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No description available.

Molecular nonlinear optical (NLO) properties are extensively studied using both theory and experiment because of their use in myriad applications. Experimental measurements of the most interesting molecules’ NLO properties are difficult, so experimental data for molecules with desirable NLO properties is scarce. Theoretical tools don’t suffer from the same limitations and can provide significant insights into the physico-chemical phenomena underlying the nonlinear responses, can help in interpreting response behaviour of molecules, and can guide design the materials with desirable response properties. Here, I present my work on developing methods for accurately calculating the NLO properties of molecules using the finite field (FF) approach. The first chapter provides a background for the finite field and electronic structure methods used in this dissertation. Chapter two is a thorough investigation of the finite field method. The limitations of the method are highlighted and the optimal conditions for overcoming its drawbacks and obtaining meaningful and accurate results are described. Chapter three presents the first systematic study of the dependence of optimal field strengths on molecular descriptors. The first protocol for predicting the optimal field for the second hyperpolarizability is presented and successfully tested, and the dependence of the optimal field strength for the first hyperpolarizability on the molecular structure is investigated. Chapter four is an assessment of various DFT functionals in calculating the second hyperpolarizabilities of organic molecules and oligomers. This study shows the limitations of conventional DFT methods and the importance of electron correlation to response properties. In chapter five we present a new method of calculating NLO properties using a rational function model that is shown to be more robust and have lower computational cost than the traditional Taylor expansion. Finally, chapter six includes a summary of the thesis and an overview of future work. / Thesis / Doctor of Philosophy (PhD)

The research performed aims to develop a deeper understanding and prediction of behaviour of complex chemical and physical systems using density functional theory (DFT) modelling complemented by experimental techniques. We focus on phenomena relevant to practical applications of semiconducting materials. Semiconductor nanowires, produced by the vapor-liquid-solid method are being considered for applications in photo sensors, field effect transistors, light emitting diodes (LEDs) and energy harvesting devices. In particular, semiconductor nanowire based photovoltaic devices show potential for lower cost due to less material utilization and greater energy conversion efficiency arising from enhanced photovoltage or photocurrent due to hot carrier or multiexciton phenomena enhanced light absorption, compared to conventional thin film devices. Further, freedom from lattice matching requirements due to strain accommodation at the nanowire surfaces enable compatibility with a wide variety of substrates including Silicon. Thus understanding and improving the optoelectronic properties of nanowires is of great interest. In the first paper, we study the effect of planar defects on optoelectronic properties of nanowire based semiconductor devices. Specifically, we were interested in investing the origin of various features observed in the photoluminisence (PL) spectrum of GaP nanowire using density functional modelling, which are not well understood. In the second paper, we work to model bonding characteristics during a chemical synthesis. We focus on the synthesis of nanoparticles for supercapacitor application. In the past decade, comprehensive research has been emphasized on manganese oxides for electrochemical supercapacitor (ECS) applications. Mn3O4 has gained significant interest due to its compatibility with capping agents and the unique spinel structure allows for potential modifications with other cations. Many metal oxide synthesis techniques are based on aqueous processing. The synthesized particles are usually dried and redispersed in organic solvents to incorporate water-insoluble additives such as binders to fabricate films and devices. However, during the drying step nano-structures are highly susceptible to agglomeration, which can be attributed to the condensation reactions occurring between particles and reduction in surface energy. Poor electrolyte access due to agglomeration and low intrinsic conductivity of Mn3O4 are detrimental to the performance of Mn 3O4 electrode especially at high active mass loadings. Numerous attempts have focused on controlling size and morphology of Mn3O4 nanostructures using capping agents, which have strong adhesion to particles surface to inhibit agglomeration. Catechol containing molecules have been used for dispersion of metallic nanoparticles and fabrication of composite thin films, resulted in narrow size distribution of nanoparticles and strong adhesion to substrates. Despite the experimental results showing good adsorption of catechol group to metal atoms, the mechanism is unclear since it is highly influenced by synthesis parameters. We use Infrared spectroscopy in conjugation with density functional modelling to understand the binding mechanism of 3,4 dihydroxy benzaldehyde onto Mn3O4 surface. / Thesis / Master of Applied Science (MASc)

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Nickel and iron-based alloys are important structure and cladding materials for modern nuclear reactors due to their high mechanical properties and high corrosion resistance. To understand the radiative and corrosive environment influence on the mechanical strength, computer simulation works are conducted. In particular, this dissertation is focused on multiscale modeling of the effects of radiation-induced solute segregation and oxidation on grain boundary (GB) strength in nickel-based and iron-based alloys. Besides the atomistic scale density functional theory (DFT) based calculations of GB strength, continuum-scale cohesive zone model (CZM) is also used to simulate intergranular fracture at the microstructure scale. First, the effects of solute or impurity segregation at GBs on the GB strength are studied. Thermal annealing or radiation induced segregation of solute and impurity elements to GBs in metallic alloys changes GB chemistry and thus can alter the GB cohesive strength. To understand the underlying mechanisms, first principles based DFT calculations are conducted to study how the segregation of substitutional solute and impurity elements (Al, C, Cr, Cu, P, Si, Ti, Fe, which are present in Ni-based X-750 alloys) influences the cohesive strength of Σ3(111),Σ3(112),Σ5(210) and Σ5(310) GBs in Ni. It is found that C and P show strong embrittlement potencies while Cr and Ti can strengthen GBs in most cases. Other solute elements, including Si, have mixed but insignificant effects on GB strength. In terms of GB character effect, these solute and impurity elements modify the GB strength of the Σ5(210) GB most and that of the Σ3(111) least. Detailed analyses of solute-GB chemical interactions are conducted using electron localization function, charge density map, partial density of states, and Bader charge analysis. The results suggest that the bond type and charge transfer between solutes and Ni atoms at GBs may play important roles on affecting the GB strength. For non-metallic solute elements (C, P, Si), their interstitial forms are also studied but the effects are weaker than their substitutional counterparts. Nickel-base alloys are also susceptible to stress corrosion cracking (SCC), in which the fracture mainly propagates along oxidized grain boundaries (GBs). To understand how oxidation degrades GB strength, the next step is to use density functional theory (DFT) calculations to study three types of oxidized interfaces: partially oxidized GBs, fully oxidized GBs, and oxide/metal interface, using Ni as a model system. For partially oxidized GBs, both substitutional and interstitial oxygen atoms of different concentrations are inserted at three Ni GBs: Σ3(111) coherent twin, Σ3(112) incoherent twin, and Σ5(210). Simulation results show that the GB strength decreases almost linearly with the increasing oxygen coverage at all GBs. Typically, substitutional oxygen causes a stronger embrittlement effect than interstitial oxygen, except at the Σ3(111). In addition, the oxygen-induced mechanical distortion has a much smaller contribution to the embrittlement than its chemical effect, except for oxygen interstitials at the Σ3(111). For the fully oxidized GBs, three NiO GBs of the same types are studied. Although the strengths of Σ3(112) and Σ5(210) NiO GBs are much weaker than the Ni counterparts, the Σ3(111) NiO GB has a higher strength than that in Ni, indicating that Σ3(111) GB may be difficult to fracture during SCC. Finally, the strength of a Ni/NiO interface is found to be the weakest among all interfaces studied, suggesting the metal/oxide interface could be a favorable crack initiation site when the tensile stress is low. Furthermore, the effects of co-segregation of oxygen and solute/impurity elements on GB strength are studied by DFT, using the 5(210) GB in an face-centered-cubic (FCC) Fe as a model system. Four elements (Cr, Ni, P, Si) that are commonly present in stainless steels are selected. Regarding the effects of single elements on GB strength, Ni and Cr are found to the increase the GB strength, while both P and Si have embrittlement effects. When each of them is combined with oxygen at the GB, the synergetic effect can be different from the linear sum of individual contributions. The synergetic effect also depends on the spatial arrangement of solute elements and oxygen. If they are aligned on the same plane at the GB, the synergetic effect is similar to the linear sum, although P and Si show stronger embrittlement potencies when they combine with both interstitial and substitutional oxygen. When they are arranged on a trans-plane structure, only nickel combined with oxygen show larger embrittlement potencies than the linear sum in all cases. Crystal Orbital Hamilton Populations analysis of bonding and anti-bonding states is conducted to interpret how the interaction between solutes and oxygen impacts GB strength. Finally, the continuum-scale CZM method, which is based on the bilinear mixed mode traction separation law, is used to model SCC-induced intergranular fracture in polycrystalline Ni and Fe based alloys in the MOOSE framework. The previous DFT results are used to justify the input parameters for the oxidation-induced GB strength degradation. In this study, it is found that the crack path does not always propagate along the weak GBs. As expected, the fracture prefers to occur at the GB orientations perpendicular to the loading direction. In addition, triple junctions can arrest or deflect fracture propagation, which is consistent with experimental observations. Simulation results also indicate that percolated weak GBs will lead to a much lower fracture stress compared to the discontinuous ones. / Doctor of Philosophy / Iron and Nickel based alloys are important structural materials for nuclear reactors due to their good mechanical properties, corrosion resistance, and radiation resistance. Under radiation and corrosive conditions, those alloys are susceptible to radiation induced segregation (RIS) and stress corrosion cracking (SCC). This dissertation is mainly focused on understanding the influence of the two effects on grain boundary (GB) strength. Systematic atomistic scale density functional theory (DFT) simulations are applied for the nickel and iron systems. Based on the DFT results, cohesive zone model is utilized for the continuum scale fracture simulation in nickel and iron polycrystal. First, DFT calculations are conducted for studying the RIS effect on the GB strength in nickel. Al, Cr, Cu, C, Si, P, Fe, and Ti are chosen as segregated element. Σ3(111), Σ3(112), Σ5(210), Σ5(310) four types of GBs are built for GB strength calculations. It is found that substitutional C and P always embrittle the GB, while substitutional Ti and Cr can strengthen the GB in most cases. Partial density of states (PDOS) analysis indicates the formation of C-Ni and P-Ni covalent bonds is the possible reason for their embrittlement effects. Bader charge analysis shows negatively charged elements likely reduce the GB strength. Interstitial element segregation is applied for non-metal elements (C, P, and Si). The results indicate interstitial elements have weaker effects than substitution ones. On the next stage to study the SCC effect, DFT calculations are performed for nickel Σ3(111), Σ3(112), and Σ5(210) GBs with difference oxygen concentration and oxygen incorporation types. Besides partially oxidized GBs, fully oxidized GBs (NiO GBs) and metal-oxide interface are also constructed for comparison. Simulation results show that the GB strength decreases nearly monotonically as oxygen concentration goes up. Typically, substitution oxygen causes a larger embrittlement effect than interstitial oxygen except at Σ3(111). It is found that the large mechanical distortion in this coherent twin GB contributes significantly to the GB strength drop. NiO GBs can be weak (Σ3(112),Σ5(210)) or strong (Σ3(111)). NiO/Ni interface shows lowest strength compared with partially and fully oxidized GBs, indicating the importance of the metal-oxide interface in the SCC process. Furthermore, the combined effects between segregated elements and oxygen are studied in face center cubic (FCC) iron system. In this part only Σ5(210) GB is selected with substitutional Cr, Ni, P, and Si as segregated elements. The results of single element effects show Cr can strength the GB while P has an opposite effect. Other two elements show little effect. For the co-segregation effects, the trans-plane structures have larger embrittlement potencies than in-plane ones for Ni, suggesting the GB strength can also be affected by the spatial arrangement of segregated elements. Finally, cohesive zone model is applied for fracture simulations in polycrystalline nickel and iron under tensile loading condition. It is found that intergranular fracture depends on both GB strength and orientation. GBs perpendicular to the loading direction have higher chances to crack. It is also found the percolated weak GBs induce larger strength drop than the discontinuous ones.

<p dir="ltr">Theoretical development and implementation of Partition Density Functional Theory, a quantum density embedding framework for electronic structure simulation.</p>

Yes / Materials-binding peptides offer promising routes to the production of tailored Pd nanomaterials in aqueous media, enabling the optimization of catalytic properties. However, the atomic-scale details needed to make these advances are relatively scarce and challenging to obtain. Molecular simulations can provide key insights into the structure of peptides adsorbed at the aqueous Pd interface, provided that the force-field can appropriately capture the relevant bio-interface interactions. Here, we introduce and apply a new polarizable force field, PdP-CHARMM, for the simulation of biomolecule–Pd binding under aqueous conditions. PdP-CHARMM was parametrized with density functional theory (DFT) calculations, using a process compatible with similar polarizable force-fields created for Ag and Au surfaces, ultimately enabling a direct comparison of peptide binding modes across these metal substrates. As part of our process for developing PdP-CHARMM, we provide an extensive study of the performance of ten different dispersion-inclusive DFT functionals in recovering biomolecule–Pd(111) binding. We use the functional with best all-round performance to create PdP-CHARMM.We then employ PdP-CHARMM and metadynamics simulations to estimate the adsorption free energy for a range of amino acids at the aqueous Pd(111) interface. Our findings suggest that only His and Met favor direct contact with the Pd substrate, which we attribute to a remarkably robust interfacial solvation layering. Replica-exchange with solute tempering molecular dynamics simulations of two experimentally-identified Pd-binding peptides also indicate surface contact to be chiefly mediated by His and Met residues at aqueous Pd(111). Adsorption of these two peptides was also predicted for the Au(111) interface, revealing distinct differences in both the solvation structure and modes of peptide adsorption at the Au and Pd interfaces. We propose that this sharp contrast in peptide binding is largely due to the differences in interfacial solvent structuring. / Air Force Office for Scientfi c Research (Grant #FA9550-12-1-0226)

Simulationen elektronischer Strukturen ermöglichen die Bestimmung grundlegender Eigenschaften von Materialien ohne jegliche Experimente. Sie zählen deshalb zu den Standardwerkzeugen, mit denen Fortschritte in materialwissenschaftlichen und chemischen Anwendungen vorangetrieben werden. In den letzten Jahrzehnten hat sich die Dichtefunktionaltheorie (DFT) aufgrund ihrer ausgezeichneten Balance zwischen Genauigkeit und Rechenkosten als die beliebteste Simulationstechnik für elektronische Strukturen etabliert. Jedoch verlangen drängende gesellschaftliche und technologische Herausforderungen nach Lösungen für immer komplexere wissenschaftliche Fragestellungen, sodass selbst die effizientesten DFT-Programme nicht mehr in der Lage sind, Antworten in angemessener Zeit und mit den verfügbaren Rechenressourcen zu liefern. Daher wächst das Interesse an Ansätzen des maschinellen Lernens (ML), die darauf abzielen, Modelle bereitzustellen, die die Vorhersagekraft von DFT-Rechnungen zu vernachlässigbaren Kosten replizieren. In dieser Arbeit wird gezeigt, dass solche ML-DFT Ansätze bisher nicht in der Lage sind, das Vorhersagen der elektronischen Struktur von Materialien auf DFT-Niveau vollständig abzubilden. Davon ausgehend wird in dieser Arbeit ein neuer Ansatz für ML-DFT Modelle vorgestellt. Es wird ein umfassendes Framework für das Training von ML-DFT-Modellen auf Grundlage einer lokalen Darstellung der elektronischen Struktur entwickelt, welcher auch Details wie Strategien zur Datengeneration und Hyperparameteroptimierung beinhaltet. Es werden Ergebnisse vorgestellt, die zeigen, dass mit diesem Framework trainierte Modelle die breite Palette der Vorhersagefähigkeit sowie Genauigkeit von DFT-Simulationen zu drastisch reduzierten Kosten replizieren. Weiterhin wird die allgemeine Nützlichkeit dieses Ansatzes demonstriert, indem Modelle über Längenskalen, Phasengrenzen und Temperaturbereiche hinweg angewendet werden.:List of Tables 10 List of Figures 12 Mathematical notation and abbreviations 14 1 Introduction 19 2 Background 23 2.1 Density Functional Theory 23 2.2 Sampling of Observables 35 2.3 Machine Learning and Neural Networks 37 2.4 Hyperparameter Optimization 46 2.5 Density Functional Theory Machine Learning Models 50 3 Scalable Density Functional Theory Machine Learning Models 59 3.1 General Framework 59 3.2 Descriptors 67 3.3 Data Generation 69 3.4 Verification of accuracy 78 3.5 Determination of Hyperparameters 87 4 Transferability and Scalability of Models 99 4.1 Large Length Scales 100 4.2 Phase Boundaries 108 4.3 Temperature Ranges 117 5 Summary and Outlook 131 Appendices 136 A Computational Details of the Materials Learning Algorithms framework 137 B Data Sets, Models, and Hyperparameter Tuning 145 Bibliography 161 / Electronic structure simulations allow researchers to compute fundamental properties of materials without the need for experimentation. As such, they routinely aid in propelling scientific advancements across materials science and chemical applications. Over the past decades, density functional theory (DFT) has emerged as the most popular technique for electronic structure simulations, due to its excellent balance between accuracy and computational cost. Yet, pressing societal and technological questions demand solutions for problems of ever-increasing complexity. Even the most efficient DFT implementations are no longer capable of providing answers in an adequate amount of time and with available computational resources. Thus, there is a growing interest in machine learning (ML) based approaches within the electronic structure community, aimed at providing models that replicate the predictive power of DFT at negligible cost. Within this work it will be shown that such ML-DFT approaches, up until now, do not succeed in fully encapsulating the level of electronic structure predictions DFT provides. Based on this assessment, a novel approach to ML-DFT models is presented within this thesis. An exhaustive framework for training ML-DFT models based on a local representation of the electronic structure is developed, including minute treatment of technical issues such as data generation techniques and hyperparameter optimization strategies. Models found via this framework recover the wide array of predictive capabilities of DFT simulations at drastically reduced cost, while retaining DFT levels of accuracy. It is further demonstrated how such models can be used across differently sized atomic systems, phase boundaries and temperature ranges, underlining the general usefulness of this approach.:List of Tables 10 List of Figures 12 Mathematical notation and abbreviations 14 1 Introduction 19 2 Background 23 2.1 Density Functional Theory 23 2.2 Sampling of Observables 35 2.3 Machine Learning and Neural Networks 37 2.4 Hyperparameter Optimization 46 2.5 Density Functional Theory Machine Learning Models 50 3 Scalable Density Functional Theory Machine Learning Models 59 3.1 General Framework 59 3.2 Descriptors 67 3.3 Data Generation 69 3.4 Verification of accuracy 78 3.5 Determination of Hyperparameters 87 4 Transferability and Scalability of Models 99 4.1 Large Length Scales 100 4.2 Phase Boundaries 108 4.3 Temperature Ranges 117 5 Summary and Outlook 131 Appendices 136 A Computational Details of the Materials Learning Algorithms framework 137 B Data Sets, Models, and Hyperparameter Tuning 145 Bibliography 161