of the coexistence of magnetic and polar orders at room temperature promising for applications. The substantial intrinsic functionalities of complex oxides can be further enhanced by growing them as thin films in superlattices or heterostructures. In most cases identified to date, this is a result of the change in the material’s lattice constants caused by epitaxial strain imposed by the substrate. This change in lattice constants can modify the functionalities, for example causing a phase change to a higher polar state in BFO. In heterostructures of polar and non-polar materials, the polar discontinuity at the interface results in an accumulation of surface charges, which in turn create an electric field, known as the depolarising field. This de- polarising field can cause the formation of domains in the polar material and even change the orientation of the polarisation, in order to reduce the energy penalty of the interfacial charge. We start our study by considering the case in which a phase transition to a non-polar phase with low relative energy is preferred over polar domains formation and present a new phase with a larger unit cell size and surprising properties matching recent experimental results. We then explore the phase space of BFO using density functional theory (DFT) calculations and reveal several low-energy phases with large unit cells which could be stabilised by a polar dis- continuity at the interface. Finally, we consider a heterostructure in which the polar discontinuity is partially reduced by the presence of differently charged ionic layers and show that this causes an isosymmetric phase transition from two phases with the same symmetry but different polar states in highly-strained BFO. Next, we study a new candidate multiferroic material, bismuth hexaferrite, which exhibits net magnetic and possible ferroelectric moments at room temperature, experimentally demonstrated. We show that the net magnetic moment is likely related to the presence of Fe vacancies and compute the energy barrier between opposite orientations of the polarisation in order to evaluate the likelihood for it to be ferroelectric. This result paves the way for the exploration of new materials in the Bi-Fe-O compositional phase space with technologically relevant properties. Finally, we conclude our work by proposing an accelerated method combining DFT, irreducible phonon modes, and machine learning to explore the potential energy surface of BFO and we predict several low-energy structures, suggesting that the phase space of BFO is still far from being completely known. This thesis highlights the richness of the phase space of Bi-Fe-O and the importance of the boundary conditions provided by the heterostructure, in particular, the electrostatics at the interface in determining the properties of functional oxides. Finally, the methods that we developed provide a new accelerated approach to structure discovery. They are broadly applicable and can certainly lead to discoveries in other materials.

Hot carrier solar cell (HCSC) is a novel photovoltaic concept which has limiting energy conversion efficiency (ECE) over 65% at one sun and over 85% at the maximum sunlight concentration. This is more than double of the Shockley—Queisser (S-Q) limit for a single p-n junction solar cell. To achieve ECE beyond the S-Q limit, a hot carrier absorber (HCA) material is required that delivers hot carriers (HCs) before they are completely thermalised. In semiconductors, the light generated HCs thermalise within a few picoseconds which is very quick in terms of the carrier extraction time and hence the excess thermal energy of the HCs is lost in the standard p-n junction solar cells. Longer HC lifetime is, therefore, critical for a HCA material to utilise the excess energy thereby minimizing the thermalisation loss. One of the main routes for achieving this is suppression of the rate of HC energy loss by optical phonons (OPs), therein creating a hot OP bottleneck which can re-heat the carrier population. In this thesis, nitrides and oxynitrides of Hf and Zr have been investigated as potential HCA materials. The density functional theory (DFT) calculations of electronic, optical and phononic properties have been supplemented by experimental synthesis of over-stoichiometric HfNx and HfOxNy thin films using atomic layer deposition. The DFT results show that the mononitrides (MN, M = Hf, Zr) are metals and have a wide phonon band gap which is desired for a potential HCA material because this can cause suppression of anharmonic scattering rates thereby securing a long HC lifetime. The nitrogen-rich phases (c-M3N4) are semiconductors and don’t have a large enough phononic gap to effectively suppress the anharmonic scattering events, therefore they may not be suitable as a HCA material. However, the metal/semiconductor heterostructure of HfN/c-Zr3N4 and ZrN/c-Hf3N4 have potential for the HCSC and thermoelectricity due to the phonon confinement effect. The HC dynamics in HfNx thin film have been investigated by transient absorption (TA) spectroscopy which estimated the HC lifetime of about ~ 50 ps and a lower sound speed of ~ 3700 m/s. These results are encouraging for the HCA and thermoelectric applications; however, a further quality improvement is necessitated to reduce the O and C impurities in the HfNx thin film. In future, focus should be given on simulation of a mixed HfN and Hf3N4 phases that would reproduce a close to real HfNx thin film. The oxynitrides (MOxNy) are insulators with a large electronic band gap and have no phononic band gap. Therefore, they cannot be used as a bulk HCA material, however, they can be used as energy selective contact material and the collector material in the other variants of the HCSC concept.

Metal-oxo clusters are discrete molecular metal-oxides that are important for applications in catalysis, medicine, precursors for thin film deposition, flash memory devices, switchable transparent films, and high-resolution nanopatterning, to name a few. There are many factors that control the solution stability of metal-oxo clusters; the internal chemistry of the coordination complex as well as the solution conditions can influence the thermodynamic stability of the species. Currently, there is a lack of understanding surrounding how to best manipulate these factors in order to achieve a specific isomer, geometry, or size of metal-oxo cluster in solution. Therefore, the work presented in this dissertation contains an evaluation of the influence of heteroatoms, caps, ligands, pH, electrochemical potential, metal ion concentration, and counterions on stabilizing metal-oxo clusters. Quantum chemical computations were employed to model tin-based and zirconium-based clusters with varying factors, to determine their hydrolysis Gibbs free energies and electronic structures. Sn-oxo clusters, specifically of Keggin geometry, have been shown to be an excellent photoresist candidate for next-generation extreme ultraviolet lithography manufacturing of advanced integrated circuits. I showed how changing the Sn-Keggin heteroatom, caps, and ligands impact the stability ordering of the five known Keggin isomers. This Sn-Keggin ion represents the only Keggin ion family thus far, that favors the lower-symmetry isomers. The computational results provide a means to isolate a single isomer to promote a pure and stable material for advanced lithography. Zr-oxo clusters, specifically the Zr hexamer, has been shown to be a good node for a metal- organic framework (MOF) which is a 3D porous network that can act as a nanoscale carrying device. However the ”green” manufacturing of Zr-based MOFs requires that the Zr hexamer stabilize in an aqueous solution without organic ligation, which has yet to be successfully accomplished. I examined how the pH, electrochemical potential, metal ion concentration, and counterions all influence the aqueous solution stability of Zr(IV) ions clusters and solids. This study successfully elucidates the magnitude of and reason for the thermodynamic instability of the hypothetical Zr hexamer compared to the ubiquitous Zr tetramer. The knowledge gained from these computational studies on Sn-oxo and Zr-oxo clusters can expedite experimental syntheses through implementation of the strategic tuning of chemistry and solution conditions.

This dissertation involves the study and manipulation of molecular orbitals on ambiphilic molecules to obtain unusual properties and develop new applications. Carbenes, which can simultaneously serve as powerful Brønsted bases and Lewis acids, undergo further amplification of those properties as the bond angle at the carbene center changes (chapter 2). Such profound reactivity fluctuations from seemingly simple geometric modifications highlight the need for accurate measurement of carbene frontier orbitals. However, X-ray and NMR studies show that carbene-selenium adducts are susceptible to H-Se non-classical hydrogen bonds which cause large downfield shifts in 77Se NMR, disrupting a commonly used technique for assessing carbene π-acidity (chapter 3). Furthermore, common techniques used to characterize carbene donor abilities are unable to avoid the confounding of carbene basic and acidic orbitals. To directly see how carbene lone pairs compare to each other, we ranked different carbene families by basicity through a proton exchange method (chapter 4). The basicity ranking highlights several surprising qualitative errors that occur throughout many theoretical calculations. Finally, exploring the reactivity of aminoboranes, a class of 1,2-ambiphiles, we developed a new methodology for the synthesis of aldimines (chapter 5). Preliminary mechanistic studies suggest that the mechanism is orthogonal to traditional condensation methods and allows access to products that are conventionally difficult to obtain. This dissertation will hopefully serve as another example of how we can advance main-group synthetic chemistry by taking a molecular orbital perspective.

Over the past few decades there have been tremendous advances in the accuracy of electronic structure calculations, rigorous methods to assess proposed reaction mechanisms, and techniques to create representative models of active sites for homogeneous and ordered heterogeneous catalysts. Advances on all these fronts have greatly improved our understanding of numerous catalysts. However, most of these methods assume that the active sites are both well-defined and uniform (or are in an ensemble of uniform sites). Catalysts with quenched disorder have non-uniform active sites. Most established modeling techniques are inapplicable and many important catalysts in this category remain poorly understood. The major challenges of modeling catalysts with quenched disorder are (i) the nature of the disorder is out-of-equilibrium and unknown; (ii) each active site has a different local environment and activity; (iii) active sites are rare, often less than ~20% of potential sites, depending on the catalyst and preparation method.In this thesis, we develop new methods to efficiently predict kinetic properties of the distribution of quenched-disordered sites and how they affect different steps of catalyst operation. We combine population balance modeling techniques with machine learning methods to learn how the population of grafted sites evolves in time. We develop a new algorithm to efficiently converge site-averaged kinetics for a given distribution of grafted sites, the Importance Learning algorithm. A combination of Importance sampling and machine Learning are used to efficiently and accurately learn activation parameters of rare sites that dominate site-averaged kinetics. Finally, we develop a protocol to create cluster models of active sites that most accurately preserve quenched degrees of freedom.

Hydrogen is an attractive carrier of renewable energy due to its natural abundance, high energy density, and lack of harmful byproducts when converted to electricity in a fuel cell. Solid-state hydrogen fuel cells are gaining particular attention for their stability, storage capacity, and ability to operate at high temperatures. However, their technological adoption requires highly conductive solid-state hydrogen electrolytes. In crystalline electrolytes, conductivity is tied to the concentration of mobile hydrogen defects, be they hydrogen interstitials or vacancies. Increasing the concentration of such defects improves the ionic conductivity. Understanding how to do so requires knowledge of the defect chemistry, including native point defects as well as extrinsic dopants and impurities.To this end, we examine a suite of prospective solid-state hydrogen electrolyte materials using accurate first-principles calculations based on density functional theory with a hybrid functional. These include proton-conducting oxides, such as the alkaline-earth zirconates (CaZrO3, SrZrO3, BaZrO3) and cerates (SrCeO3 and BaCeO3), as well as several hydride-ion conductors, namely, the alkaline-earth hydrides (CaH2, SrH2, and BaH2), La-Sr-Li-H-O oxyhydrides, and the nitride hydride Sr2LiH2N. We report on calculations of defect formation energies under conditions representative of experimental growth and operation.Our results suggest approaches for improving ionic conductivity through defect engineering. For instance, in the alkaline-earth zirconates and hydrides, we show that alkali metal dopants boost the concentration of mobile hydrogen defects, while also possessing low Coulombic binding energies to those defect species so as not to hinder their mobility. Native point defects and extrinsic impurities also affect these materials’ chemical stability. In the alkaline-earth cerates, carbon impurities hamper device performance and stability, and we show specifically why their impact is worse than in the zirconates. For the oxyhydrides and nitride hydride, the same defects that grant them high hydride conductivity provide a stabilizing effect that may permit synthesis.The work summarized here lays a foundation for the development of solid-state fuel cells and of hydrogen as a reliable energy source. These goals require the engineering of novel materials such as those described here; thus, our results provide essential knowledge to help power a sustainable energy future.

In this thesis we use first-principles density functional theory (DFT) and related methods to predict and understand the properties of two categories of materials with particular promise for technological applications: topological semimetals, and antiferromagnets whose magnetic order can be controlled by electrical current.Topological semimetals (TSMs), a subset of topological materials which are the particular focus of the first part of this thesis, have robust band crossings in reciprocal space protected by crystalline symmetries and characterized by mathematical invariants. They exhibit a variety of exotic phenomena such as ultrahigh mobility of electrons, giant magnetoresistance, and chiral anomalies. Moreover, analogously to the better known topological insulators (TIs), the ``bulk-boundary correspondence", related to the change in topological invariant in going from material to vacuum, implies the existence of electronic states localized at the compound surface which can differ significantly from the semimetallic bulk states in TSMs. The development of group-theoretical methods to identify TSMs and TIs have revealed that TSMs are far more ubiquitous than initially hypothesized; to date over $10000$ candidates have been identified. While this might seem to imply that the goal of harnessing properties of TSMs for practical purposes is a solved problem, most candidate materials have one or more features which make experimental manipulation and detection of the topological properties difficult if not impossible. If the symmetry-protected band crossings occur at energies far from the Fermi level, or if they are obscured by other trivial bands at the same energy, the topological signatures will be obscured. Thus, the identification of specific materials, structural motifs, and possible tuning parameters through which one can realize ``functional" TSMs is highly desirable.\\\indent The first section of this thesis describes a set of studies focused on the interplay of symmetry, orbital character and magnetism in yielding electronic structures with controllable TSM features near the Fermi level and free from interfering trivial bands. First, we use a combination of DFT and tight-binding to examine the electronic structure of a previously synthesized compound, $\mathrm{TiRhAs}$. We find that $\mathrm{TiRhAs}$ hosts a topological nodal line protected by a mirror plane nearly exactly at the Fermi level, with no other energetically degenerate trivial bands. Next, in combination with experimental ARPES data which confirmed our DFT findings, we investigate the transition metal dichalcogenide (TMD) $\mathrm{NiTe_2}$, and find that is a Dirac semimetal with a bulk tilted Dirac cone and topological surface states. While previous isostructural $\mathrm{MX_2}$ ($\mathrm{M}=\mathrm{Pd}$, $\mathrm{Pt}$: $\mathrm{X}=\mathrm{Te}$, $\mathrm{Se}$) compounds have been shown to host similar ``ladders" of topologically protected bulk and surface states, the features of interest occur at large binding energies which render their topological properties irrelevant to transport. We show that the increased hybridization between $\mathrm{Ni}$ $\mathrm{d}$ and $\mathrm{Te}$ $\mathrm{p}$ states as compared to the other $\mathrm{MX_2}$ compounds is responsible for tuning the Dirac cone very close to the Fermi level; thus substitution of the transition metal element is an effective method for designing functional TSMs within this class of TMDs. Finally, we examine the possibility of realizing TSM features in compounds isostructural to the multiferroic hexagonal manganites. This was motivated by the numerous order parameters in multiferroic compounds which can be controlled by external fields; thus, a multiferroic compound with TSM features in a particular phase would provide an opportunity to switch from nontrivial to trivial topology by tuning of the ferroic order parameters. We find through our DFT calculations that by enforcing a metastable ferromagnetic order in the nonpolar centrosymmetric phase, hexagonal $\mathrm{YCrO_3}$ and $\mathrm{YVO_3}$ become topological nodal line semimetals, in contrast to the insulating band structures that occur with the ground state antiferromagnetic order of the transition metal ions.\\ \indent The second section of this thesis focuses on first-principles characterization of ``functional materials" in which the feature to leverage for functionality is magnetic, rather than topological, order. We focus on antiferromagnetic (AFM) materials. There has been a recent surge of interest in using AFMs rather than their traditional ferromagnetic (FM) counterparts for spintronic devices whose magnetic order can be manipulated by an electrical current. The vanishing bulk magnetization of AFMs makes them particularly robust to magnetic field perturbations, and the limiting rate of spin dynamics (i.e. the rate at which spins can rotate) in AFMs is order $\sim \mathrm{THz}$ as opposed to $\sim \mathrm{GHz}$ for FMs.\\ \indent Our studies focus on one example, the iron-intercalated TMD, $\mathrm{Fe_{1/3}NbS_2}$. The triangular lattice of $\mathrm{Fe}$ ions has an antiferromagnetic (AFM) order which can be manipulated with electrical pulses of very low current density. While numerous experimental characterizations have been performed on this compound, ambiguities regarding the magnetic ground state, spin exchange constants, and the specifics of the current-induced magnetization dynamics, remain. In one study, we calculate the nearest-neighbor Heisenberg exchange constants in $\mathrm{Fe_{1/3}NbS_2}$ and find that competition between strong nearest-neighbor interplanar and intraplanar $\mathrm{Fe}$ exchange constants is responsible for an observed half-magnetization plateau. In the second part of our first-principles characterization of $\mathrm{Fe_{1/3}NbS_2}$, we explore the working hypothesis that the current-induced manipulation of AFM order, which is detected by changes in electrical resistance, is due to a repopulation of three energetically equivalent AFM domains on the triangular lattice. Based on calculated conductivity tensors within a constant relaxation time approximation with experimentally proposed AFM magnetic orders, we verify that the transport parallel to the $\mathrm{Fe}$ layers is anisotropic, a necessary condition for the domain repopulation hypothesis. Finally, by comparing our ab-initio transport with experimental changes in resistance for specific pulse directions, we infer the likely current-domain response for $\mathrm{Fe_{1/3}NbS_2}$, that is, which domains are favored for a given current direction.

This dissertation has two main objectives. The first objective of this dissertation is to emphasize the importance of synergistic interactions between experimentalists and theorists. Such work can lead to a holistic understanding of chemical processes yielding an understanding whose whole is greater than the sum of its parts. Chapters two and three describe new insights on hydrolysis reactions at TiO2 and ZrO2 done in close collaboration with experimental colleagues. Studies of such metal oxides are motivated by the critical role they play in chemical catalysis and a multitude of other high-impact applications. As discussed in these two chapters, the rich spectroscopic data attained by experimentalists studying these systems can only be interpreted after engaging theoretical simulation. Interestingly, such calculations often stretch the capabilities of readily available computational models and often motivate new methodological and theoretical advancements to explore excited state computational methods. Motivated by the titanium and zirconium studies, the second part of this dissertation evaluates a new tool for locating excited states at a ground state computational cost, which has been termed the Projection-based Maximum Overlap Method (PMOM) and Projection-based Initial Maximum Overlap Method (PIMOM). Chapter four introduces the PMOM and PIMOM methods and provides an initial demonstration of their applicability to various classes of excited electronic states. Chapter five extends the use of the model to the evaluation of excited state molecular properties, such as optimizing excited state minimum energy structures, evaluating adiabatic excitation energies, and calculating vibrational frequencies. Chapter six presents a case where PMOM and PIMOM successfully lead to simulations of the spectrum of methylene blue, while simulations using popular time-dependent density functional theory models seemingly fail to locate a spin-pure state. Chapter seven highlights the usage of PIMOM to explore exotic electronic excited states in Lanthanides. Specifically, this chapter discusses the photoelectron spectra of Gd2O-.The final chapter reviews the results of the dissertation. Examining the outcomes of that work, the final chapter also outlines potential new directions motivated by the reported research.

This thesis reports on incorporating various computational methods and tools, including first-principles calculations, mesoscale modeling, stochastic modeling, and Artificial intelli-gence to study structure-property relations in wide bandgap semiconductors. In the first project, we present first-principles calculations that elucidate the growth mecha-nism of ZnO photocatalytic materials for water-splitting applications used in sustainable energy storage. Polar surfaces of ZnO are known to have higher photoelectrochemical ac-tivity increasing water splitting efficiency. However, they are known to be unstable and less probable to form under normal conditions. In this project, we demonstrate these high-energy surfaces could be stabilized under certain growth conditions. This approach sug-gests a general solution for controlling the growth morphology, and it can be applied to other compounds to tailor their structure and obtain materials that are not normally stable. Further, we present a comprehensive study of diamond-based systems for application in high-power and high-frequency electronic devices. This includes two major projects to find solutions for SD of diamond. the first project studies the possibility of using hexagonal Bo-ron Nitride (hBN) and graphene 2D materials as acceptor or interface layer in diamond-based heterostructures. The second project reports modeling and analysis of amorphous vanadium pentoxide slabs as candidates for SD of diamond. Chapter six of this thesis introduces a novel AI-based approach to accelerate computational studies of molecule-surface (and molecule-molecule) interactions. To this end, we present an interactive method that couples Gaussian Processes, Bayesian Inference, and molecular dynamics simulations to accelerate the search for the minimum energy structure in mole-cule-surface interactions. This method addresses the problem of dealing with multiple con-figurations with similar energies. It enables making accurate predictions from relatively small datasets and quantifying the uncertainty associated with each prediction. The last chapter of this thesis introduces a Monte Carlo-based raytracing model, named LightCapture, that simulates light absorption in a microfluidic water-treatment reactor. The LightCapture model was developed to predict geometry - light interaction correlations in reactors consisting of micropillars. This is a critical step in determining the reactor’s overall photocatalytic efficiency. To evaluate the performance, the model was applied to determine light capture efficiency in microreactors that use an array of TiO2 photocatalytic micropil-lars, which are being developed for treating recycled water on spacecraft during deep space missions, and the results were in great agreement with experimental tests. This thesis provides guiding principles for accelerated discovery of wide bandgap semi-conductors. Many of the presented methods and tools have been successfully utilized to provide guiding principles for experimental fabrication.

Photochemical reaction, which starts by exciting a system into an electronically excited state, is ubiquitous, for example, in the atmosphere. This has made photochemical reactions a very interesting topic. Multiconfigurational pair-density functional theory (MC-PDFT) is a powerful and efficient method for studying photochemical processes. This method has proved very efficient compared with other wave function methods, such as multi-state complete active space second order perturbation theory (MS-CASPT2), especially for large systems. Successful as MC-PDFT is, there are some limitations that stop MC-PDFT from being applied to studying photochemistry problems. The first limitation is that, like other multireference methods, the performance of MC-PDFT depends on the quality of the reference wave function, which by convention is optimized by an active-space method, such as complete active space self-consistent field (CASSCF). The second limitation is that MC-PDFT is a single-state method that does not include state interaction between reference states. This means that MC-PDFT gives wrong topologies of potential energy surfaces, which are important in studying photochemical reactions. My work is focused on resolving these two limitations. We proposed the ABC scheme and the ABC2 scheme to automatically generate the active space that gives good-quality reference wave functions thus successfully reproducing vertical excitation energies obtained from experiments or high-level calculations. We proposed the extended multi-state PDFT (XMS-PDFT) and compressed-state multi-state PDFT (CMS-PDFT) as two options to introduce state-interaction in pair-density functional theory. Among two methods, XMS-PDFT is more efficient, while CMS-PDFT is more robust. Both methods proved successful in providing correct topologies of potential energy surfaces for a variety of systems.

Ever since the beginning of research journals, the number of academic publications has been increasing steadily. Nowadays, especially, with the new importance of online open-access journals and databases, research papers are more easily available to read and share. It also becomes harder to keep up with novelties and grasp an idea of the general impact of a given researcher, institution, journal, or field. For this reason, different bibliometric indicators are now routinely used to classify and evaluate the impact or significance of individual researchers, conferences, journals, or entire scientific communities. In this thesis, we provide tools to study trends in any given area of science. However, we focus our work on the field of Density Functional Theory (DFT), an important methodology in physics and chemistry, used to describe materials at the atomic scale, which has demonstrated an exponential number of related publications (with 5,339 in 2010, 9,931 in 2015 and 14,265 in 2019). We measure the specific impact of this theory by means of the citation record of the most used solid-state first principle {\it ab initio} computational packages. Along with this analysis, we developed a Python library, pyBiblio, to compute basic bibliometric analyses on any Web of Science database. To get a deeper understanding of the field, we also use the Latent Dirichlet Allocation (LDA) algorithm on the abstracts of the papers published in this field to classify documents into topics of interest. Indeed, LDA is a generative topic modeling algorithm that creates an efficient and reliable distribution of documents over topics constructed from the papers’ vocabulary. We find that DFT is a collaborative field, with tight international clusters, especially in Europe and between countries where packages are developed. We study the evolution of topics over the years and find evidence for the specialization of the software packages, even if they include similar capabilities.

This study focuses upon understanding and rationally tuning the surface reactivity towards C, H, and O of non-noble metal intermetallic compounds (IMCs) catalysts in olefin production and hydrocarbons reforming reactions. In these reactions, different degrees of surface reactivity towards C, H, and O are required to achieve high activity and selectivity as well as stability. A combined computational and experimental method was performed to build this understanding how to rationally design catalysts. Investigations based on quantum chemical calculations indicate surface reactivity towards C, H, and O is a function of element size of constituent elements as well as bulk and surface composition, which also translates to reaction energetics of catalytic activity preference for each material. New BEP correlations are also evident over the IMCs that exhibit strong electronic effects and pronounced p-element contributions to the surface chemistry. Analysis of electronic structure beyond d-band center illustrates how the surface chemistry and nature of reaction site is dictated by density of d-states of TM and p-states of p-element near Fermi level, degree of their hybridization, and degree of covalent-like bonding within solids. Experimentally, a fundamental understanding of how to synthesize well-defined oxide-supported nanoparticle IMCs was established. It is found that the kinetics of IMC formation and control of surface composition is related to precursor choice, reduction temperature, H2 chemical potential, interaction between surface and support, and gas phase environment and temperature during annealing. Based on this understanding, oxide supported nanoparticle Ni+Ga IMCs with pure phase and controlled surface compositions were synthesized and investigated in propane steam reforming, which enabled the direct investigation of correlation between surface and catalytic chemistry and bulk and surface composition. This study provides directions on developing new materials that exhibit new, unique, and tunable surface chemistry, which will have considerable impact on the greater heterogeneous catalysis community.

Redox reactions are one of the most prevalent chemistries and a ubiquitous component in naturally occurring processes as well as in the production of fuels and various chemicals involving pharmaceuticals, agrochemicals, and other industrially relevant chemicals. Research efforts in the past have focused on the development of safe and environmentally benign redox processes using alternative resources to deal with issues pertaining to global warming and depletion of the existing crude oil reserves. In addition, in the area of organic synthesis, new synthesis strategies are being developed to access important redox derived chemical intermediates via use of less toxic and easily available redox reagents. However, as is the case with most newly developed catalytic processes, the fundamental challenges associated with poor reaction selectivity and activity hinder the effectiveness of these new reactive transformations and technologies, making them less desirable compared to the currently used approaches. Understanding the molecular transformations occurring during these processes thus remains the key ingredient in the further development of these new processes. Gaining mechanistic and kinetic insights into the reaction would allow us to identify factors that control the selectivity and activity for the given reaction, which would then guide the development of more active and selective reaction systems. Along these lines, electronic structure calculations based mainly on density functional theory (DFT) has emerged as a powerful tool to model the reaction kinetics and also to gain important perspectives about the chemical reactions that are not observable experimentally. My dissertation thus focuses on using density functional theory (DFT) to gain important molecular level insights into the transformations that control the oxidation and reduction pathways occurring via thermal or electrocatalytic routes for five independent redox chemistries. Further, we also look into the salient features of redox chemistry that come into play during fabrication of electronic devices, particularly for MoS2 based transistor devices, which ultimately affect its ensuing performance. The first part of the thesis focuses on ab-initio study of three particular systems of interest in oxidation chemistry. Each of these studies separately report on three distinct catalytic features including the nature of the sites, molecularity of the catalyst, and the presence of surface coverages that affect the selectivity towards the desired product. The initial focus is on processes involving partial oxidation of feedstock chemicals which suffer from issues of low selectivity since the processes of over-oxidation is thermodynamically more favorable. One such example of a system that suffers from over-oxidation is sulfur oxidative coupling of methane (SOCM), which involves selective production of ethylene from methane using sulfur as an oxidant. Herein, we report a detailed kinetic and mechanistic study for SOCM reaction done over a sulfided Fe3O4 catalyst (FeS2). Experimental mechanistic analysis involving Delplot and contact time studies reveal a reaction network for SOCM that is different compared to the traditional OCM. Further computational analysis done for this reaction network suggests a site-specific formation of the selective C2 products and unselective carbon-di-sulfide, allowing for potential tuning of the reaction selectivity. The second system of interest that displays challenges with over-oxidation is the dehydrogenation of cyclohexanone. In this study, we examine the role of the homogeneity of the catalyst (Pd(DMSO)2(TFA)2) in selective control of the dehydrogenation reaction of cyclohexanone. Our DFT calculations indicate that the dehydrogenation proceeds via a rate determining intermolecular deprotonation step, activating the C-H bond at the alpha position. Using distortion-interaction analysis, the further activation of the cyclohexenone intermediate formed is shown to be inhibited due to its higher C-H bond strengths as well as due to its weaker interactions with the hydrogen abstracting entity, TFA- compared to cyclohexanone. Besides issues pertaining to over-oxidation affecting the selectivity, for reacting species with multiple reaction centres or functionalities, multiple parallel reaction pathways can emerge which could also lead to lowering of selectivity towards the desired intermediate. Primary electro-oxidation of glycerol over Pt catalyst is one such example wherein the product distribution is dependent on the carbon center that is being activated (primary versus secondary). Using DFT calculations, we hereby report the influence of coverages on directing the oxidation of the C-H bond at the primary versus the secondary position. We show that under high coverage conditions, oxidation at the primary C-H bond to form glyceraldehyde is preferred despite the stronger C-H bond strengths whereas under low coverage conditions, the weaker secondary C-H bond is preferentially activated to form dihydroxyacetone. We further examine the influence of the reaction conditions such as the pH, metal surface, and the operating potentials on the resultant product distribution for electrocatalytic glycerol oxidation over Pt. The second part of the thesis involves a mechanistic investigation of some important reduction chemistries, in particular - the hydrogen evolution reaction (HER) and the electrochemical Birch reduction. In the HER, we hereby examine MoS2 as a non-precious catalyst for use in fuel cells for production of hydrogen from water, and report enhancements in its electrocatalytic activity via application of an external electric field normal to the MoS2 electrode surface (back-gating). DFT studies aimed to understand the mechanistic nuances of the observed increased activity show that the excess electron densities induced via back-gating increase the binding energies of the hydrogen on the MoS2 surface, which in turn lead to improvements in the electrocatalytic activity of MoS2 for HER. In the next topic, we look at the Birch reduction process wherein we report a practical, safe, and scalable electrosynthetic strategy for the reduction of the arenes to dienes and for other similar reductive transformations and subsequently determine the mechanistic nuances of the reaction in the presence of various reagents and additives. Through electroanalytical and computational investigations, we have shown the reaction pathway to proceed via the reduction of the substrate near the electrode surface with the protonation step as the rate limiting step of the reaction. Further, we have deciphered the unique role of each of the reagents used in the study – electrolyte, lithium bromide (LiBr); solvent, tetrahydrofuran (THF); proton source, dimethyl urea (DMU); and the additive, tris(pyrrolidino)phosphoramide (TPPA), to understand how these components co-operatively aid in promoting the desired transformations towards higher reaction yields. In the final part of the thesis, diverging from the theme of redox reactions in chemical production, we investigate the influence of the redox reactions in device fabrication processes with the aim to mitigate some of the issues concerning the contact problem typically observed at the interface. We study here a system of MoS2-metal contacts, which have applications in development of sub-nanometric transistors. Via our “single-atom addition” approach used here, we show that the nature of the metal deposited strongly influences the resulting structural and electronic properties of the MoS2-metal interface. In conjunction with analytical scanning tunnelling electron microscopy (STEM) studies, we screen and characterize the interfacial properties for different metal contacts including Sc, Ti, Cu, In, and Au, with the aim to design systems that minimize the contact resistance at these interfaces.

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.

In recent years, deep learning technologies have received huge attention and interest in the field of high-performance material design. This is primarily because deep learning algorithms in nature have huge advantages over the conventional machine learning models in processing massive amounts of unstructured data with high performance. Besides, deep learning models are capable of recognizing the hidden patterns among unstructured data in an automatic fashion without relying on excessive human domain knowledge. Nevertheless, constructing a robust deep learning model for assessing materials' structure-property relationships remains a non-trivial task due to highly flexible model architecture and the challenge of selecting appropriate material representation methods. In this regard, we develop advanced deep-learning models and implement them for predicting the quantum-chemical calculated properties (i.e., formation energy) for an enormous number of crystal systems. Chapter 1 briefly introduces some fundamental theory of deep learning models (i.e., CNN, GNN) and advanced analysis methods (i.e., saliency map). In Chapter 2, the convolutional neural network (CNN) model is established to find the correlation between the physically intuitive partial electronic density of state (PDOS) and the formation energies of crystals. Importantly, advanced machine learning analysis methods (i.e., salience mapping analysis) are utilized to shed light on underlying physical factors governing the energy properties. In Chapter 3, we introduce the methodology of implementing the cutting-edge graph neural networks (GNN) models for learning an enormous number of crystal structures for the desired properties.

Improving the target properties of existing materials or finding new materials with enhanced functionality for practical applications is at the heart of the materials research. In this respect, the first-principles approaches, which have been successfully integrated into modern high- performance computers, have become an indispensable part of the materials research, providing a better understanding of existing materials and guidance on the design of new materials. Using state-of-the-art computational/theoretical approaches that couple global structure prediction with ab initio density functional theory calculations, we investigate structural and electronic properties of CsxO [cesium oxides], Li1+xMn2O4 [lithium manganese oxides], and Y5Si3Hx [hydrogenated yttrium silicide]. First, we identify thermodynamically stable crystal structures of CsxO (1 ≤ x ≤ 3), Li1+xMn2O4 (0 ≤ x ≤ 1), and Y5Si3Hx (0 ≤ x ≤ 8), and analyze their properties that are relevant to practical applications. CsxO are known as low work function materials, and it is revealed that a very low work function (0.66 eV) is achievable for CsO compound over a relatively wide range of the oxygen chemical potential. Moreover, atomic adsorption on the surface of Cs3O results in a decrease in the work function, which stems from the fact that it is an electride. In the case of LiMn2O4, a promising cathode material for secondary lithium-ion batteries, it is demonstrated that lithium insertion into the spinel structure results in the emergence of various degrees of freedom for crystal formation such as Li ions occupation sites, oxidation states of Mn ions, and local Jahn-Teller distortions. This finding suggests a possible cause of the cycle capacity fade. Finally, Y5Si3 is known as an electride that can improve catalytic reaction and reversibly store hydrogen. Investigation on the anionic electrons demonstrates that Y5Si3 contains two times more anionic electrons than reported value and that the anionic electrons are robust against lattice vibrations. We also shed light on the hydrogenation mechanism of Y5Si3Hx by identifying favorable hydrogen occupation sites and diffusion paths. It is revealed that the 1D hollow space is the dominant diffusion channel, and fast diffusion is possible in a specific hydrogen content range.

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

Microwave vacuum electron devices are a critical part of communication satellite payloads. The phenomenon of multipactor breakdown (MPB) can cause the performance of these devices to deteriorate, including their partial or total destruction. MPB depends on a number of factors, including the secondary electron yield (SEY) of materials used within the device. The process of electron multiplication, which leads to breakdown, initiates with the impact of primary electrons on target surfaces, and the resonance of free electrons within an oscillating electromagnetic field under vacuum conditions. Therefore, it is of interest to find materials and surface treatment methods that exhibit low SEY. This thesis work describes controlled measurements of SEY from electron bombardment of several materials in the low energy regime, from 10 eV to 1 keV. Materials studied include copper (Cu), silver, stainless steel, aluminum 6061 (Al), monel, nickel/cobalt and invar. In addition, different surface treatment methods employed in this study are described, which illustrate that SEY is a surface-specific process.

Herein, experiment and theory are combined to study the efficacy of synthesizing core@shell Au@Pt dendrimer-encapsulated nanoparticles (DENs) through electrochemical means. DENs are small (~1-2 nm), catalytically active nanoparticles (NPs) with precise sizes and compositions. These features assist pairing experiment with theory and catalytic interpretation. The small sizes of DENs can impart interesting physical and chemical properties that differ from bulk phase materials. Core@Pt shell NPs with monolayer (ML)-thick shells minimize the use of Pt, which is a key catalyst for various reactions but is expensive and scarce. Owing to the fact that the available techniques for Pt ML deposition were originally developed for bulk Au, their applicability to ~1.6 nm Au DEN cores is not trivial. In this dissertation, we explore several electrochemical strategies for synthesizing Au@Pt DENS: hydride-terminated (HT) Pt electrodeposition and underpotential deposition, followed by galvanic exchange with Pt (UPD/Pt GE) using two different UPD metals (Cu and Pb). Each of these techniques deposits a single Pt ML onto bulk Au surfaces. However, when they are applied to ~1.6 nm Au DENs, the AuPt NP structures that form are both dissimilar to one another and to the corresponding structures for bulk Au. The HT synthesis is found to lead to an alloy structure, whereas AuPt NP structure formed upon UPD/Pt GE depends on the choice of the UPD metal (Cu or Pb). More specifically, Cu UPD/Pt GE produces a core@shell structure, whereas an alloy structure is afforded by Pb UPD/Pt GE. These conclusions are supported by extensive experimental characterization and density functional theory (DFT) calculations. For the HT method, a core@shell structure can ultimately be obtained, but requires 3-5 total HT pulses. Due to the fact that catalysis tends to be highly structure sensitive, the results of these studies are important for rational design of catalysts. Indeed, we show that varying the number of HT pulses (from 1-10) can be used to tune electrocatalysis for formic acid oxidation (FAO).

We develop a theoretical framework for a priori estimation of catalytic activity of metal nanoparticles using geometry-based reactivity descriptors of surface atoms and kinetic analysis of reaction pathways at various types of active sites. We show that orbitalwise coordination numbers α (α = or ) can be used to predict chemical reactivity of a metal site (e.g., adsorption energies of critical reaction intermediates) by being aware of the neighboring chemical environment, outperforming their regular () and generalized (̅̅) counterparts with little added computational cost. Here we include two examples to illustrate this method: CO oxidation on Au (5¹⁰6¹) and O₂ reduction on Pt (5⁹6¹). We also employ Bayesian learning and the Newns-Anderson model to advance the fundamental understanding of adsorbate-surface interactions on metal nanocatalysts, paving the path toward adsorbate-specific tuning of catalysis.

Dogan et al.[1], investigated the causes of ferroelectricity in doped hafnia using ab initio methods. Similarly, we investigated the stability of Al doped hafnia using quantum mechanical methods. There are many different phases of Hafnia: monoclinic, tetragonal, cubic and orthorhombic. Starting with the monoclinic phase of Hafnia, Hafnia undergoes phase transitions which result in different space groups. The temperature at which the tetragonal phase is induced is 2000 K and cubic phase is induced at 2900 K[1]. Different dielectric constants vary from phase to phase. The average dielectric constants are highest for the cubic and tetragonal phases. In order to force a high temperature phase to be stable in Hafnia, one would need to introduce cation dopants. The polar orthorhombic phase is well known to exhibit ferroelectricity. Since inducing this phase with about 40 GPa it is more effective to induce a ferroelectric phase in Hafnia via doping methods. Doping methods for Hafnia are well established in [1] and demonstrated for Si, Ge, La and other elements. The rhombohedral phase is of interest and investigated first in [35]. Since ferroelectricity in the rhombohedral phase is difficult to stabilize, dopants are introduced analogously to the orthorhombic case. In this work, we doped the monoclinic, tetragonal, orthorhombic and rhombohedral phase of Hafnia with Al to investigate the effect of dopants on ferroelectricity and relevant EXAFS data was also produced. Using plane wave density functional theory, the tetragonal, orthorhombic, and trigonal phases of aluminum doped hafnia were geometrically optimized. The resulting 3 percent, 6 percent, and 7 percent Al doped hafnia structures were used for generating expected EXAFS spectra for experimental data analysis and validation. Specifically, these DFT calculated structures are going to be used in the non-linear least square EXAFS fits of the thin films HfAlO2.

As the world is facing post-climate change situation and the escalating demand of energy, a gradual energy transition from fossil fuels (oil, coal, natural gas) to renewable energy technologies (natural resources such as biomass, solar, wind) is inevitable and essential. Solar energy, as the most abundant, safe and clean energy source among all available renewable energy technologies, has prospective potentials as future power generators. However, although significant progress has been made, the electricity generation from sunlight in commercial photovoltaic (PV) solar cells still suffer from low energy conversion efficiency and high cost. In addition, due to technological difficulties associated with the rapid electron-hole recombination in photocatalysis systems, realizing commercial implementations of photocatalytic systems remains a challenge. In this dissertation, a thorough first-principles analysis was performed on three hydrogen-involving processes, including solar to electricity, catalytic hydrotreating and solar to chemical conversion processes. By investigating their thermodynamic and kinetics properties, we aim to bridge solar energy utilization with chemical energy production under ambient conditions. We begin with the discussion of nitrogen-related defects in a dilute nitride material GaInNAs, which shows promising candidacy as the fourth junction in multijunction solar cells. While the photoluminescence spectra presented remaining defects in the hydrogenated alloy, first-principles calculations revealed the thermodynamic limitation of hydrogen addition on the In-N complexes. Next, a comprehensive kinetic study on cyclohexene hydrogenation reaction on palladium and palladium hydride surfaces were conducted, through a combination of experimental and theoretical work, which laid foundations for later study of plasmonic hydrogenation reactions. In light of thermodynamic and kinetic studies addressed earlier, we undertake investigations on plasmonic catalytic hydrogenation of 4-nitrophenol on gold surfaces. We measured the catalytic activity with and without light irradiation and observed higher reaction rates under light. A theoretical correlation between rate enhancements and reaction energy barrier reduction was constructed via density functional theory calculations. Moreover, we propose that the reaction can be further enhanced over a hexagonal boron nitride (h-BN) layer supported on Au in an Au@h-BN core-shell structure. In this dissertation, we attempt to understand the electron behavior at atomic level, through the investigation of hydrogen-involved processes. Moreover, by discussing efficiency improvement options, we attempt to guide the future design of plasmonic catalysts.

The first study project is based on modeling Earth abundant 3d transition-metal methoxide complexes with potentially redox-noninnocent ligands for methane C–H bond activation to form methanol (LnM-OMe + CH4 → LnM–Me + CH3OH). Three types of complex consisting of tridentate pincer terpyridine-like ligands, and different first-row transition metals (M = Ti, V, Cr, Mn, Fe, Co, Ni, and Cu) were modeled to elucidate the reaction mechanism as well as the effect of the metal identity on the thermodynamics and kinetics of a methane activation reaction. The calculations showed that the d electron count of the metal is a more significant factor than the metal's formal charge in controlling the thermodynamics and kinetics of C–H activation. These researches suggest that late 3d-metal methoxide complexes that favor σ-bond metathesis pathways for methane activation will yield lower barriers for C–H activation, and are more profitable catalyst for future studies. Second, subsequently, on the basis of the first project, density functional theory is used to analyze methane C−H activation by neutral and cationic nickel-methoxide complexes. This study identifies strategies to further lower the barriers for methane C−H activation through evaluation of supporting ligand modifications, solvent polarity, overall charge of complex, metal identity and counterion effects. Overall, neutral low coordinate complexes (e.g. bipyridine) are calculated to have lower activation barriers than the cationic complexes. For both neutral and cationic complexes, the methane C−H activation proceed via a σ-bond metathesis rather than an oxidative addition/reductive elimination pathway. Neutralizing the cationic catalyst models by a counterion, BF4-, has a considerable impact on reducing the methane activation barrier free energy. Third, theoretical studies were performed to explore the effects of appended s-block metal ion crown ethers upon the redox properties of nitridomanganese(V) salen complexes, [(salen)MnV(N)(Mn+-crown ether)]n+, where, M = Na+, K+, Ba2+, Sr2+ for 1Na, 1K, 1Ba, 1Sr complexes respectively; A = complex without Mn+-crown ether and B = without Mn+). The results of the calculations reveal that ΔGrxn(e ̶ ) and thus reduction potentials are quite sensitive to the point charge (q) of the s-block metal ions. Methane activation by A, 1K and 1Ba complexes proceeds via a hydrogen atom abstraction (HAA) pathway with reasonable barriers for all complexes with ~ 4 kcal/mol difference in energy, more favorable free energy barrier for the complexes with higher point charge of metal ion. Changes in predicted properties as a function of continuum solvent dielectric constant suggest that the primary effect of the appended s-block ion is via "through space" interactions. Finally, a comprehensive DFT study of the electrocatalytic oxidation of ammonia to dinitrogen by a ruthenium polypyridyl complex, [(tpy)(bpy)RuII(NH3)]2+ (complex a), and its NMe2-substituted derivative (b), is presented. The thermodynamics and kinetics of electron (ET) and proton transfer (PT) steps and transition states are calculated. NMe2 substitution on bpy reduces the ET steps on average 8 kcal/mol for complex b as compared to a. The calculations indicate that N–N formation occurs by ammonia nucleophilic attack/H-transfer via a nitrene intermediate, rather than a nitride intermediate. Comparison of the free energy profiles of Ru-b with its first-row Fe congener reveals that the thermodynamics are less favorable for the Fe-b model, especially for ET steps. The N-H bond dissociation free energies (BDFEs) for NH3 to form N2 show the following trend: Ru-b

Point defects in wide-bandgap semiconductors can be used to implement qubits that exploit spin-dependent fluorescence from electronic transitions. These qubits are the quantum mechanical analog of classical bits in that they leverage quantum mechanical properties such as superposition and entanglement and a spin-dependent fluorescence implies the ability to read out the qubit state optically. Fluorescent defects in wide-bandgap semiconductors such as diamond have long been studied, but an electronic spin coherence time exceeding one second at room temperature for the NV−, a defect in diamond comprised of a substitutional nitrogen atom adjacent to a carbon vacancy with a single negative charge, has only relatively recently been demonstrated. The demonstration of long-lived spin memory coupled with the fact that the NV− exhibits spin-dependent fluorescence spurred research on other point defects in diamond and in related materials such as silicon carbide for applications in quantum computing and quantum information as well as in magnetometry. However, maximizing the yield of point defects from the various techniques for creating them such as ion implantation or chemical vapor deposition is still not perfectly understood. Using density functional theory and kinetic Monte Carlo and molecular dynamics simulations, we provide an understanding of the thermodynamics and kinetics of point defect formation and migration that can be used to predict the annealing regimes that should lead to high yields and we provide a general framework that can be used to explain relative yields for various Fermi level regimes.

The growing concerns over greenhouse gas emissions and the limited fossil fuel resources has driven the development of alternative carbon-neutral energy sources. Lignocellulosic biomass is considered an attractive option as they are renewable and are available in abundance at low cost. However, these feedstocks possess relatively low carbon/oxygen ratio and thus making the oxygen removal necessary to increase the energy density. The chemical treatment of biomass in aqueous phase typically involves initial decomposition of sugar polymers into individual sugar monomers followed by a series of acid-catalyzed dehydration steps. The intermediate products that from include 5-hydroxymethylfurfural (HMF), 2-furfuraldehyde (furfural), levulinic acid, and formic acid which can be further upgraded to liquid fuel. These processes are relatively cost-efficient as they can be carried out in aqueous phase at low temperature. Despite its economic potential, very little is known about the effects of solvent on the dehydration kinetics. In this work, we utilized ab initio quantum chemical methods to examine the underlying principles that control the reactivity and selectivity of biomass conversion reactions, especially Brønsted acid-catalyzed dehydration of biomass-derived oxygenates in organic solvent/water mixtures. Recent experimental studies have shown that the rate of acid-catalyzed dehydration of sugars increases by up to two orders of magnitude when the reaction is carried out in polar aprotic solvent/water mixtures such as DMSO/water mixtures and acetonitrile/water mixtures in comparison to water solvent. Furthermore, the addition of metal halides such as chloride anion into the reaction media has been shown to significantly improve the reactivity and selectivity towards the desired HMF product. In order to develop better understandings of these observations, we used ab initio molecular dynamics (AIMD) together with classical molecular dynamics simulations to explore the effects of reaction environment in liquid phase on the dehydration reactions of biomass-derived oxygenates. Our calculations suggest that the rate of Brønsted acid catalyzed dehydration in polar aprotic solvent/water mixtures is governed by the relative stability between reactants in comparison to transition state in the rate determining step. This difference in the stability is in turn controlled by the different extent of solvation for these reactive species in different solvents which explains the reactivity trend observed experimentally. The molecular dynamics trajectories for organic solvent/water systems reveal that the solvent-solute as well as solvent-solvent interactions result in the reorganization of solvent molecules around the solvated species, leading to the formation of hydrophilic domain on the reactive polyol hydroxyl groups in which the acidic proton and other hydrophilic species reside. The localization of these reactive species in close vicinity facilitates the dehydration reaction, stabilizes the charged transition state, and lowers the activation free energy of dehydration. The investigation is also extended to different reaction paths leading to the formation of humins side products to elucidate the effects of solvent on the selectivity of dehydration reactions toward HMF. In the second part of this thesis, we focus on the use of heterogeneous catalyst for dehydration reactions. The use of heterogeneous catalyst is favored over homogeneous catalyst due to the ease of catalyst recovery. However, the ab initio computational investigation of liquid-phase reactions in microporous catalyst is challenging as the result can be sensitive to the number of solvent molecules and reactive species available in the zeolite pore. In this work, we thus focused on developing an atomistic model to predict the adsorption properties of water molecules in industrially common Faujasite and Mordenite zeolites. The model features a new ‘cluster swap’ Monte Carlo move that allows simultaneous simulation of cation siting and aluminum distribution within the zeolite framework. The simulation results were found to satisfactorily reproduce the cationic siting in Faujasite as well as aluminum occupancy of the framework T-sites in Mordenite over a broad range of Si/Al ratios. The obtained parameters can be further improved to include the adsorption affinity for each solvent and solvated species.

Alloys have been widely studied for heterogeneous catalysis. Many bi- and multi-metallic alloys have enhanced performance as compared to their monometallic counterparts. However, a full understanding of the alloying effects was not well-established. In my Ph.D. works, density functional theory (DFT) was employed to disentangle the atomic ensemble, ligand, and strain effects of surfaces alloyed by transition metals. It is found that alloying elements with strong and weak adsorption properties could produce a surface ensemble with optimally tuned adsorbate binding, which can help to understand the mechanisms of catalytic reactions and design high-performance alloy catalysts. We developed a Tunability theory that quantifies the tuning of adsorbate bindings at the specific atomic ensembles on surface, which provides predictive power of theory for experiments. Using combined theoretical and experimental methods, we designed and studied new alloy catalysts for many industrially significant reactions including electrocatalysis, vapor-phase catalysis, and liquid-phase catalysis. We developed comprehensive theories that predominantly based on the atomic ensemble effect to unify theories and experiments for alloy heterogeneous catalysts. Most importantly, we show how fundamental understandings from theories can be precisely applied to efficient energy and environmental reactions. In addition, to accelerate atomistic simulations and materials design, we have been developing a machine learning framework that can fit the potential energy surfaces from the quantum mechanical data, which can help to partly replace expensive DFT calculations and reduce scientific costs

Vacancy-mediated diffusion along dislocations, often referred to as pipe diffusion, can contribute to creep deformation of metals in many engineering applications. This process is studied along an a/2⟨1 -1 0⟩ screw dislocation in fcc Ni using a density functional theory approach. An accurate geometrical configuration of the screw dislocation core, dissociated into Shockley partial dislocations and separated by a stacking fault, was previously derived using a lattice Green's function technique. Activation energies and frequencies are calculated for atom-vacancy exchanges that contribute to diffusion around and along one of the partial cores. This analysis reveals the significant role of the sites within the compressive component of the dislocation, the dominant contribution of the hops around the screw geometry rather than directly along the dislocation line, and the importance of including the stacking fault sites. Kinetic Monte Carlo simulations use these frequencies to generate diffusion coefficients that account for correlation effects. Near 80% of the melting temperature, these pipe diffusivities are an order of magnitude higher than those found in fcc regions, and they are eight orders higher at room temperature. Calculations are compared to experimental results and the differences are discussed. While pipe diffusion is unlikely to contribute to isotropic mass flux at low dislocation densities, it will accelerate dislocation mechanisms controlling creep, particularly when alloying elements are involved. To quantify the effects of chemistry on diffusivity in addition to geometry, the described techniques also analyze the pipe diffusivity of several important alloying elements to Ni-base superalloys: Co, Cr, Al, Ti, Mo, W, and Re. Calculations quantify how the partial core enhances diffusivities of each element relative to isotropic diffusion in fcc Ni. Advances in first principles methods provide a means to explore the rate constants of mechanisms that constitute diffusion with molecular dynamics. We apply these to select atom-vacancy exchanges near the same screw dislocation partial core in fcc Ni at 1000 and 1400 K. Intrinsic properties of this dynamic approach contrast limitations of the widely used harmonic transition state theory (HTST) approximations that were previously applied, revealing direction-dependent free energy profiles and temperature-dependent correlation effects. In contrast to static HTST, the dynamic approach reveals an asymmetry in mass transport that is related to the edge character of the partial dislocation. This arises from different philosophies underlying the two methodologies. The absolute values of the rate constants, however, are within the same order of magnitude, with the HTST method generally underestimating rates by a factor of 2-5. Combined with a twofold increase in rates due to thermal expansion at high temperatures, this partially explains why our kinetic Monte Carlo simulations yield lower diffusivities for pipe diffusion than experiments observe.

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.

Electronic structure calculations are an integral step in the design and engineering of materials. Kohn-Sham Density Functional Theory (KS-DFT) is a computationally tractable first-principles formulation of electronic structure that is widely used to predict material properties. KS-DFT represents the electron density in terms of single-electron wavefunctions by replacing explicit electron-electron interactions with a mean-field interaction and an approximation of an exchange-correlation functional. The development of numerical methods for KS-DFT is an ongoing area of research; more efficient numerical methods will enable the simulation of larger and more challenging materials systems and improve KS-DFT's predictive capability. The goal of this work was to develop an integral equation based numerical method for KS-DFT, both to investigate the feasibility and to explore any advantages of an integral equation approach compared to the preexisting numerical methods based on differential equations. We achieved this goal through the development of Treecode-Accelerated Green Iteration (TAGI).We used the method of Green's functions to convert the eigenvalue problem for the Kohn-Sham differential operator into a fixed-point problem for an equivalent integral operator. We developed real-space discretization techniques to numerically evaluate the integral operators with high accuracy, including adaptive mesh refinement schemes, a higher order Fejer quadrature rule, and singularity-subtraction schemes to weaken the Green's function singularities. Next, we leveraged fixed-point acceleration techniques to improve the convergence rates of the fixed-point iterations. Finally, we developed treecodes based on barycentric interpolation; these fast summation algorithms reduce the computational complexity of evaluating the discretized integral operators. We developed these barycentric treecodes to run efficiently on high performance computing architectures; in particular, parallelized the computations with a distributed memory Message Passing Interface (MPI) implementation, and accelerated the computations with Graphics Processing Units (GPUs). We achieved high GPU efficiency by leveraging an extra level of parallelism afforded by the barycentric approximations that is not present for previous treecodes based on multipole expansions. We developed TAGI for two types of calculations; all-electron and Optimized Norm-Conserving Vanderbilt (ONCV) pseudopotential. In all-electron calculations, atomic nuclei are represented by singular Coulomb potentials, and wavefunctions are computed for each electron in the system. The singular nuclear potential, which gives rise to sharply varying wavefunctions near the nuclei, requires substantial mesh refinement to achieve high accuracy. In pseudopotential calculations, the singular nuclear potentials are replaced with smooth, non-singular pseudopotentials. These are generated by absorbing an atom's chemically inert core electrons into its nucleus, leaving only the chemically active valence electrons to be computed. These calculations require significantly less local refinement near the nuclei, enabling TAGI calculations of larger systems. We demonstrated TAGI by computing the ground-state energy of a variety of molecules for all-electron and pseudopotential calculations. We showed TAGI's ability to systematically converge to chemical accuracy through the adaptive mesh refinement schemes. These calculations were performed on up to eight compute nodes containing a total of thirty two GPUs. The techniques developed in this work have enabled TAGI to calculate chemically accurate ground state energies of molecules containing up to a few hundred electrons in several hours (all-electron C6H6 in 4 hours, pseudopotential Si30H40 in 3 hours, and pseudopotential C60 in 8 hours). While TAGI does not outperform the more mature methods based on differential equations, this work constitutes a substantial proof of concept for treecode-accelerated integral equation based methods for KS-DFT and presents numerous opportunities for further improvement.

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.

Control of Néel vector in antiferromagnetic materials is one of the challenges preventing their use of as active device components. Several methods have been investigated such as exchange bias, electric field, and spin injection, but little is known about strain-meditated anisotropy. This study of antiferromagnetic L10-type MnX alloys MnIr, MnRh, MnNi, MnPd, and MnPt shows that a small amount of strain effectively rotates the direction of the Néel vector by 90⁰ for all of the materials. For MnIr, MnRh, MnNi, and MnPd, the Néel vector rotates within the basal plane. For MnPt, the Néel vector rotates from out-of-plane to in-plane under tensile strain. The effectiveness of strain control is quantified by a metric of efficiency and by direct calculation of the magnetostriction coefficients. The values of the magnetostriction coefficients are comparable with those from ferromagnetic materials. These results indicate that strain is a mechanism that can be exploited for control of the Néel vectors in this family of antiferromagnets. CrSb is a layered antiferromagnet (AFM) with perpendicular magnetic anisotropy, a high Néel temperature, and large spin-orbit coupling (SOC), which makes it interesting for AFM spintronic applications. To elucidate the various mechanisms of Néel vector control, the effects of strain, band filling, and electric field on the magnetic anisotropy energy (MAE) of bulk and thin-film CrSb are determined and analysed using density functional theory. The MAE of the bulk crystal is large (1.2 meV per unit cell). Due to the significant ionic nature of the Cr-Sb bond, finite slabs are strongly affected by end termination. Truncation of the bulk crystal to a thin film with one surface terminated with Cr and the other surface terminated with Sb breaks inversion symmetry, creates a large charge dipole and average electric field across the film, and breaks spin degeneracy, such that the thin film becomes a ferrimagnet. The MAE is reduced such that its sign can be switched with realistic strain, and the large SOC gives rise to an intrinsic voltage controlled magnetic anisotropy (VCMA). A slab terminated on both faces with Cr remains a compensated AFM, but with the compensation occurring nonlocally between mirror symmetric Cr pairs. In-plane alignment of the moments is preferred, the magnitude of the MAE remains large, similar to that of the bulk, and it is relatively insensitive to filling.

Density functional theory (DFT) is the most widely used quantum chemistry method. This dissertation explores and advances different aspects of DFT. The computational cost of DFT scales as the third power of the system size. In Chapter 2, we introduce an embedded many-body expansion, called the polarized many-body expansion, which removes the computational bottleneck steps in DFT calculations by exploiting the pairwise additivity of some components of interaction energy in molecular clusters. Energy decomposition analysis fragments interaction energy into physico-chemically meaningful components like permanent electrostatics, dispersion, polarization, and charge transfer. In Chapter 3, we introduce a non-perturbative approach to decompose charge transfer energy into pairwise additive terms. This approach improves upon the previous perturbative approach and can be applied to chemical complexes with both weak and strong charge transfer interactions. Different density functional approximations (DFAs) show varied performance for predicting properties of different chemical systems. In Chapters 4 and 5, we identify DFAs which give good accuracy for predicting the interaction energy of hydrogen with different model binding sites. These DFAs can potentially be used for in silico high-throughput screening of materials capable of storing hydrogen at desired conditions.

Amorphous (non-crystalline) materials offer new capabilities and applications relative to their corresponding crystalline phases due to their novel and tunable material properties, including but not limited to larger surface area, wider substrate compatibility, stronger corrosion resistance, and lower temperature processing than their crystal counterparts. However, even the most advanced experimental techniques (e.g., in situ NMR and ``inverse" approaches such as Reverse Monte Carlo (RMC) modeling of diffraction data) cannot directly measure the bulk atomic structure of such complex, non-crystalline materials. This, coupled with the lack of a structural theory of amorphous materials, has limited our understanding about the structure-property relationships required for the optimization of realistic devices. In this thesis, we compare and combine several atomistic modeling techniques for generating accurate and general structures of amorphous systems, both solid and liquid. We develop several metrics for measuring efficiency of configuration space sampling to guide and standardize the computational study of amorphous materials, and use these to differentiate between different samples of amorphous structures. Lastly, we use these structures to help elucidate structure-property relationships in these systems. In amorphous metal oxides, we characterize the types of coordination defects in amorphous metal oxide systems and subsequently identify potential electron and hole traps in their electronic structures. In a liquid system, water-in-salt electrolytes, we evaluate how the hydrogen bonding network and ion coordination shells change with composition, and how these are directly correlated to battery performance.

Theoretical calculations of molecular properties can assist experimental design of moleculeswith interesting optical, electronic and structural properties which would help accelerate materials discovery. Density functional theory (DFT) within the Kohn–Sham (KS) frameworkhas been the most widely used method for molecular properties calculations in the last threedecades because of its advantageous computational cost-to-accuracy ratio. However, commonly used density functional approximations (DFAs) have been shown to be inadequatefor calculations involving transition metal compounds, metal clusters, conjugated moleculesand for describing noncovalent interactions. Random phase approximation is a post-KSDFA that is accurate for describing noncovalent interactions without the need for empiricalparameters, does not diverge for small-, or even zero-gap systems and incorporates Hartree–Fock (HF) exchange. The first part of this thesis aims at answering the question: can aself-consistent generalized KS scheme be developed for the RPA energy functional whichalso gives access to single particle energies within a variational Lagrangian formalism? Tothis end, an orbital self-consistent scheme called the generalized KS semicanonical projectedRPA (GKS-spRPA) is developed, implemented and benchmarked for ground state as wellas single particle energies. The ionization energies and band-gaps that are calculated using the GKS-spRPA suggest that it is better than the commonly used G0W0 method. Thesecond part of the thesis is concerned with the implementation and testing of static polarizabilities within the GKS-spRPA method. The GKS-spRPA successfully solves theoverpolarization problem observed with the use of semilocal/hybrid DFAs for calculationsof static polarizabilities of pi-conjugated molecules. Calculations involving metallocenes, metal clusters anda small molecule testset are used to show that the static polarizability calculated using the GKS-spRPAmethod is more accurate than DFAs such as PBE, PBE0, CAM-B3LYP and wave functionbased methods such as HF and the second-order Møller–Plesset perturbation theory (MP2).Thus, this thesis conclusively shows that the GKS-spRPA within a Lagrangian framework,is a method that provides not only accurate ground state energies but also a wide range ofmolecular properties such as geometries, ionization potentials, electron affinities, dipoles andpolarizabilities with a reasonable computational cost of O(N 4 log(N )).