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Lithium ion batteries have proven to be a paradigm shifting technology, enabling high energy density storage to power the handheld device and electric automotive revolutions. However relatively slow progress toward increased energy and power density has been made since the inception of the first functional lithium ion battery. Materials under consideration for next generation lithium ion batteries include anionic-redox-active cathodes, solid state electrolytes, and lithium metal anodes. Li-rich cathodes harness anionic redox, showing increased first charge capacity well beyond the redox capacity of traditional transition metal oxides, though suffer from severe capacity and voltage fade after the first cycle. This is in part attributed to oxygen evolution, driving surface reconstruction. Solid-state electrolytes (SSEs) offer the potential for safer devices, serving as physical barriers for dendrite penetration, while hoping to enable the lithium metal anode. The lithium metal naturally exhibits the highest volumetric energy density of all anode materials. Here, we employ simulation and advanced characterization methodologies to understand the fundamental properties of a variety of next generation lithium ion battery materials and devices leading to their successes or failures. Using density functional theory, the effect of cationic substitution on the propensity for oxygen evolution was explored. Improvement in Li-rich cathode performance is predicted and demonstrated through doping of 4d transition metal Mo. Next, lithium phosphorus oxynitride (LiPON), an SSE utilized in thin film batteries, was explored. LiPON has proven stable cycling against lithium metal anodes, though its stability is poorly understood. RF sputtered thin films of LiPON are examined via spectroscopic computational methods and nuclear magnetic resonance to reveal its atomic structure, ultimately responsible for its success as a thin film solid electrolyte. A new perspective on LiPON is presented, emphasizing its glassy nature and lack of long-range connectivity. Progress toward in situ methodologies for solid-state interfaces is described, and a protocol for FIB-produced nanobatteries is developed. Cryogenic methodologies are applied to a PEO/NCA composite electrode. Cryogenic focused ion beam was shown to preserve polymer structure and morphology, enabling accurate morphological quantification and preserving the crystallinity, as observed via TEM. Last, development of in situ solid-state interface characterization is discussed.

This dissertation is an accumulation of my contribution to the fundamental understanding of ensemble Density Functional Theory and finite temperature Density Functional Theory, through the use of quantum model systems. Ensemble DFT is a time-independent method to extracting excitation energies, and provides a way to extract multiple excitations. Currently this is not possible with the common approximations used in time-dependent DFT. Chapter 3 of this thesis covers ensemble DFT and verifies an exact exchange approximation that can accurately capture multiple excitations using the Hubbard model. Finite temperature DFT is often incorrectly confused with ensemble DFT, and refers to DFT at non zero temperatures. Its value comes from its applications to warm dense matter simulations. The last three chapters of this dissertation contain several projects in the hopes of improving the understanding of finite temperature DFT. In particular it generalizes the PPLB derivative discontinuity model to finite temperatures, and discusses the construction of an exact finite temperature approximation. These contributions are done with the intention on enhancing warm dense simulations.

CuAlO2 is among several ternary delafossites, which is a rare p-type semiconductor with potential applications as a transparent conductive oxide, photocatalyst, and spintronics when doped with transition metal ions. Reported in this thesis are results from our investigations of CuAl1-xFexO2 (x = 0 to1) with a focus on the x-dependence of structural, magnetic, vibrational, optical properties, and the role of defects and impurities. Samples are prepared by solid-state reactions. We performed a complete study of magnetic properties to investigate the possibility of room temperature ferromagnetic alloys, which are used in transparent ferromagnet applications, suggested by a computational study. Analysis of magnetization (M) vs. temperature (T, from 2 to 300 K) data by Curie-Weiss law confirms Fe3+ as the electronic state of Fe; this analysis also yields a negative θ characteristic of an antiferromagnetic Fe3+-Fe3+ exchange coupling and magnitudes of x in good agreement with the nominal values. The isothermal M vs. H (up to H= 90 kOe) data analyzed by the modified Brillouin function support the results obtained from the M vs. T analysis. High-resolution M-H loop measurements at 300 K and 10 K show negligible coercivity (HC)at 10 K but HC ~ 100 Oe at 300K. The results suggest that the room temperature ferromagnetism can originate from hematite impurity, but not for CuAl1-xFexO2 alloys. The understanding of the phonon dynamics of alloys is crucial because they have a fundamental indirect bandgap. I introduce a new approach, which is applicable to anisotropic, dilute alloys with allowance for a large variety of alloying elements. This approach has significant advantages over previously reported methods, especially for the lattice dynamics of such complex alloys. We use this approach to model the effects of Fe-doping on the vibrational modes in dilute alloys of CuAl1-xFexO2 (x = 0-0.10) delafossite powders. Raman and FTIR spectroscopies are performed to measure optical phonon frequencies. For the phonon calculations, an approach using a disordered supercell is not feasible because it is computationally expensive. Instead, we developed our weighted dynamical matrix (WDM) approach that uses a straightforward ordered supercell for force-constant calculations of the CuAlO2 and CuFeO2 parent endpoints and combines them using a WDM approach. Computationally, when Fe is substituted for Al (increasing x), an increase in the bond length is observed, leading to a redshift in the peak positions in all the phonon modes vs. x, in agreement with the experimentally observed trend. CuAlO2, with an indirect bandgap ~3 eV, is a good candidate for photocatalysis applications because of its chemical stability and absorption in the solar region. However, efforts to improve its optical absorption continue. Here, I report the effect of alloying on the optical absorption of CuAl1-xFexO2(x = 0.0-1.0) by measuring the optical absorption of alloys from 1 to 6 eV and comparing these results to electronic band structures calculated using density functional theory (DFT) calculations. The calculations use DFT+U supercell methods, including spin. Experimentally, we observe a new absorption feature associated with Fe at about 1.8 eV for x = 0.01 shifting to 1.4 eV for x = 0.10. The energy of this feature and its redshift with x agrees with calculated spin-down Fe-3d states. This added feature will lead to more optical absorption. The major conclusions from this research are that by alloying CuAlO2 with Fe, the optical absorption will improve. Also, we proposed a straightforward, computationally efficient WDM approach, which confirms the redshift associated with the optical phonon modes. With our magnetic measurements, we confirmed that the dilute alloys are not ferromagnetic at room temperature.

The physical origin of charged interfaces involving electrolyte solutions is in the thermodynamic equilibrium between the surface reactive groups and certain dissolved ionic species in the bulk. This equilibrium is very strongly dependent on the precise local density of these species, also known as potential determining ions in the solution. The latter, however, is determined by the overall solution structure, which is dominated by the large number of solvent molecules relative to all solutes. Hence, the solvent contribution to the molecular structure is a crucial factor that determines the properties of electric double layers. Models that explicitly account for the solvent structure are often referred to as "civilized" as opposed to the "primitive" ones that consider the solvent as a structureless continuum. A physically correct description of charged interfaces that involve electrolyte solutions (electric double layers), needs to account for the full solution structure in conjunction with the precise surface chemistry governed by the thermodynamic equilibrium. Apart from charge regulation, these systems involve a wide variety of interactions between the different components of the electrolyte solutions and with the charged interface. While the role of all Coulombic type of interactions is clear, that of the non-Coulombic forces is less obvious. Such as the effects of bulk solvation interactions and solvophobic or solvophillic interactions on the properties of the electric double layer. This doctoral dissertation presents a comprehensive study of the effects of the non-electrostatic interactions on the electric double layer such as ionic solvation, solvent-solvent interactions and interactions of solution components with the charged surface in Electric double layer. The analysis of electrostatic properties like surface charge and potential, and the distribution of ionic species and total electrostatic charge distribution were presented. The analysis uses classical Density Functional Theory which treats the solvent explicitly and ionic species with finite sizes coupled with surface charge regulation.

In recent decades, machine learning techniques have received an explosion of interest in the domain of high-throughput materials discovery, which is largely attributed to the fastgrowing development of quantum-chemical methods and learning algorithms. Nevertheless, machine learning for catalysis is still at its initial stage due to our insufficient knowledge of the structure-property relationships. In this regard, we demonstrate a holistic machine-learning framework as surrogate models for the expensive density functional theory to facilitate the discovery of high-performance catalysts. The framework, which integrates the descriptor-based kinetic analysis, material fingerprinting and machine learning algorithms, can rapidly explore a broad range of materials space with enormous compositional and configurational degrees of freedom prior to the expensive quantum-chemical calculations and/or experimental testing. Importantly, advanced machine learning approaches (e.g., global sensitivity analysis, principal component analysis, and exploratory analysis) can be utilized to shed light on the underlying physical factors governing the catalytic activity on a diverse type of catalytic materials with different applications. Chapter 1 introduces some basic concepts and knowledge relating to the computational catalyst design. Chapter 2 and Chapter 3 demonstrate the methodology to construct the machine-learning models for bimetallic catalysts. In Chapter 4, the multi-functionality of the machine-learning models is illustrated to understand the metalloporphyrin's underlying structure-property relationships. In Chapter 5, an uncertainty-guided machine learning strategy is introduced to tackle the challenge of data deficiency for perovskite electrode materials design in the electrochemical water splitting cell.

Mesostructured porous materials, especially metal-organic framework systems (MOFs), have received widespread attention by researchers over the course of several decades, aimed at exploiting their suitability as sensors, catalysts, adsorption materials, etc. These materials may exhibit high thermal stability for a variety of physical and chemical reactions, benefiting from their unique structural features such as their crystalline order and high internal surface area. Recently, within this class of materials, Covalent Organic Frameworks (COFs) are of particular interest, as they are generally composed of light elements, and offer nanoporous crystalline structure. In this work, we offer an atomic-scale view of the recently synthesized 3D woven COFs, known formally as COF-505-Cu and COF-506-Cu, using molecular simulation techniques, including ab initio Density Functional Theory (DFT) and Molecular Dynamics (MD). First, we explored the reversible demetallation of COF-505-Cu. Serving as the “linkers” of the structure, the covalent bonding between the metal atom and the organic thread is essential to maintain the overall weave of the structure. We proposed two distinct pathways for the demetallation reaction that breaks the covalent bonding, and calculated their respective free energy barriers. We also tested the re-insertion of other, as yet experimentally untested transition metals (Zn and Fe), to the structure, and found that re-formation of the covalent bonding with the metal is strongly affected by the nature of the ionic complex being inserted. Second, we studied the second-generation, non-interpenetrated, COF-506-Cu that was first synthesized in 2018. We adopted the potential function parameters devised for its predecessor (COF-505) to model this material. This force field model was then used to characterize the nature and rate of guest molecule diffusion and uptake in the COF-506-Cu weave. We found that the presence of more guest-accessible space inside the weave greatly increased the diffusion of small molecules, compared to the limited diffusion observed by Acevedo in COF-505. But for other molecules, like the larger THF and methyl orange, movement inside the weave is still constrained through molecular interactions to be tortuous and relatively slow; this was suggested without direct observation in the experimental work. The computationally predicted uptake of THF vapor compared quantitatively well with the experimental data. Those results provided strong validation of the original Acevedo force field model. Looking to the future, the validation of this model could provide a way to predict the efficacy of COF-506, or other variants of this woven COF, ahead of experimental synthesis or applications such as molecule capture (adsorption), separation processes, or catalysis.

The semiconductor industry enabling numerous electronic devices that empower the digital era has arrived at a turning point in recent years. Traditional silicon (Si)-based devices are about to reach the material limit as a result of extreme scaling of gate length below 10nm, leading to the forthcoming end of Moore's law. One of the proposed ways to advance beyond Moore's law is to use alternative, advanced semiconducting materials such as indium gallium arsenide (InGaAs) and gallium oxide (Ga2O3), mainly for their superior electronic properties compared to traditional semiconductors. However, a series of major challenges need to be overcome in order for these materials to live up to their promise, including insufficiently high dopant activation and insufficiently low contact resistivity for InGaAs, and strong degree of dopant segregation towards the surface for Ga2O3. In this thesis, we address these challenges with a computational modeling approach, based on ab initio density functional theory calculations. Our main findings include: (1) formation and binding of negatively-charged cation vacancies are the major contributor of charge compensation in heavily Si-doped InGaAs; (2) composition, surface termination, doping concentration, compositional grading and metal-semiconductor alloying can all affect the contact resistivity of InGaAs; (3) in random InGaAs, vibrational modes associated with shallow n-type dopants (Si, Se) and defects (cation vacancies) assume band-like distribution with many satellite peaks, in contrast with the single-peak signature of the same impurities in binary compounds (GaAs, InAs); and (4) Sn has the strongest tendency to segregate towards the (010) surface of Ga2O3 among three common shallow n-type dopants (Si, Ge, Sn), and the presence of negatively-charged surface Ga vacancies drastically enhance the segregation effect due to Coulomb interaction. This work would serve as a guidance for further experimentation and engineering with advanced semiconducting materials in electronic device applications.

Functional materials are essential to human welfare and to provide foundations for emerging industries. As an alternative route to experimental materials discovery, computational materials designs are playing an increasingly significant role in the whole discovery process. In this work, we use an in-house developed python utility: PAOFLOW, which generates finite basis Hamiltonians from the projection of first principles plane-wave pseudopotential wavefunctions on pseudo atomic orbitals(PAO) for post-process calculation on various properties such as the band structures, density of states, complex dielectric constants, diffusive and anomalous spin and charge transport coefficients. In particular, we calculated the dielectric function of Sr-, Pb-, and Bi-substituted BaSnO3 over wide concentration ranges. Together with some high-throughput experimental study, our result indicates the importance of considering the mixed-valence nature and clustering effects upon substitution of BaSnO3 with Pb and Bi. We also studied two prototype ferroelectric rashba semiconductors, GeTe and SnTe, and found the spin Hall conductivity(SHC) can be large either in ferroelectric or paraelectric structure phase. Upon doping, the polar displacements in GeTe can be sustained up to a critical hole concentration while the tiny distortions in SnTe vanish at a minimal level of doping. Moreover, we investigated the sensitivity of two dimensional group-IV monochalcogenides to external strain and doping, which reveal for the first time giant intrinsic SHC in these materials, providing a new route for the design of highly tunable spintronics devices based on two-dimensional materials.

The linear electro-optic (Pockels) effect, which relates the change in the index of refraction of a crystal to an applied electric field, has been subject to increasing study in recent years due to its potential applications in integrated photonics, which include interchip optical interconnects, neuromorphic computing, and photonic chips for quantum computing. The current “gold standard” Pockels-active material is LiNbO₃, which sees wide use as an optical modulator in the telecommunications industry. However, LiNbO₃ has a small Pockels response (~30 pm/V) and does not integrate well with Si. Therefore, finding other Pockels-active materials is of great importance for their potential use in future devices. Most current studies are focused on BaTiO₃, which has an enormous response (~1600 pm/V) in bulk, and which can be epitaxially integrated on silicon (001). However, it of great technological importance to find other strong Pockels materials and to understand the underlying physical principles which drive strong Pockels responses. In this work, we calculate the Pockels response of a wide variety of materials from first principles. We show that SrTiO₃, a centrosymmetric crystal which ordinarily cannot exhibit a Pockels response, can be made to have a strong response through epitaxial strain. The phonon modes driving the large response in SrTiO₃ are very anharmonic. Noticing that other strong Pockels materials are also strongly anharmonic, we investigate whether crystal anharmonicity in non-centrosymmetric crystals is a predictor of strong Pockels responses. We do this by through an in-depth study of LiB₃O₅, which has thermal anharmonicity an order of magnitude larger than that of BaTiO₃ or SrTiO₃. We find that crystal anharmonicity (or rather, soft phonon modes) is a necessary, but not sufficient requirement for strong Pockels responses. Large Raman susceptibilities, which we associate with strong electron-phonon interactions and large deformation potentials, are also required. Finally, we summarize unpublished calculations of the Pockels response of a variety of crystals, many of which have not been considered for the electro-optical applications, and we provide suggestions for future first-principles studies of the Pockels effect

Electrochemical ammonia synthesis is being actively studied as a low temperature, low pressure alternative to the Haber-Bosch process. This thesis explores iridium as the catalyst for the electrochemical process, following a previous study of platinum catalysts. Specifically, the adsorption characteristics of intermediates involved in the synthesis reaction were studied on a theoretical 15-atom cluster of iridium. Characteristics studied here include bond energies, bond lengths, spin densities, and free and adsorbed vibrational frequencies for the following set of molecules: N2, N, NH, NH2, NH3, N2H, N2H2, N2H3, N2H4, H2O, and OH radical. Objective one was to generate useful information that will set the basis for a mechanistic study of the synthesis process. Objective two was to use these simulations to explore the use of dispersion-corrected Density Functional Theory methods that can model N2 adsorption – the key reactant for electrochemical ammonia synthesis via transition metal catalysis. Specifically, three methods were tested: hybrid B3LYP, a dispersion-corrected form B3LYP-D3, and semi-empirical B97-D3. The latter semi-empirical method was explored to increase the accuracy obtained in vibrational analysis as well as reduce computational time. Two lattice surfaces, (111) and (100), were compared. The adsorption energies were stronger on (100) and follow the trend EB3LYP > EB3LYP-D3 > EB97-D3 on both surfaces. In the conclusion of this thesis, recommendations are given on how this data may be used to support a future study in pursuit of a mechanism for ammonia synthesis on iridium.

The detrimental environmental impact of excessive use of petroleum and fossil-fuel has motivated efforts to manufacture chemicals and fuels from biomass, a renewable feedstock. In addition, a large emphasis has been placed on developing chemistries that are not only atom-efficient, but also involve one-pot synthesis, green solvents, and easy separations, thereby, driving next generation technologies towards sustainability and green manufacturing. To that effect, this dissertation elucidates reaction mechanisms of such new chemistries, identifies catalytic features that enable fast, yet selective chemical transformations, as well as explores the complex role of greener solvents in mediating reactions, in a series of collaborative studies involving density functional theory (DFT) simulations and experiments. Biomass-derived precursors are shown to possess chemical and structural flexibility to generate a novel slate of structurally diverse intermediates that can be used as scaffolds in the synthesis of current chemical feedstocks as well as new molecules with advanced functionality for the design of new chemical and materials intermediates. Chemical diversification is demonstrated by using heterogeneous catalysts together with solvents to achieve high reactivity and selectivity. The results presented in this dissertation aim to guide the design of more active, selective, stable and cheaper catalytic materials, as well as synthesis of new molecules and establish new strategies to control the specific products that are formed. A significant part of this dissertation focusses on chemically diversifying coumalic acid (CMA)- a bio-privileged molecule, using a selective, one-pot Diels-Alder-decarboxylation-dehydrogenation domino sequence to generate a library of aromatic compounds. Not only petroleum-derived aromatics, but also a host of novel structures can be synthesized using this approach. These new aromatics have functionalities and structures inaccessible via petroleum and have applications as pharmaceuticals, insecticides, and antimicrobials. Here, we combine in-situ NMR, DFT, and solid-state NMR to guide one-pot synthesis of aromatics from biomass, thus, providing a sustainable means to access these compounds. Our efforts lie at engineering the reactivity by identifying the rate-limiting step in the domino sequence and then subsequently catalyzing it using a heterogeneous catalyst. Selectivity is fine-tuned by esterification of CMA as well as by screening different solvents. Finally, during synthesis of more complex aromatics, structure-reactivity relationships are developed to guide stereo-selective synthesis. This dissertation also discusses aldol condensation and esterification, reactions that are can be utilized to make longer molecules from bio-derived oxygenates to synthesize fuel-grade precursors and additives respectively. Previous studies have shown that these reactions can be catalyzed when pre-equilibrated mixture of alcohol-aldehyde-H2 is fed over metallic Cu catalysts. Here, we reveal the nature of active sites on metallic Cu that enable C-C and C-O bond formation reactions that traditionally been thought to require explicit base or acid assistance. DFT simulations, together with kinetic analyses show the prevalence of Lewis-basic alkoxide sites as well as Lewis-acidic aldehyde sites that co-operatively carry out proton and hydride transfer reactions that enable C-C and C-O bond formation reactions. The studies not only demonstrate the use of greener, earth-abundant transition metals such as Cu in biomass processing, but also opens opportunities to utilize active sites unique to Cu, and possibly other coinage metals, in reduction reactions that selectively require hydrides (for example, C-H formation). Reactions that allow for selective C-O bond hydrogenolysis are central to biomass upgradation. This dissertation also reports hydrogenolysis of fatty acid over bimetallic rhenium oxide catalysts promoted by Pd. Such bimetallic catalysts allow for near-atmospheric pressure operations, contrary to commercial processes. The role of the constituent metals is elucidated here through DFT and multiple experimental probes including steady state and transient rate measurements, extensive characterization, and kinetic isotope effects. The results demonstrate a synergistic role of Pd and Re; while Pd directly provides hydrides (electrons) for C-H formation, Re catalyzes dehydration reactions by providing protons via Brønsted acid sites (ReOH). These results can be extended to over oxophilic metals to screen more selective, cheap and durable materials in the processing of fatty acids and other oxygenates. The last part of this dissertation highlights the role of protic solvents in mediating reduction reactions of oxygenates. Reduction reactions are not only at the heart of biomass upgradation, but also are involved in a host of important applications such as organic and pharmaceutical synthesis. The use of greener solvents such as water, methanol or their mixtures instead of organic solvents is of critical importance from an environmental viewpoint. Here we combine DFT, kinetic isotope effects, flow reactor measurements, and in operando X-ray spectroscopy to show that protic solvents directly participate in hydrogenation reactions over transition metal surfaces. Protic solvents are shown to activate characteristically different routes that can enable transfer of hydrogens as protons and electrons in proton coupled electron transfer (PCET) reactions that favor the formation of O-H bonds on oxygenates. In such reactions, solvent molecules react with co-adsorbed hydrogen atoms on the metal catalyst as well as enable proton-shuttling, while the metal catalyst is found to conduct electrons. In addition, we present evidence for the prevalence of heterogeneous mediator-based chemistry that governs reduction reactions when the protic solvent can decompose on the host catalyst. The structure and reactivity of these heterogeneous surface mediators are found to resemble well-known solution-phase mediators such as TEMPOH (in organic synthesis) and NADPH (in biology). These results demonstrate the ability to impact selectivity during hydrogenation of oxygenates and perhaps other unsaturated compounds by tuning the metal and the protic solvent.

This dissertation focuses on the use of precious metals in ultra-high vacuum to get a better mechanistic understanding of industrially-relevant processes. Ethanol (EtOH) is an excellent candidate as an alternative energy source because the infrastructure is already constructed to effectively distribute it as a liquid and because processes are continuously improving to make EtOH from non-edible biomass. Therefore, we explored the dehydrogenation, decomposition, and oxidation of EtOH on a Pd-Au alloy surface in ultra-high vacuum using quadrupole mass spectrometry, Auger electron spectroscopy, and reflection-absorption infrared spectroscopy coupled with support from theoretical calculations. With growing interest in a hydrogen fuel economy, dehydrogenation from EtOH can serve as an alternative to produce H₂. With molecular beam experiments and DFT calculations, we correlated mechanistic differences of EtOH dehydrogenation with changes in the Pd ensemble size on the Au(111) substrate. Next, we investigated the decomposition of EtOH and observed preferential C-C bond breakage over that of the C-O on our Pd-Au catalysts. A major hindrance to direct ethanol fuel cells is incomplete dehydrogenation, oftentimes yielding acetaldehyde as an unwanted byproduct. Therefore, gaining insight on how EtOH decomposes is beneficial to designing better catalysts. Additionally, we investigated the oxidative self-esterification of ethanol to produce ethyl acetate. Here, we observed how the presence of oxygen and hydroxyl group enables facile EtOH dehydrogenation to acetaldehyde, enabling cross-coupling to form an ester. In whole, the compilation of my work is an example of how mechanistic understanding in controlled environments can lead to better catalytic design for real-world applications.

Color additive molecules have widespread applications ranging from ingestible foods and pharmaceutics to non-ingestible cosmetics and other naturally or synthetically developed consumer products available worldwide. Certification for approved use of color additives varies globally; therefore, a feasible method to analyze existing color additives or to design novel color additive molecules with enhanced or otherwise desired physicochemical properties (such as hue) is in high demand for universal adoption. The studies herein provide sufficient proof that density functional theory and time-dependent density functional theory serve as effective predictive modeling techniques for generating theoretical maximum absorbance spectral peak responsivity for a single color additive molecule structure in the virtual workspace, as well as for multiple (heterodimeric and heterotrimeric) structures represented simultaneously. Furthermore, DFT and TD-DFT can be used to analyze changes in hue attributed to structural anomalies in molecules due to tautomerism, vibronic effects, intra- or intermolecular interactions, implicit or explicit solvation effects, or charge transfer effects on the structure represented in a given solvent or in vapor phase. Advancements in computational processing make incorporation of these and similar advanced ab initio quantum chemical methods more tangible for the modern pharmaceutical or cosmetic formulator to use in perfecting batch hue.

As scientists are pushing the limit of technology, experimental trial and error explorations are becoming increasingly unaffordable, especially when accurate manipulation of materials at the atomic level is essential. The need for theoretical oversight is ever increasing while traditional empirical theories are becoming obsolete. Density Functional Theory (DFT) is therefore developed as a portable theoretical tool that can shed light on many different fields based on first principle quantum mechanics. It is enjoying increasing popularity for its ability to provide information complementary to experimental characterization methods, as well as its astounding prediction power that can potentially vastly increases experimental output. In this dissertation, I showcase how DFT can be used to support experimental endeavors by providing insights and validations otherwise unobtainable and deepen our understanding of a variety of subjects. DFT calculations are also used to validate and explain experimental characterization results. For isolated Pt atom and Pt clusters with few atoms, the Pt dband center and hydrogen binding energy were calculated, used in conjunction with cyclicvoltammetry data to characterize the Pt atom/clusters and explain the observed activity towards hydrogen evolution reaction. For methodology development, DFT calculations are used to provide a plausible mechanism for the technique of hydrogen elimination monitoring in ultraviolet photodissociation of proteins for mass-spectroscopy to solve protein structures. It shows that depending on the degree of hydrogen bonding engagement, backbone cleavage can take place first and prevent succeeding hydrogen transfer. The results explained why the technique is a reliable method for finding protein structure information. Towards the development of materials, DFT calculations are used to find reaction mechanisms for hydrogenation using carbon-nitrogen-phosphorous pincer Fe catalysts and to find causes for geometry change for post-synthetically modified metal-organic frameworks

Vertical van der Waals heterostructures created from two chemically different two-dimensional (2D) materials have made significant headway in the current electronic manufacturing landscape. They demonstrate enhanced electronic properties and have the potential to be used in high performance electronic devices. Among the various van der Waals heterostructures, molybdenum disulfide (MoS2) thin film on an indium arsenide (InAs) substrate has shown promising opto-electronic properties. This hybrid heterostructure gives stronger optical absorption properties than a 2D layer of graphene on InAs due to smaller equilibrium spacing. To realize the true potential of these heterostructures, it is imperative to understand not only their stability, but also the thermodynamic stability of various defects at the heterointerface. We have performed first principles density functional theory calculations to study MoS2(100)/InAs(111) heterostructures with two different terminations of the substrate. We will discuss the thermodynamic stabilities of In-terminated and As-terminated heterostructures and shed light on the stabilities of S, In, and As vacancy defects at the heterointerface. Overall, our results offer insights into the fundamental role of defects in van der Waals heterostructures.

Aqueous-phase heterogeneous catalysis is an important chemical process in applications such as water remediation, fuel cells, and the production of fuels and chemicals, including from biomass sources. However, designing alternative, improved catalyst materials for these applications is difficult due to fluctuations in the solvation environment surrounding the catalytic species. In order to elucidate the thermodynamics and kinetics of the relevant reactions, it is imperative to gain a better understanding of the roles of liquid water molecules in these reactions. In this work, a method combining both classical and quantum simulations was developed to generate configurations of liquid water molecules over catalytic species adsorbed on a catalyst surface, which can provide valuable insight into the roles of the liquid water reaction environment on aqueous-phase heterogeneous catalysis. The method developed in this work entails combining force field molecular dynamics (FFMD) and density functional theory (DFT) simulations. This method leverages the strengths of each type of simulation to enable the calculation of catalytic energies under “realistic” liquid water configurations. FFMD simulations are used to generate trajectories of liquid water configurations that include thermal fluctuations, while DFT simulations are used to capture the energies associated with bond breaking and forming that are required for microkinetic modeling and catalyst design studies. This FFMD-DFT method was used to calculate the interaction energies between the liquid water environment and the reaction intermediate or transition state species. The trend in the calculated interaction energies was shown to correlate with the trend in hydrogen-bond formation between liquid water molecules and the catalytic species. This work also demonstrated that entropic effects due to the thermal fluctuations in the solvation environment are a significant contribution to the free energies calculated for aqueous-phase, heterogeneously-catalyzed systems. The FFMD-DFT method was also used to calculate reaction energies, activation barriers, and pre-exponential factors to study the kinetics of example O—H and C—H cleavage reactions on a platinum catalyst surface under an aqueous reaction environment. Using this method, it was found that O—H cleavage reactions prefer H2O-mediated pathways, while C—H cleavage reactions prefer non-H2O-mediated pathways. In summary, the FFMD-DFT method developed in this work has been shown to be a robust technique for generating realistic liquid water configurations over catalytic species on a platinum catalyst surface. Those liquid water configurations can be used to calculate catalytic properties that can provide insight into the roles of water molecules in these reactions and facilitate microkinetic modeling and catalyst design studies.

Computational chemistry has gained interest as a characterization tool to predict photoluminescence, reactivity and structural properties of organic and transition metal complexes. With the rise of methods including relativity, these studies have been expanded to the accurate modeling of luminescence spectra of complexes with considerable spin-orbit splitting due to heavy metal centers as well as the reaction pathways for these complexes to produce natural products such as hydrogen gas. These advances have led to the synthesis and utility of more effective catalysis as well as the development of more effective organic light emitting diodes (OLEDs) through the incorporation of organometallic complexes as emitters instead of typical organic emitters. In terms of significant scientific advancement presented in this work is in relation to the discovery of significant spin-orbit splitting in a gold(I) alkylphosphine complex, where the splitting results in the states that emit in different colors of the visible region of the electromagnetic spectrum. This work also reveals the discovery both computationally and experimentally, of a genuine polar-covalent bond between two-closed shell metals. This work highlights a complex with an incredibly short gold(I) – copper(I) intermetallic distance leading to a vibrational frequency and dissociation energy that is on par with those of other systems with single-bonded metal centers. Lastly, this work outlines a strategy for the production of hydrogen gas through the use of trinuclear cyclic coinage metal complexes as catalysis to split hydrohalic acids.

This work discusses the C-H bond activation by transition metal complexes using various computational methods. First, we performed a DFT study of oxidative addition of methane to Ta(OC2H4)3A (where A may act as an ancillary ligand) to understand how A may affect the propensity of the complex to undergo oxidative addition. Among the A groups studied, they can be a Lewis acid (B or Al), a saturated, electron-precise moiety (CH or SiH), a σ-donor (N), or a σ-donor/π-acid (P). By varying A, we seek to understand how changing the electronic properties of A can affect the kinetics and thermodynamics of methane C–H activation by these complexes. For all A, the TS with H trans to A is favored kinetically over TS with CH3 trans to A. Upon moving from electron-deficient to electron-rich moieties (P and N), the computed C–H activation barrier for the kinetic product decreases significantly. Thus, changing A greatly influences the barrier for methane C–H oxidative addition by these complexes. Secondly, a computational study of oxidative addition (OA) of methane to M(OC2H4)3A (M = Ta, Re and A = ancillary ligand) was carried out using various computational methods. The purpose of this study was to understand how variation in A and M affects the kinetics and thermodynamics of OA. Results obtained from MP2 calculations revealed that for OA of CH4 to Re(OC2H4)3A, the order of ΔG‡ for a choice of ancillary ligand is B > Al > SiH > CH > N > P. Single point calculations for ΔG‡ obtained with CCSD(T) showed excellent agreement with those computed with MP2 methods. MCSCF calculations indicated that oxidative addition transition states are well described by a single electronic configuration, giving further confidence in the MP2 approach used for geometry optimization and ΔG‡ determination, and that the transition states are more electronically similar to the reactant than the product. Thirdly, a computational study of olefin polymerization has been performed on 51 zirconocene catalysts. The catalysts can be categorized into three classes according to the supporting ligand framework: Class I - Cp2ZrCl2 (ten catalysts), Class II - CpIndZrCl2 (thirty-eight catalysts), and Class III - Ind2ZrCl2 (three catalysts), Cp = η5-cyclopentaidenyl, Ind = η5-indenyl. Detailed reaction pathways, including chain propagation and chain termination steps, are modeled for ethylene polymerization using Class II catalysts. Optimized structures for reaction coordinates indicated the presence of α-agostic interactions in the transition states (TSs) for both the 1st and 2nd ethylene insertions as well as in the ethylene π-complex of the Zr-nPr cation. However, β-agostic interactions predominate in the cationic n-propyl and n-pentyl intermediates. The calculated relative Gibbs free energies show that the TS for insertion of ethylene into the Zr-CH3+ bond is the highest point on the computed reaction coordinates. This study, in concert with previous work, suggests that the type of ring attached to Zr (Cp vs. Ind) affects the reaction kinetics and thermodynamics less significantly than the type of substituents attached to the Cp and indenyl rings, and that substituent effects are even greater than those arising from changing the metal (Zr vs. Hf)

Materials with features at the nanoscale can provide unique mechanical properties and increased functionality when included as part of a nanocomposite. This dissertation utilizes computational methods at multiple scales, including molecular dynamics (MD) and density functional theory (DFT), and the coupled atomistic and discrete dislocation multiscale method (CADD), to predict the mechanical properties of nanocomposites possessing nanomaterials that are either 1-D (carbyne chains), 2-D (graphene sheets), or 3-D (Al/amorphous-Si core-shell nanorod). The MD method is used to model Ni-graphene nanocomposites. The strength of a Ni-graphene nanocomposite is found to improve by increasing the gap between the graphene sheet and a crack embedded in the Ni matrix. Ni-graphene nanocomposites also show substantially greater strength than pure Ni, depending on the loading direction and crack orientation relative to the graphene sheet. Moreover, polycrystalline graphene may serve as a better reinforce in Ni-graphene nanocomposites due to its improved interfacial shear stress with the Ni matrix compared to pristine graphene. This work develops a patchwork quilt method for generating polycrystalline graphene sheets for use in MD models. Carbyne-based nanocomposites are modeled from first principles using DFT. This research finds that carbyne can only serve as an effective reinforcement in Ni-based nanocomposites when it is dielectrically screened from the Ni matrix, otherwise the carbyne structure is lost. When graphene is used as a dielectric screen, the local stiffness of the nanocomposite improves with the number of carbyne chains present. Specific stiffness is introduced as an alternative to elastic stiffness for characterizing low-dimensional materials because it is not dependent on volume when derived using an energy vs. strain relation. A two-material formulation of CADD is developed to model Al/a-Si core-shell nanorods under indentation/retraction. The structural deformation behavior is found to be dependent on the geometry of both core and shell. When present, the a-Si shell protects the Al core by delocalizing forces produced by the indenter. It is also found that substrate deformation becomes important for core-shell structures with sufficiently small cores. This work can help guide experimental and computational work related to the discussed 1-D, 2-D and 3-D nanomaterials and aid in future nanocomposite design.

Titanium (Ti) and its alloys have a wide range of applications in biomedical, automotive and aerospace industries due to their excellent strength to weight ratio and corrosion resistance. Alpha phase Ti has hexagonal closed packed (hcp) structure that shows anisotropic plastic deformation; 〈 a 〉 type slip on prism planes is the easiest to activate but cannot accommodate deformation along the 〈 c 〉 axis. The low temperature ductility of Ti is linked to twinning. Therefore, understanding the mechanisms behind the twin nucleation and growth in Ti alloys is important from both theoretical and industrial application points of view. To that end, the present study seeks a better understanding of the atomic scale processes involved in twin nucleation mechanisms and the effect of alpha-stabilizing solutes such as interstitial oxygen, substitutional aluminum and rare earth elements on twinning. Systematic molecular dynamics (MD) simulations are used to identify the underlying mechanism of twin nucleation from dislocation/grain boundary interactions. Density functional theory (DFT) simulations are employed to examine the effect of oxygen interstitials on the twinning behavior of Ti. A systematic framework has been developed to predict the diffusion of interstitial elements near the twin boundaries in hcp alloys. Next, uncertainty that arises from first-principles calculations in predicting diffusion coefficients are quantified. Finally, solute segregation to the twin boundaries as a new mechanism for dynamic strain aging (DSA) is investigated in Ti and other hcp alloys.

Internal combustion engine vehicles (ICEVs) rely on petroleum-based fuels; these fuels have limited reserves and their combustion is a major contributor to climate change. These issues have triggered the development of electric vehicles (EVs) that use electricity stored in Li-ion batteries as a fuel. However, the performance of current Li-ion technology has largely plateaued, yet the driving range of EVs remains below that of conventional ICEVs. This limitation has sparked a search for safer battery chemistries with higher capacities than Li-ion at lower cost. The lithium-sulfur (Li-S) battery is one promising chemistry due to its high theoretical energy density and potential for low cost. Despite these benefits, Li-S batteries are not commercially feasible yet because their cycleability and practical energy density do not meet expectations. The limited performance is attributed to deficient understanding of several phenomena, including slow transport in the positive electrode, dissolution of polysulfide species, and the cycleability of Li metal anodes. This thesis aims to provide insight into these mechanisms using density functional theory (DFT) calculations, and thereby provide information aiding the design of Li-S systems. As a first step in understanding the fundamental features of Li-S batteries, the state-of-art DFT techniques were applied to predict several properties of redox end members in Li-S batteries. More specifically, the relative stabilities of the α-and β- sulfurs were confirmed by calculating the phase diagram. Similarly, the stability analysis of Li2S2 suggests that this is a metastable phase. The equilibrium crystallites of Li2S are predicted to be comprised entirely of stoichiometric (111) surfaces, while for α-S a mixture of several facets is predicted. Finally, α-S, β-S, Li2S, and Li2S2 are predicted to be insulators with band gaps greater than 2.5 eV. Regarding PS dissolution from the cathode, metal-organic frameworks (MOFs) are explored as cathode support materials. MOFs combine encapsulation and chemical adsorption as strategies for minimizing PS dissolution. Optimal MOF compositions are pinpointed by computationally screening 16 metal-substituted variants of M2(dobdc) for their ability to chemically anchor prototypical PS species. Ti2(dobdc), Ni2(dobdc), and Mo2(dobdc) are identified as the compositions with the largest affinities for Li2S4 and Li2S. As Ni2(dobdc) has been synthesized previously, this MOF is proposed as a promising cathode support for Li-S batteries. Charge transport limitations through insulating Li2S and α-S have the potential to constrain the capacity of Li-S batteries. Therefore, understanding these charge transport mechanisms is a prerequisite for enhancing cell performance. Charge transport mechanisms were explored in both the adiabatic and nonadiabatic regimes. Charge transfer in Li2S is predicted to be adiabatic. In sulfur, however, transitions between S8 rings are nonadiabatic and conventional DFT overestimates charge transfer rates. Delocalized holes are predicted to be the most mobile charge carriers in α-S; in Li2S hole polarons dominate. Finally, the last portion of this dissertation examines sulfide-based solid electrolytes (SSEs). These electrolytes are expected to enable the use of a high-capacity Li-metal anode. Realizing these systems requires stability between the SSEs and Li metal. This stability is investigated computationally by predicting band edge positions. Based on this information we predict the likelihood charge injection from Li anodes. Our calculations reveal that reduction by Li is expected for all SSEs examined. The position of the CBM is sensitive to the surface features of the SSE and varying the surface composition of Li3BS3 can stabilize the Li/Li3BS3 interface.

Using computational simulations to model reaction mechanisms has been a common way to garner kinetic and thermodynamic information of a chemical system that can help to understand reaction rates and selectivities. The most widely used tools available for reaction path modeling, however, lend themselves to working best when chemical intuition or experimental data for a chemical reaction is available to help build the simulation. In order to model reaction mechanisms of chemical systems that have not been well studied, or even tested experimentally yet, new tools for simulation are needed that do not rely on chemical intuition or experimental data. Herein, new methods developed for reaction discovery are laid out and applied to understanding transition metal catalyzed reactions on a deeper level. The first tool, ZStruct, allows for the systematic exploration of chemical space for unimolecular and bimolecular reactions for both organic and organometallic reactions. This method requires minimal user guidance, with the only prior knowledge required being that of which atoms on each reactant may be participating in bond forming/breaking processes. This method has been applied to explore the following reaction: (1) methane activation by cisplatin, where 10 previously unexplored reaction pathways were identified. (2) Ni(II)-catalyzed beta-hydride elimination, where off cycle Ni-THF or Ni-pyridine intermediates were found to be thermodynamically favorable. (3) Palladium-catalyzed piperidine arylation, where the full catalytic cycle, including the rate limiting C-H activation step, as well as roles of important Ar-I and Cs-salt additives, were elucidated through ZStruct’s ability to explore chemical space. The second tool, CGen, allows for sampling conformational changes that reactants of interest can undergo, and use them to generate metal-reactant complexes. Using the conformers that are generated, reaction discovery using ZStruct can be done to understand how conformational changes impact the mechanism by which a reaction occurs. Additionally, CGen can also be used to create catalyst-reagent complexes (reagents could be counterions, solvent molecules, or any other molecular additives used experimentally) by aligning a catalyst and reagent of interest in different orientations, thus allowing for better sampling of molecular interactions during a reaction. CGen was used to explore the chemical space of ethylene polymerization with a Ti-constrained geometry catalyst (Ti-CGC). With this system the impact of the polymer chain conformation on the mechanism to ethylene insertion was investigated. The favorable pathways to insertion that were found involve polymer chain conformations that maximize the distance between Ti and the polymer chain. The barriers to insertion found with the naked cation system were lower than experimental results, implying that inclusion of the experimentally required borane counterion is important for generating a model that reflects experimental observations. Therefore, the same reaction was remodeled once with Me-B(C6F5)3- and again with B(C6F5)4-, and in both cases CGen was used to generate Counterion-catalyst complexes. The sampling of various counterion alignments and polymer chain conformations demonstrated that alignment/conformer combinations can actually impede monomer insertion from occurring. Additionally, the models showed that the difference in nucleophilic strength between the two counterions impact how energetically challenging monomer uptake prior to insertion is, as well as how each counterion positions itself with respect to the catalyst during monomer insertion. The insights gained using reaction discovery methods in this work gave way to a deeper understanding of how chemical interactions in-situ may be hindering or helping a chemical reaction to occur.

Density functional theory (DFT), in its ground-state as well as time-dependent variant, have enjoyed incredible success in predicting a range of physical, chemical and materials properties. Although a formally exact theory, in practice DFT entails two key approximations---(a) the pseudopotential approximation, and (b) the exchange-correlation approximation. The pseudopotential approximation models the effect of sharply varying core-electrons along with the singular nuclear potential into a smooth effective potential called the pseudopotential, thereby mitigating the need for a highly refined spatial discretization. The exchange-correlation approximation, on the other hand, models the quantum many-electron interactions into an effective mean-field of the electron density ($rho(mathbf{r})$), and, remains an unavoidable approximation in DFT. The overarching goal of this dissertation work is ---(a) to develop efficient numerical methods for all-electron DFT and TDDFT calculations which can dispense with the pseudopotentials without incurring huge computational cost, and (b) to provide key insights into the nature of the exchange-correlation potential that can later constitute a route to systematic improvement of the exchange-correlation approximation through machine learning algorithms (i.e., which can learn these functionals using training data from wavefunction-based methods). This, in turn, involves---(a) obtaining training data mapping $rho(mathbf{r})$ to $v_text{xc}(mathbf{r})$, and (b) using machine learning on the training data ($rho(mathbf{r}) Leftrightarrow v_text{xc}(mathbf{r})$ maps) to obtain the functional form of $v_text{xc}[rho(mathbf{r})]$, with conformity to the known exact conditions. The research efforts, in this thesis, constitute significant steps towards both the aforementioned goals. To begin with, we have developed a computationally efficient approach to perform large-scale all-electron DFT calculations by augmenting the classical finite element basis with compactly supported atom-centered numerical basis functions. We term the resultant basis as enriched finite element basis. Our numerical investigations show an extraordinary $50-300$-fold and $5-8$-fold speedup afforded by the enriched finite element basis over classical finite element and Gaussian basis, respectively. In the case of TDDFT, we have developed an efficient emph{a priori} spatio-temporal discretization scheme guided by rigorous error estimates based on the time-dependent Kohn-Sham equations. Our numerical studies show a staggering $100$-fold speedup afforded by higher-order finite elements over linear finite elements. Furthermore, for pseudopotential calculations, our approach achieve a $3-60$-fold speedup over finite difference based approaches. The aforementioned emph{a priori} spatio-temporal discretization strategy forms an important foundation for extending the key ideas of the enriched finite element basis to TDDFT. Lastly, as a first step towards the goal of machine-learned exchange-correlation functionals, we have addressed the challenge of obtaining the training data mapping $rho(mathbf{r})$ to $v_text{xc}(mathbf{r})$. This constitute generating accurate ground-state density, $rho(mathbf{r})$, from wavefunction-based calculations, and then inverting the Kohn-Sham eigenvalue problem to obtain the $v_text{xc}(mathbf{r})$ that yields the same $rho(mathbf{r})$. This is otherwise known as the emph{inverse} DFT problem. Heretofore, this remained an open challenge owing lack of accurate and systematically convergent numerical techniques. To this end, we have provided a robust and systematically convergent scheme to solve the inverse DFT problem, employing finite element basis. We obtained the exact $v_text{xc}$ corresponding to ground-state densities obtained from configuration interaction calculations, to unprecedented accuracy, for both weak and strongly correlated polyatomic systems ranging up to 40 electrons. This ability to evaluate exact $v_text{xc}$'s from ground-state densities provides a powerful tool in the future testing and development of approximate exchange-correlation functionals.

A societal shift towards greater adoption of renewable energy is underway. To accelerate this transition, new approaches for energy storage are needed to address the intermittent nature of these resources. Batteries are amongst the most promising energy storage devices. State-of-the-art lithium-ion (Li-ion) batteries are beginning to be deployed in applications such as transportation and for grid energy storage. However, Li-ion technologies have yet to be applied widely in these applications due to their limited energy storage capacities. Promising next-generation battery chemistries, such as magnesium (Mg) and lithium metal batteries, have the potential to improve capacities by as much as an order of magnitude over current Li-ion cells. However, metallic anode materials suffer from detrimental interactions with the battery electrolyte. This dissertation analyzes the unique challenges for Mg and Li metal batteries at the anode/electrolyte interface using first-principles computation. In Mg metal batteries, a key challenge is electrolyte decomposition at the Mg anode surface. DFT calculations are performed to predict both the thermodynamic driving force and kinetics of plausible decomposition reactions of DME, a common solvent, on three Mg anode surface phases: Mg metal, MgO, and MgCl2. DME is predicted to rapidly decompose to ethylene gas and other products on the metallic Mg surface, whereas the presence of an oxide or chloride surface film on a Mg anode is predicted to limit solvent decomposition. The stability of the Cl-based surface may explain how Cl additions to an electrolyte contribute to improvements in the anode performance via a Mg−Cl enhancement layer. In Li-based batteries, a solid electrolyte interphase (SEI) layer is known to form on the anode, but the detailed composition and structure of the SEI, along with its evolution upon battery cycling, remains a matter of debate. In batteries that use metallic lithium as an anode, dendrite formation during Li plating presents an additional and major failure mode. These challenges are addressed from two angles. First, one potential solution to these challenges is to employ a protective membrane at the Li anode. For such membrane to function, strong adhesion or wetting of the membrane by Li must occur. Using first-principles calculations, the adhesive properties of graphene oxide (GO), a promising membrane material, are investigated at an interface with lithium. These calculations indicate that Li strongly adheres to the GO surface, supporting the use of GO as a protective layer. Second, although the composition of the SEI varies across electrolytes, the native oxide Li2O is an omnipresent component that comprises the innermost SEI layer in virtually any battery employing a Li metal anode. Despite its ubiquity, the properties of this native oxide layer, and its interface with underlying Li metal, have not been widely examined. Here rigorous, first-principles models of the native oxide layer on Li metal are developed. Two models are constructed and analyzed: an ideal crystalline interface, and an amorphous model in which the oxide layer is ‘grown’ by step-wise oxidation of Li metal. Quantitative analyses are presented that distinguish the change in electronic structure of Li atoms across the interface, differentiating metallic Li and oxidized Li ions. Finally, Li-ion diffusivities within the oxide are computed; the data support the fast transport of Li ions through the oxide layer. In total, these calculations provide a highly detailed description of the structural, transport, and mechanical properties of the Li/Li2O interface.

Alloyed semiconductor systems can provide improved properties beyond their unalloyed counterparts. Atomistic modeling of such systems was previously infeasible, but increases in computational power and improvements in modeling methods for random alloys have opened up this field of study, as well as unalloyed systems with natural disorder. Using these techniques, we analyze the structure-composition-property relationships in many technologically relevant alloyed and disordered semiconductor systems. First, we show the band gap insensitivity of the solid-state Li electrolyte LLZO to tantalum and aluminum doping. Next we show that antisite defects play a crucial role in ferromagnetic ordering within the high Tc ferromagnetic semiconductor FeSb2Se4. We then predict the large absorption coefficient and optimal band gap of Cu4TiSe4 for function as a thin film solar cell absorber material, but also its tendency to form copper-related defects that would hinder functionality as a photovoltaic. We analyze new inorganic compounds, SrHfSe3 and (Ge, Sn, Pb)(S, Se, Te). Finally, we explore boron incorporation in group-III nitrides. We show that the incorporation of boron into InGaN, the material that is the active layer in blue LEDs and won the 2014 Nobel prize in physics, can maintain the electronic properties of InGaN while better matching its structural properties to the surrounding GaN. This will allow for thicker active layers to be grown in devices, improving overall device efficiency and reducing device size. Additionally, BAlGaN alloys are shown to be easy to lattice match to AlN with band gaps similar to AlGaN alloys of the same gallium content, but with a smaller structural u parameter that correlates to a TE polarized light emission that is easier to extract from LEDs. Additionally, we analyze the polarization of BAlGaN alloys. Lastly, we also use machine learning on combined experimental and computational data for exploring vast chemical spaces for potential scintillator host materials. By adding local structural information into a machine learning model, we show improved ability to predict doping Ce d energy levels inside various host materials.

In this thesis a framework for quantum transport simulation from first principles is introduced, focusing on coherent electronic transport, but discussing the importance of electron-phonon interactions for transition metal dichalcogenides (TMDs) as well. The transport model is based on the non-equilibrium Green's function (NEGF) formalism which requires a localized basis set. To obtain a suitable Hamiltonian matrix for a given device structure, a representative unit cell is first identified and its electronic properties calculated with density-functional theory (DFT) expressed in a plane-wave basis. These results must be transformed into a localized basis set using so-called maximally localized Wannier functions (MLWFs). From the MLWF representation of the original unit cell, the device Hamiltonian can be constructed with the help of properly designed upscaling techniques. The main focus of this thesis lies in the interfacing part of the MLWF representation with a quantum transport code, OMEN in our case. To this end, a code called Winterface was developed. Elaborations of its concepts, algorithms, and general functionality are presented on the basis of a molybdenum disulfide (MoS_2) monolayer structure, as well as heterostructures involving tungsten disulfide (WS_2). The process of Hamiltonian upscaling was simplified through the application of a two-stage process, where the first step consists of a conversion of the MLWF Hamiltonian data into blocks corresponding to interactions along atomic bonds. This is the key innovation of this work. This does not only make the actual upscaling procedure, which represents the second step, more transparent, as opposed to an approach relying on raw MLWF data, but also allows for an investigation of the local properties of super structures. The latter can help shed light onto the influence of local features such as interface regions. On the basis of this, more complex device geometries can be generated and the corresponding Hamiltonians be approximately constructed. Examples of such super structures are provided with MoS_2 on top of WS_2 or both 2-D materials next to each other. The developed approach can be considered completely general, restricted only by the capability of the user to accurately model the developed physics in DFT and to "wannierize" plane-wave results, as well as the imposed computational intensity. Finally, the importance of the inclusion of electron-phonon interactions into the transport model is demonstrated on the basis of self-heating effects in MoS_2, WS_2, and black phosphorous devices. Due to the versatiliy of the NEGF formalism, this can be done conveniently through additional scattering self-energy terms. The interactions for the phonon part can also be derived from first-principles using density functional perturbation theory (DFPT). The electronic part results from multiple wannierizations using the concepts already introduced for the coherent case. Dissipative calculations present themselves as natural extensions of ballistic ones, but with a significant increase in computational burden.

Photocatalytic and photoelectrochemical water splitting can play an important role in future renewable energy systems by allowing solar energy to be stored in the form of hydrogen. Tungsten oxide (WO3) has some promising properties as a photocatalytic material, but as a simple binary oxide, is has limitations on its optoelectronic properties. In order to improve the visible-light absorption efficiency for photocatalytic applications, the band gap should be further reduced. Moving to ternary compounds can be a strategic method to achieve this, as ternary compounds provide a much greater variety in compositions and hence properties. In this work, the effect of introducing a second metal cation into tungsten oxide is studied by Density Functional Theory (DFT) calculations using the HSE06 functional. The compounds investigated include AWO4 tungstates (A = Sn, Fe), M2WO6 tungstates (M = Bi, Sb), tungstite (WO3·H2O) and hydrotungstite (WO3·2H2O). The band gaps are found to be 2.98 eV (WO3), 1.65 eV (SnWO4), 2.03 eV (FeWO4), 2.83 eV (Bi2WO6), 2.91 eV (Sb2WO6), 2.27 eV (WO3·H2O), 2.51 eV (WO3·2H2O). The band gaps of all the tungstates studied in this work are lower than that of binary WO3. The valence bands of these ternary oxides are formed by the oxygen 2p orbitals and the s orbitals of the second metal cations, which contribute to the band gap narrowing compared to WO3. Structural properties are also strongly correlated with the band energies; the spacing between layers of WO6 octahedra was found to be related to the conduction band edge, with larger spacing causing a shift in the band edge to a more negative potential compared to WO3. The tungstates studied are found to have either a small band gap (SnWO4, FeWO4, WO3·H2O and WO3·2H2O), and hence can potentially give a high efficiency for visible-light absorption, or a more negative conduction band edge than WO3 (Bi2WO6, Sb2WO6), which means they may be able to achieve the overall water splitting. The defect properties of several intrinsic defects in α-SnWO4 are also evaluated; a SnW antisite defect and oxygen vacancy (VO) are found to be the most two probable defects to form in α-SnWO4. Furthermore, SnW defects may lead to an enhancement of the photocatalytic performance under sunlight, while VO are expected to decrease the performance.

Molybdenum oxide (MoO3) has been shown to be an effective hole-selective contact layer for Si solar cells. However, the influence of intrinsic defects (e.g., vacancies) and extrinsic defects (dopants and contaminants) on the electrical properties of MoO3 contact layers has not been fully determined. There has also been little investigation into the possibility that Mo can diffuse from contact layers to detrimentally impact the carrier lifetimes in Si solar cells. In this thesis, the defect chemistry of MoO3 and Si is investigated using density functional theory (DFT) modelling. A novel method is outlined (including a rigorous definition of chemical potential and utilising an unconventional supercell) to calculate defect formation energies for all intrinsic defects in MoO3, resolving conflicting information arising from previous studies. The concentrations of all intrinsic defects is predicted under different pO2 and T conditions through the construction of DFT-informed Brouwer diagrams, identifying charge neutral O vacancies as the dominant intrinsic defect and providing a guide that allows for the stoichiometry of MoO3 ¬to be tuned to desired levels. The solubility and electronic effects of twelve potential dopants and contaminants in MoO3 are evaluated. Silicon is predicted to contaminate MoO3 at levels above 1.3 ppm under the commonly-used thermal evaporation conditions of 700 K and 10-6 atm pO2., introducing mid gap state that could increase parasitic absorption. While Cu, Zn and Se contamination of up to ppt are predicted to induce mid-gap defect states that can increase parasitic absorption, Mn/V may advantageously increase conductivity in MoO3 if introduced under low/high pO2 conditions, respectively. It is also shown that at preparation temperatures between 750 and 1000 K, Mo can detrimentally contaminate Si at concentrations > 1012 atoms/cm3, which is known to cause relative cell efficiency degradation of 20%. Importantly, more Mo contamination is predicted to occur in p-type than n-type Si which has implications for solar cell designs in industry. The diffusion mechanism of Mo in Si is confirmed to be dominantly interstitial, occurring faster in p-type than n-type Si. Finally it is demonstrated that Mo impurities in Si can bind with B, H and Cu, resulting in defect states that are likely to be more detrimental, raising concerns about p-type cells with B doping or utilising the ‘hydrogenation’ process used to deactivate B-O complexes in p-type Si solar cells.

Due to the unique feature of tunable size and surface chemistry, the optical and electronic properties of quantum dots (QDs) can be controlled, which helps to circumvent the challenges to turning these versatile materials into high-performance optoelectronic devices. However, a major bottleneck has prevented further development of devices with superior performance based on QDs, especially for small band QDs. It is the lack of a good understanding of surface chemistry on the electronic properties of QDs that limits their application.In this thesis, we have precisely controlled the surface chemistry of lead selenide (PbSe) QDs by replacing initial oleate with a group of thiophenol-based organic molecules. Consequently, we are able to successfully engineer the carrier type within PbSe QDs by varying the substitutional group on these thiophenol-based molecules.Field-effect transistors employing ligand-exchanged QDs are fabricated to study the charge transport properties and trap states within QD films. The results show that ligands with electron-donating substitutional groups give n-channel transistors while electron-withdrawing groups tend to yield p-channel transistors. X-ray photoelectron spectroscopy results confirm that the Fermi level tends to swing n-type for QDs with ligands containing electron-donating groups and p-type for withdrawing groups. This series of ligands shift valence band energy values spanning 2.0 eV for 6.5 nm QDs, which, by far, is the largest energy shift achieved through ligand exchange for PbSe QDs. Models of (111) plane PbSe slabs attached with different ligands are studied by First-principles calculations. The calculation shows a similar trend of band energy shift for semi-infinite 2D slabs passivated with different ligands. These ligands were also applied to 7.3 nm PbSe QDs to fabricate photodetectors used in the extended short-wavelength infrared region (2-3 μm). Preliminary results show that the adjustment of band energies and doping types have great impacts on the charge transport of these devices.In summary, we have developed a surface modification technique for tailoring the band energy and the doping type of QD films. Although the resulting films are not very conductive, the adjustable energy level with a 2.0 eV window enables these ligands applied for directing charge flow in QD optoelectronic devices.

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

A general method incorporating both vibrational and configurational degrees of freedom is proposed by expanding energy into vibrational clusters. Our approach captures vibrational properties in substitutional alloys with arbitrary atomic configurations which can serve as an accurate surrogate model for first-principles calculations. Compressive sensing is applied to robustly and accurately select the important configuration dependent force constants and determine their value by learning from first principles calculations in one shot. Unlike virtual crystal approximation which tends to overestimate lattice thermal conductivity at high concentration range. Great agreements with experimental results across all composition for PbTe-Se demonstrates our VCE method is an accurate approach to generate high fidelity potential energy surface across a wide range of alloy materials.

Investigating the mechanisms of phase transformation in t’-YSZ using first principle calculationsBySeyed Amirhossein SaeidiDoctor of Philosophy in Materials and Manufacturing technologyUniversity of California, Irvine, 2019Professor Daniel R. Mumm, ChairPower generation is one of the driving forces of economic growth. Gas turbine engines are one of the main power generation approaches that is in use stationary powerplants and propulsion systems in aviation and naval vessels. A basic design principle for gas turbine engines is that efficiency increases with increasing temperatures; as such, designs and materials allowing higher gas combustion temperatures are desired. To protect the metallic parts, ceramics with low thermal conductivity such as yttria stabilized zirconia (YSZ) are used as thermal barrier coatings. Use of new alternative fuels, with higher hydrogen content than conventional fuels, increase both the temperature and water production during combustion. This new extreme condition has shown to result in faster degradation of YSZ coatings. To understand the mechanisms behind this accelerated degradation, an understanding of the material behavior at the atomic scale under such exposure conditions is necessary, which is the focus of this work. First principles density functional theory (DFT) calculations are employed to study point defects and their interactions in the bulk of YSZ systems in different phase fractions and dopant concentrations. Cation vacancies and anion vacancy-cation vacancy pairs are investigated to determine the rate controlling defects in material aging processes. Equations derived from the grand canonical approach are used to determine the formation energy of defects. The effect of water vapor pressure, temperature and electronic structure of YSZ on these defects are studied. The change in formation energy of cationic defects and their concentrations as a function of the Fermi energy and water vapor pressure are reported. In addition, the possibility of changes in the main defect species responsible for the predominant cation diffusion at varying Fermi energies is presented. The results are used to propose a mechanism that can explain a series of experimental observations showing an acceleration of aging degradation of YSZ coatings under elevated water vapor exposures. The insights offered by this work advance our knowledge and predictive understanding of the aging process in t’-YSZ and give directions for future experimental and theoretical efforts directed at the design of more degradation resistant thermal barrier coatings.

The ability to precisely control and characterize the electronic structure of low-dimensional materials is essential for the realization of device logic beyond the limits of Moore’s Law. In addition, such materials can act as versatile testbeds for the study of fundamental condensed matter physics, including magnetism and superlattice physics. Due to the sensitivity of their electronic properties to atomic-scale structure, these reduced-dimensional materials must be fabricated and designed with atomic-precision. This dissertation focuses on the on-surface growth and electronic structure characterization of two-dimensional (2D) covalent organic frameworks (COFs) and one-dimensional (1D) graphene nanoribbons (GNRs) studied using low temperature (LT) scanning tunneling microscopy (STM) and spectroscopy (STS). The first part of this dissertation details the study of boroxine-linked COFs grown and characterized on both Au(111) and single-layer hexagonal boronitride (hBN) on Cu(111). On Au(111), we image the LDOS of unoccupied COF states of this prototypical class of COFs for the first time. These results show that the reduction of COF pore-size is associated with a confinement-based destabilization of unoccupied COF orbitals. For COFs studied on hBN/Cu(111), the addition of the insulating hBN layer significantly reduces the COF-substrate hybridization. This leads to a much more detailed spectral profile for STS conducted on boroxine-linked COFs, which we determine reflects the COF’s Kagome lattice. The second part of this dissertation focuses on several studies, each associated with growth and electronic structure characterization of a specific target GNR structure. The first section centers around a series of studies on boron-doped GNRs. We determine that boron substituents form two new in-gap orbital states that exhibit parity-dependent hybridization with the underlying Au(111) substrate. The next group of studies focuses on chevron-based GNR heterojunctions (HJs), including edge-doped carbazole and fluorenone systems, and wide/narrow junctions realized via hierarchical growth strategies. We were able to observe dopant-induced shifts in the GNR electronic structure and to uncover a subtle charge-transfer process in differentially-doped GNR HJs that ultimately determines the magnitude of HJ orbital energy offsets. In the next series of experiments, we demonstrate the ability to precisely control GNR topology in HJs of 7- and 9-atom-wide armchair GNRs (7-AGNRs and 9-AGNRs). Based on the specialized reaction geometry of a “linker” molecular precursor, we are able to realize topological junction states as single junctions, coupled double-junctions, and junction superlattices. The resulting electronic structures are observed to be well explained using basic 1D tight binding concepts and suggest a general route to engineering GNR electronic properties based on precise implantation of topological boundary states. Here we also discuss an intuitive realization of 1D GNR topology in terms of real-space charge position, with a simple mapping onto Clar Theory. The final study is a spinoff of the above-described topological superlattice, in which we embed a symmetric zero-mode superlattice into a semiconducting GNR backbone in order to realize metallicity in GNRs for the first time. Our results demonstrate general criteria for designing GNR metallicity and reveal strategies for engineering frontier bandwidth through control of sublattice polarization of zero-mode wavefunctions. The results presented in this dissertation reveal the broad range of tunable electronic structures accessible via atomically-precise on-surface synthetic strategies. In so doing, we are able to uncover general design principles for engineering low-dimensional materials at the smallest possible length scales.

Atomic scale imperfections, know as point defects, dictate the performance and efficiency of many modern energy materials. The challenges of climate change require the continued improvement and identification of materials which can have point defects engineered in a favorable fashion for the application of interest. With the rise of computer-aided materials design, the possibility of performing high-throughput, first-principles computation on point defects remains an attractive direction for improved screening of new energy materials. Yet major barriers have prevented the large scale implementation of point defect calculations in non-metals - namely, errors arising from the finite size of the computation cell, compounded with errors associated with the underestimation of the band gap. Moreover, the lack of an organized computational framework for storing and analyzing such calculations, as well as the lack of a reliable benchmark for understanding the quantities which can be reliably computed, have prevented high-throughput point defect calculations from being performed in a practical context. In this dissertation, the notion of performing first principles calculations of point defects in semiconductors and insulators in a high-throughput format is investigated. This begins with an overview of the theoretical requirements for performing first principles computation of defects, as well as the presentation of a set of open source command line tools for doing the same. Then three different application areas in the energy space - thermoelectrics for waste heat recovery, solid state electrolyte batteries, and solar cells - are explored with first principles calculations of point defects. Finally, scaling of the previously presented command line tools to a fully automated framework is demonstrated and used for a large benchmark study of fully-automated point defect calculations with semi-local functionals as compared to a set of previously published point defect calculations with hybrid-functionals. This benchmark work outlines the strengths and weaknesses associated with such an automation framework, and advocates for the use of such a framework for qualitative screening of doping limits and general carrier type for high-throughput computational discovery and design of new energy materials.

Due to increased availability and power of computational resources over the past few decades, prediction and design of novel materials using computational methods has become feasible. Simulation of material systems has become vital to the further realization of novel material systems. In order to ascertain physical properties, accurate determination and identification of stable crystalline structures is necessary. Additionally, further identification of novel properties, such as magnetic moments or orbital occupation, is necessary to further realize this goal. Global search methods provide a path to accurate prediction of these properties. In this dissertation, the Firefly algorithm and minima hopping methods are presented. The Firefly algorithm is applied to two problems: Magnetic moment orientation optimization and orbital occupation optimization. For the first, three noncollinear systems investigated: NiF2, a weak ferromagnet, and Mn3Pt and the molecular system (MnIV )3O4L4(H2O) , where L = N ,N-bis(methylene)-Z-1,2-ethenediamine, both of which display frustration. For the problem of orbital occupation, UO2 , an oxide, and KCoF3 , a perovskite-like antiferromagnet are investigated to show that FA works equally-well for d and f electron systems. The minima hopping method is applied to the problem of structural search in the binary system NiTi. The convex hull is constructed from this search. From this, results are compared to those previously reported.

Deep eutectic solvents are a new generation of ionic liquid-like solvents formed by combining hydrogen bond acceptor with hydrogen bond donor which result in the depression of the melting point of the solvent. Like ionic liquids, anions play a critical role in tuning the polarity, physicochemical properties, and thermodynamic behavior of deep eutectic solvent (DES). Choline chloride is the most widely used quaternary ammonium salt (QAS) in the literature and has remarkable advantages from reduced cost to low toxicity and volatility. Choline bromide and choline iodide are other QAS that have not been used often for DES synthesis and applications, probably with the opinion that chlorides form stronger hydrogen bonds. Developing new DES from these anions will broaden the scope of green solvents selection for diverse applications. The first objective of this dissertation looked into the synthesis and characterization of DES from choline chloride, choline bromide, and choline iodide with malic acid, malonic acid, and urea. Also, we studied the thermodynamic behavior of the solvents by measuring their vapor pressure, density, and infinite activity coefficient in polar and nonpolar solvents. The results show that choline bromide can sometimes be used to replace choline chloride because both QAS share comparable physicochemical behavior. In most cases, choline iodide forms weaker hydrogen bonding with the donors leading to the formation of a solid at room temperature. Nevertheless, all the solvents have melting temperature below 100℃. In summary, DES can be synthesized from the choline cation bonded with the halides, with the melting point and nature of the solvent dependent on the hydrogen bond donor (HBD). Secondly, despite the rapid rise in publications and applications since their inception in 2001, most of the DES synthesized are generally hydrophilic. The low cost, low toxicity, and bioavailability of DES make the solvent green and sustainable for diverse applications. Conversely, the hydrophilicity of DES practically limits their application to only polar compounds, which is a major drawback of the solvent. For the past three years, hydrophobic deep eutectic solvents (HDES) have emerged as alternative extractive media capable of extracting nonpolar molecules from aqueous environments. In chapter three of this dissertation, the general objective was to design a cost-effective hydrophobic DES from choline chloride and fatty acids. Varying the alkyl chain of the fatty acid broadened our understanding about the role of HBD in DES and also helped in the tunability of the HDES polarity. Due to the infancy of HDES, for the first time, this dissertation expands on the design, synthesis, and physicochemical characterization of HDES developed from choline chloride and fatty acids. To understand the hydrogen-bonding pattern of HDES, a multivariate unsupervised principal component analysis was used to cluster HDES by using known DES as a control. The HDES was able to extract about 70% of piperine, a bioactive compound from Piper nigrum. In the future, it is believed that HDES could replace the majority of toxic organic solvents used for analytical purposes. Lastly, the electronic and molecular properties of the HDES synthesized were studied by using a solvatochromic molecular probes and a hybrid density functional theory at 6-31G (d) basis set. The empirical polarity assay and quantum theoretical calculations showed that decreasing the alkyl chain length of the hydrogen bond donor increases viscosity of the DES. Optimization of the DES molecular geometry indicates a reduced bond angle between the C15-O16-H17 atoms in choline chloride, signifying a change in electronegativity of the central atom (O16) during DES formation. From our results, we predict a possible molecular reorientation between the donor and the acceptor molecules during DES formation. The combined theoretical calculations and experimental approaches are useful to establish clear correlations between electronic parameters and physiochemical parameters like polarity, viscosity, and stability of carboxylic acid-DES and can be extended to other conventional solvents.

Atom probe tomography (APT) produces 3D, atom-by-atom images of a sample microstructure that provide impressive detail of materials systems. With advancements in detector and laser technology in the last couple decades, APT has gained new life and has established itself as a premier technique in materials characterization. New laser technology has allowed for data collection rates of 100M atoms/hour or more while new detector technology has pushed detection rates up to and beyond 80%. The technique holds huge promise for providing a direct link between the actual atomic microstructure of a material and its properties. Being a destructive technique by nature, and ultimately relying on observing the surface of the sample, the atom probe suffers from artifacts in data that are sometimes not apparent. Reconstruction algorithms, too, have been hampered in adapting to these artifacts, as for many of them the mechanisms are simply not known. Development of physically complete, accurate forward simulations of the field evaporation process can provide both the mechanistic information and a testing framework necessary for future advances in reconstruction algorithms.The work presented in this thesis is focused on expanding the capabilities of current atom probe simulations though application of both DFT and classical atomistic modeling techniques. Chapters 1-3 describe the theoretical underpinnings along with the details of the DFT calculations and atom probe simulation codes used in this research. Three projects focused on creating a more physically detailed model for simulation of atom probe are presented.Chapters 4 and 5 present work done developing a model for the site specific evaporation field of pure FCC emitters. This site specific model is parametrized in terms of the neighbor counts of an evaporated surface atom in order to more closely match the real evaporation sequence. In chapter 4, it is shown that this modified evaporation sequence is the origin of the "flower-petal" features in the aluminum (100) desorption pattern. The limitations of this simple neighbor model are then shown in chapter 5 when it is applied to the evaporation of nickel emitters.Chapter 6 presents a study using a modified version of the atom probe simulation code, TAPSim, which allows for the performance of molecular dynamics on simulated emitters in between evaporation steps. TAPSim-MD simulations of an Al0.98Cu0.02 emitter reveal the origin of the clustering artifact seen in experimental observations of AlCu alloys. The drag effect, an athermal surface migration process that occurs partly due to a non-ideal evaporation sequence of the alloy emitter, a new mechanism for these types of atom probe artifacts is described.Finally, DFT calculations of the surface binding energy of the dilute limit for alloyed adatoms are presented in chapter 7. Fitting a simple exponential function to the per-bond energy of the studied evaporations yields better results than fitting to the ZBEF computed by the Image Hump model, since the desorption energy should be neighbor dependent but the work function is surface dependent. The results of these DFT calculations suggest that for some alloyed elements, namely Mg when alloyed in Al, reduction of the ZBEF of the solute may occur. The weaker binding energy also leads to longer surface bonds, meaning Mg will be naturally more exposed, leading to a higher probability of undergoing evaporation before reaching a terrace edge. Predicting this type of situation is important so that it is not convoluted with other artifacts, such as the drag effect.While large advancements have been made in the hardware necessary to run the atom probe, advances in the development of reconstruction algorithms have not been so quick. Much of this stems from a lack of information about how exactly samples behave while in the instrument and how field evaporation proceeds for a given sample composition. Development of forward simulations with more complete physical models has shown promise for identifying the mechanisms for atom probe artifacts, and will be crucial as part of a testing framework for new reconstruction protocols. As the atom probe becomes more popular, the need for verifying and improving the quality of the data reconstructions becomes more important.

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

Type-II strained layer superlattice (T2SL) semiconductors hold great promise for mid- and long-wavelength infrared photodetectors. While T2SL-based materials have advanced significantly in the last three decades, an outstanding challenge to improve the T2SLs is to understand the carrier transport and its limitations, in particular along the superlattice growth layers. In this dissertation, an overview of the current state-of-the-art InAs/GaSb T2SLs is presented. Fundamental semiconductor device equations and transport properties, including miniband conduction and the drift-diffusion parameters, are reviewed, and the fundamental limiting factors in carrier's transport are discussed. Furthermore, the standard method of electron-beam-induced current technique to measuring these parameters is described. The bulk of the manuscript will then explore the characterization of transport properties in an InAs/GaSb nBp photodetector through a variety of techniques. Through electron-beam-induced-current ($EBIC$) measurements, the minority carrier diffusion length along the growth direction has been measured. The $EBIC$ analysis combined with lifetime measurements using time-resolved microwave reflectance method is then used to calculate the minority electron mobility along the growth direction. Quantum efficiency modeling technique is used as an alternative approach to studying transport and quantify the complex relationships between the device performance and the underlying physics involved in it. Photocurrent-voltage measurement is also used to qualitatively investigate the drift characteristics, such as the field dependence of the drift mobility and drift velocity. Finally, the result of a recent study on the electronic band structure of a series of narrow-band superlattices is summarized.

Oxide dielectric breakdown is an old problem that has been studied over decades. It causes power dissipations and irreversible damage to the electronic devices. The aggressive downscaling of the device size exponentially increases the leakage current density, which also raises the risk of dielectric breakdown. It has been proposed that point defects, current leakages, impurity diffusions, etc. all contribute to the change of oxide chemical composition and ultimately lead to the dielectric breakdown. However, the conclusive cause and a clear understanding of the entire process of dielectric breakdown are still under debate. In this research, the electronic structure at metal/oxide interfaces is studied using first-principle calculations within the framework of Density Functional Theory (DFT) to investigate any possible key signature that would trigger the dielectric breakdown. A classical band alignment method, the Van de Walle method, is applied to the case study of the Al/crystal-SiO2 (Al/c-SiO2) interface. Point defects, such as oxygen vacancy (VO) and hydrogen impurity (IH), are introduced into the Al/c-SiO2 interface to study the effects on band offset and electronic structure caused by point defects at metal/oxide interfaces. It is shown that the bonding chemistry at metal/oxide interfaces, which is mainly ionic bond, polarizes the interface. It results in many interface effects such as the interface dipole, built-in voltage, band bending, etc. Charge density analysis also indicates that the interface can localize charge due to such ionic bonding. It is also found that VO at the interface traps metal electrons which closes the open -sp3 orbital. The analysis on local potential shows that the metal potential penetrates through a few layers of oxide starting from the interface, which metalizes the interfacial region and induces unoccupied states in the oxide band gap. In addition, it is shown that higher oxygen content at metal/oxide interfaces minimizes such metal potential invasion. In addition, an oxygen vacancy is created at multiple sites through the Al/c-SiO2 and Al/a-SiO2 interface systems, separately. The oxygen local pressure is also calculated before its removal using Quantum Stress Density theory. Correlations among electronic structure, stress density, and vacancy formation energy are found, which provide informative insights into the defect generation controlling and dielectric breakdown analysis. A new band alignment approach based on the projection of plane-waves (PWs) into the space-dependent atomic orbital (LCAO) basis is presented and tested against classical band offset methods -- the Van de Walle method. It is found that the new band alignment approach can provide a quantitative and reliable band alignment and can be applied to the heterojunctions consisting of amorphous materials. The new band alignment approach reveals the real-space dependency of the electronic structure at interfaces. In addition, it includes all interface effects, such as the interface dipole, built-in voltage, virtual oxide thinning, and band deformation, which cannot be derived using classical band offset methods. This new band alignment approach is applied to the case study of both the Al/amorphous-SiO2 (Al/a-SiO2) interface and the Al/c-SiO2. We have found that at extremely low dimensions, the reduction of the insulator character due to the virtual oxide thinning is a pure quantum effect. I highlight that the quantum tunneling current leakage is more critical than the decrease of the potential barrier height on the failure of the devices.

Nowadays, the development of energy-efficient processes for the treatment of wastewater is becoming an essential research field; taking into account the projected global population rise, the depletion of fresh water, and the necessity for available and renewable sources of energy. Within this context, the electro-oxidation of ammonia has been received considerable and increasing attention due to its advantageous in deammonification of wastewater and at the same time, production of pure hydrogen as a source of energy. However, the performance of this process should be optimized prior to wide industrial utilization.There are several factors that affect the performance of ammonia electrolysis. These factors can be divided into macro scale (like flowrate, concentration) and micro scale (like electrodes material and morphology). The effects of these factors can be evaluated in a mathematical model which would be able to optimize the process. Optimization of the process results in widely and commercially usage of ammonia electrolyzers. In this electrolyzer, water reduces at the cathode, while ammonia oxidizes at the anode. Hydrogen evolution reaction (HER) at the cathode can proceed on transition metals like nickel. However, ammonia oxidation needs noble metals like platinum to proceed; one of the other hindrances of widely commercialization of this process. This problem can be solved if the knowledge of the process kinetics and mechanism of the reaction on the surface of the catalyst clarified.The first part of this research focused on developing a mathematical model using flow regime, transport equations, the ammonia oxidation kinetics on platinum at the anode and the hydrogen evolution kinetics on nickel at the cathode. All of the non-linear differential equations were solved by finite difference methods in a comprehensive FORTRAN code. The model showed both qualitative and quantitative agreement with experimental measurements which were carried on in a bench scale prototype at ammonia concentrations found within wastewater (200 – 1200 mg L-1). The model could predict the electrolyzer performance as high as 95 percent accuracy in average. The optimum electrolyzer performance was found to be dependent on both the applied voltage and the inlet concentrations of reactant and electrolyte.The second part of this research dedicated to the investigation of the effect of platinum, iridium and their bimetallic catalysts on ammonia oxidation process through computational technique. Density functional theory calculations were performed on four platinum-iridium clusters, Pt3-xIrx (x=0-3) to examine ammonia oxidation. The adsorption of NH3-x and N2H4-x on these clusters and the effect of cluster composition on the adsorption were investigated. The relative adsorption energy showed more stability of the intermediates on the Ir3 and less stability as the number of platinum atoms increased in the cluster. These results combined with activation and free energy calculations of sequential dehydrogenation and N-N bond formation reactions showed that ammonia oxidation kinetics on platinum goes through production of N2H4; However, this kinetics on iridium proceed through sequential deprotonation of ammonia to produce nitrogen atom and then N2. Additionally, computational calculations suggest that iridium deactivated faster than platinum while onset potential on iridium is lower than platinum.

Functional perovskites have been investigated extensively for many years. Thousands of new perovskites are synthesized and studied every year. Many functional perovskites have been widely employed in industry. Density functional theory (DFT) calculations have been used to obtain a better understanding of functional perovskites, especially their electronic and structural properties. During my graduate study, I investigated perovskite’s properties on ionic transport, magnetic ordering, ferroelectricity, physical property and phase transition using DFT calculations. In the first case, I simulated the ionic transport process in several Ruddlesden- Popper (RP) phases. Climbing image nudged elastic band (CI-NEB) calculation was used to get accurate oxygen interstitial migration barrier. I established a linkage between interstitial migration barrier and perovskite’s octahedral rotation with symmetry mode approach. Two factors, including A-site atom radius and epitaxial strain, were used to reduce interstitial migration barrier in my simulation. My study on ionic transport in RP phases provides guidance on the design of fast ionic transport in perovskite oxides. In the second case, DFT calculation was employed to investigate a double perovskite’s magnetic and electronic properties. A new ferroelectric mechanism in perovskite, associated with the displacement of coplanar Mn²⁺, was discovered experimentally. My DFT calculation explained the origin of coplanar displacement from an orbital point of view. In addition, DFT simulations were used in the design of ferroelectricity enhancement perovskite. In the last case, I simulated structural behaviors under pressure of several double perovskites. The results show that these double perovskites can be divided into two groups based on their octahedral rotations under pressure. The origin of their distinct volume reduction mechanisms was studied through DFT simulations. The difference between the two mechanisms and their influence on bulk modulus were discussed based on my computational results.

As many key elementary reactions having broad utility in chemistry have been found to be catalyzed at transition-metal centers, there has been enormous effort devoted to the design and optimization of such catalysts. In addition to empirical experiments that measure catalytic activity, theoretical chemistry offers a complementary approach to understanding mechanisms of catalysis and can be used to accelerate the design of second- and subsequent-generation catalysts with further improved properties. In the broader sense, catalysis science provides opportunities to explore and understand how catalysts work at the atomic scale by means of computation, synthesis, and characterization. The primary goal of this thesis to investigate the fundamental features of transition-metal-based systems and their roles in the activation mechanisms of hydrocarbon and biomass feedstocks via density functional theory (DFT) and wave function (WF) theory methods. Accordingly, this thesis presents (i) accurate prediction of gas-phase ionization energies of mononuclear copper complexes using high level quantum mechanical methods (Chapter 2), (ii) the effects of electronic perturbations on the copper-hydroxide complexes involved in C-H bond activation reactions (Chapter 3), (iii) catalytic decarbonylation of biomass-derived carboxylic acid derivatives to olefins (Chapter 4), (iv) the dual ligand role in selective decarbonylation of fatty-acid esters to linear α-olefins (Chapter 5).

Organic electronic materials are a new generation of materials that have the potential to move our society to clean and renewable energy sources, but the efficiency of these materials has only recently become competitive with that of their inorganic counterparts. One of the major challenges to increasing the efficiency of organic materials is our present inability to understand in detail the mechanisms of energy and charge transport within them. Computational modeling can play a very important role in understanding such mechanisms and discovering new materials. However, accurate modeling of charge and energy transfer processes remains challenging for many electronic structure methods. Often it is not enough to just model static properties of these systems, and charge and energy transfer dynamics need to be studied as well. Considering the large size of these systems, such modeling is still not practical with ab-initio electronic structure methods and current computational power. Herein, I report studies of the charge-transport properties of both small organic electronics and molecular wires. We have tried to understand structure-function relationships in these materials and suggest how chemical modification may affect charge-transport mechanisms. We have also tested multiconfiguration pair-density functional theory, which is a very accurate electronic structure method for recovering both static and dynamic electron correlation, for ground- and excited-state charge transfer. Finally, we have developed a new method for modeling electronic dynamics in chemical systems by combining semiempirical Hartree-Fock theory with real-time dynamics. We have used this method to calculate UV/Vis spectra of medium and large organic systems. We have developed a new approach to characterize different peaks in real-time spectra, and we have also developed a new approach for modeling exciton dynamics using real-time approaches.

Nuclear energy is a viable solution to the world’s energy demands. Nuclear energy applications involve rich and complex physics, with high energy events, the incorporation of fission products, and the production of point and extended defects. All these phenomena have an impact on the microstructure of the constituent materials and represent efficiency and safety concerns. A mature understanding of the microstructural evolution of the component materials in the nuclear reactor core is essential to have a safe and reliable process. Experimental investigation of materials in radiation environments is difficult and expensive, making computational simulations a suitable alternative. In this dissertation, employ computational methods to study the microstructural evolution of both nuclear fuel and the iron based reactor structural components, and the impact on their material properties. In the nuclear fuel side, we investigate the crystallographic and electronic structure of Ln-U-O compounds that may be formed inside nuclear fuel operational life by the incorporation of lanthanide fission products using density functional theory (DFT). We used a layered atomic model to propose ordered structures and compared their stability to disordered phases. We also employed the atom-in-molecule approach to study the oxidation state of uranium atoms, and the iconicity/covalency of the U-O bonds. In the structural components side, we studied the migration mechanisms of self-interstitial dumbbells and vacancies around single edge or screw dislocations. The actual saddle point energy and configuration as a function of position with respect of the dislocation core was calculated with the self-evolving atomistic kinetic Monte Carlo (SEAKMC) method, and used this data as an input for KMC calculations. This allowed the analysis of the migration paths, the range of interaction of point defects with dislocations, and the preferential absorption of self-interstitial dumbbells over vacancies, known as dislocation bias, which is responsible for swelling in irradiated materials. The understanding of the mechanism responsible for the microstructural changes, and how these changes impact the material properties is a key aspect to be able to develop materials with enhanced radiation resistance, and achieve high performance under extreme conditions that are vital for nuclear energy generation with improved efficiency and safety.

The importance and promise that topological materials hold has been recently underscored by the award of the Nobel Prize in Physics in 2016 ``for theoretical discoveries of topological phase transitions and topological phases of matter." This dissertation explores the novel qualities and useful topologically protected surface states of topological insulators and semimetals. Topological materials have protected qualities which are not removed by weak perturbations. The manifestations of these qualities in topological insulators are spin-momentum-locked surface states, and in Weyl and Dirac semimetals they are unconventional open surface states (Fermi arcs) with anomalous electrical transport properties. There is great promise in utilizing the topologically protected surface states in electronics of the future, including spintronics, quantum computers, and highly sensitive devices. Physicists and chemists are also interested in the fundamental physics and exotic fermions exhibited in topological materials and in heterostructures including them. Chapter 1 provides an introduction to the concepts and methods of topological band theory. Chapter 2 investigates the spin and spin-orbital texture and electronic structures of the surface states at side surfaces of a topological insulator, Bi2Se3, by using slab models within density functional theory. Two representative, experimentally achieved surfaces are examined, and it is shown that careful consideration of the crystal symmetry is necessary to understand the physics of the surface state Dirac cones at these surfaces. This advances the existing literature by properly taking into account surface relaxation and symmetry beyond what is contained in effective bulk model Hamiltonians. Chapter 3 examines the Fermi arcs of a topological Dirac semimetal (DSM) in the presence of asymmetric charge transfer, of the kind which would be present in heterostructures. Asymmetric charge transfer allows one to accurately identify the projections of Dirac nodes despite the existence of a band gap and to engineer the properties of the Fermi arcs, including spin texture. Chapter 4 investigates the effect of an external magnetic field applied to a DSM. The breaking of time reversal symmetry splits the Dirac nodes into topologically charged Weyl nodes which exhibit Fermi arcs as well as conventionally-closed surface states as one varies the chemical potential.

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

From the single hydrogen to proteins in the hundreds of thousands of kilodaltons, scientists can use the electronic structure of interacting atoms to predict their material properties. Knowing the material properties through solving the electronic structure problem, would allow for the controlled prediction and corresponding design of materials. The Kohn-Sham equations, based on density functional theory, transform a many-body problem impossible to solve for anything but the smallest molecules, into a practical problem which can be used to predict material properties. Although KSDFT scales as the cube of the number of electrons in the system, there are additional well documented approximations to further reduce the number of electrons, such as the pseudopotential method. The incoming exascale era will lead to unavoidable challenges in solving the Kohn-Sham equations. These challenges include communication and hardware considerations. Old paradigms, epitomized by repeated series of globally forced synchronization points, will give way to new breeds of algorithms to maximizing scaling performance while maintaining portability. This thesis focuses on the solution to Kohn-Sham DFT in real space at scale. Key to this effort is a parallel treatment of numerical elements involving the Rayleigh-Ritz method. At minimum, the Rayleigh-Ritz projection requires a number of distributed matrix vector operations equal to the number of electrons solved for in a system. Furthermore, the projection requires that number, squared and then halved, of dot products. The memory cost for such an algorithm also grows very large quickly, and explicit intelligent management is not an option. I demonstrate the computational requirements for the various steps in solving for the electronic structure problem for both large and small molecular systems. This thesis also discusses opportunities in real space Kohn-Sham DFT to further utilize floating point optimized hardware the with higher order stencils.

The combined use of organic and inorganic precursors enables the discovery of new hybrid organic-inorganic materials as well as the development of improved synthetic routes to conventional solids. Herein, we present three major directions: the application of tetraborylmethanes as organometallic single-C synthons for carbon-metal bond formation in carbides, the synthesis and theoretical study of low-dimensional early transition metal dichalcogenide derivatives, and a separate study on the development of a new layered exfoliatable Zintl phase. In Chapter 2, we present the stepwise deborylation-homologation of C(Bpg)4 (pg = 1,3-propanediolate) with trimethylsilyl and trimethylgermyl groups as organometallic models for the initial carbon-metal bond formation in SiC and GeC. We demonstrate that 2 substitutions with these tertiary substituents proceed readily, and that the disubstituted derivatives are recalcitrant towards further homologation. In Chapter 3, we explore various strategies to utilize tetraborylmethanes as a carbido source with the intent to form solid state metal carbide materials at low temperatures. We present the thermal co-decomposition of C(Bpg)4 with Si(IV) and Nb(V) sources, which ultimately formed intimate SiO2:B2O3/C and Nb2O5/C mixtures. We also discuss the synthesis of a single-source Si-C precursor molecule, SiCl3C(Bpg)3, and its chemistry with activating bases and hydride sources. In Chapter 4, we present the rational synthesis, characterization, and electronic structure of 1D- TiS2 derivatives TiS2(en) (en = ethylenediamine) and TiS2(pn) (pn = 1,3-propylenediamine), which feature direct band gaps attractive for near-IR optoelectronics. Additionally, we discuss the synthesis of [V7S8Cl2(en)8]Cl4 (en = ethylenediamine), the first early transition metal chalcogenide dicubane, and DFT calculations explaining its observed magnetic behavior. In Chapter 5, we present the synthesis, electronic structure calculations, and exfoliation studies of the layered Zintl phase EuSn2As2. This anisotropic pseudo-2D material features anti-ferromagnetic coupling out-of-plane and ferromagnetic coupling in-plane. Overall, the work presented herein builds on our understanding of polyborylmethane chemistry as well as the rational synthesis and properties of low-dimensional metal-organic solids, and motivates further exploration of 2D Sn-containing lattices for the discovery of novel magnetic phenomena.

Advancement and understanding of amorphous materials has been limited by conventional computer simulations. A generic approach of "Melt-Quenching" (MQ) with molecular dynamics (MD) has several shortcomings. First: a slow quenching rate limits model size for ab initio method while efficient empirical potentials undermine the accuracy of results. To overcome this restrictions in this dissertation we have modeled large and realistic models of different amorphous systems with an accuracy comparable to density functional theory (DFT) calculations. We have used a recently developed method Force Enhanced Atomic Refinement (FEAR). FEAR offers the logical advantage of using a priori knowledge, but within an ab initio simulation framework. It utilizes Reverse Monte Carlo (RMC) technique [a simple stochastic scheme to find coordinates matching a diffraction experiment] and relaxation using ab initio interactions. We have extensively explored large systems with 648-800 atom utilizing the FEAR method to get a better understanding of these systems. We have used FEAR to generate amorphous carbon (648 atoms) across densities 0.95-3.5 g/cc . We study the low density regime of amorphous carbon and reveal that nano-porous carbon obtained in diffraction experiment is actually interconnected and wrapped form of amorphous graphene. We summarize our conclusion on amorphous graphene by thoroughly investigating and comparing its properties with two-dimensional amorphous graphene, crystalline graphene and schwarzite. In addition, vibrational properties of amorphous silica and amorphous carbon have been thoroughly studied.