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Los óxidos de nitrógeno, comúnmente llamados NOx, han sido objeto de estudio en numerosos trabajos relacionados con el medio ambiente, puesto que constituyen una de las principales fuentes de contaminación del aire que existen en la actualidad. Estos provienen principalmente de los automóviles, y en la atmósfera contribuyen a la formación de smog y lluvia ácida. Una de las formas para disminuir dicha contaminación es convertir los ya mencionados NOx en especies inocuas como N2 y O2. Una de las reacciones más importantes de reducción de estas especies tiene lugar en los caños de escape de los autos a través de un convertidor catalítico, donde además de producirse la deseada eliminación de los NOx, también convierten otras especies contaminantes, como CO o hidrocarburos en CO2 y H2O. Sin embargo, los metales utilizados en el convertidor catalítico son caros y realmente escasos. El oro es tradicionalmente considerado como un metal con propiedades catalíticas muy pobres. Sus caras cristalinas son químicamente inertes a la mayoría de las moléculas gaseosas. No obstante, cuando trabajamos a escala nanométrica se observan notables cambios en su estructura electrónica tornándose altamente reactivo, especialmente a temperatura ambiente. Se ha visto que cataliza varias reacciones relacionadas a la industria química y a la protección del medio ambiente. En este trabajo de tesis se estudia mediante métodos químico-cuánticos la adsorción de NO en partículas de Au tanto libres como soportadas, así como la formación de dímeros N2O2 en las mismas partículas como posibles intermediarios durante la reducción de NO. Primeramente se estudiaron los sitios activos y principales propiedades de las partículas de Au aisladas de escasos átomos (de 1 a 10). Las partículas más estables resultaron ser las planares. Luego de esto se hicieron interaccionar las mismas con NO obteniendo un comportamiento oscilatorio par-impar con máximos en la fuerza del enlace en sistemas impares, debido a que se produce un acoplamiento entre dos especies de capa abierta. La formación del dímero de NO fue llevada a cabo posteriormente obteniendo un comportamiento similar que con el monómero pero más pronunciado, debido principalmente a una mayor transferencia electrónica hacia el dímero por parte de las partículas con número de átomos de Au impar, acortando el enlace N-N y por lo tanto aumentando la fuerza de interacción en el mismo. Las partículas más activas resultaron ser las de 5, 7 y 9 átomos. Posteriormente, se modelaron los dos mecanismos propuestos para la ruptura del enlace N-O, tanto a través de la disociación directa como a través de la formación del dímero de NO, resultando este último caso mucho más favorable por tener barreras de activación sustancialmente más bajas. Una vez analizados los resultados correspondientes a partículas de Au en estado libre, en la segunda parte de la tesis se estudió la capacidad adsortiva y reactiva de dichas partículas soportadas en superficies de goethita hidratada y parcialmente deshidratada. La goethita (α-FeOOH) es un oxohidróxido de hierro usado como adsorbente y como soporte de catalizadores. Estudiamos la estructura geométrica y electrónica de partículas de Au de hasta 5 átomos en la cara (110) a traves del método del funcional de la densidad (DFT) usando condiciones periódicas. Se obtuvieron las partículas más estables en la superficie sin hidroxilar, debido a la alta reactividad de los O superficiales no saturados que posee. Las partículas de Au3, Au4 y Au5 presentan una particular estabilidad debido a la generación de un efecto de polarización de la nube electrónica ocasionada por la interacción de la partícula metálica con la superficie del soporte. Sobre esta superficie las partículas son positivas con valores que llegan a tener una carga de 0.7e. Por su parte, los agregados de Au sobre la superficie hidratada están ligados al soporte más débilmente y su carga es levemente negativa. Se ha escogido a la partícula de Au5 soportada como prototipo para estudiar la adsorción y reacción de NO. Se modelaron dos situaciones para comparar los mecanismos de ruptura del enlace N-O. Se observó que la disociación de este enlace vía formación del dímero de NO requiere mucha menor energía que la ruptura directa. Además, usar una partícula anclada a un soporte cambia la termodinámica de la reacción, ya que la misma pasa de ser endotérmica para el caso de la partícula de Au5 libre en la ruptura vía dímero, a exotérmica para el caso soportado a través del mismo mecanismo. / Nitrogen oxides (NOx) have been deeply studied in relation with environmental problems, since they are one of the main sources of air pollution. They are formed mainly in automobiles and they contribute to the formation of smog and acid rain. One way to reduce their effects is by converting these species to gases such as N2 and O2. One of the most important reduction reactions of these species occurs in exhaust of automobiles through a catalytic converter, where in addition to the desired removal of NOx produced, also make other pollutant species such as CO or hydrocarbons in CO2 and H2O. However, the metals used in the catalytic converter are really expensive and scarce. Gold is traditionally considered a metal with very poor catalytic properties. Their crystalline faces are inert to most of the gases. However, at nanometric scale it becomes highly active especially at room temperature. Supported gold materials catalyze important reactions of industrial interest and in relation with environmental protection. In this work the NO adsorption on free and supported Au particles is studied using quantum chemistry methods. The formation of N2O2 species (NO dimers) is also considered as a possible intermediate during NO reduction. First, the preferential adsorption sites and other properties of free tiny Au particles are analyzed. The most stable aggregates resulted to be the planar structures. NO interaction with Au shows an even-odd oscillatory behavior with stronger bonds with the particles with an odd number of Au atoms, due to an efficient coupling between two open-shell species. The formation of N2O2 was also modeled, showing a similar behavior mainly due to the more significant electronic charge transfer from odd particles to the adsorbate which produces a stronger N-N bond. Particles with 5, 7 and 9 gold atoms are particularly active to the dimer formation. Two different mechanisms for NO breaking are modeled: the direct dissociation, and the one via N2O2 as intermediate. The latter resulted to be much more favorable because show lower activation barriers. In the second part of this thesis, the reactivity of these Au particles was investigated by supporting them to hydrated and partially dehydrated goethite surfaces. Goethite is an iron oxo-hydroxide commonly used as adsorbent and as a support of metal catalysts. We have studied gold particles up to 5 atoms deposited on the (110) face using a periodic density functional theory (DFT) approximation. The more stable particles were observed on dehydrated surfaces due to the high reactivity of surface unsatured oxygen ions. In particular, Au3, Au4 and Au5 planar particles are very stable because a polarization effect is produced due the interaction with the support. These aggregates are positively charged reaching values up to 0.7e. On the other hand, on the hydrated surface the interaction with the support is relatively weak and the particle has a slight negative charge. Supported Au5 particle was selected as a prototype system to study the NO reactivity. Two situations are modeled to compare the mechanism for N-O breaking. It was observed that the dissociation via dimer formation requires much less energy than the direct one. Besides, when the particle is anchored to the support changes the thermodynamics of the reaction because it becomes exothermic.

En esta tesis contribuimos a la comprensión de sistemas que contienen moléculas de interés farmacológico adsorbidas en una superficie vehicular de carbón activado, con la finalidad de desarrollar en el futuro fármacos dirigidos con mayor eficacia y menor toxicidad. Los resultados obtenidos son mayoritariamente teóricos y nos proporcionan datos sobre las interacciones existentes entre las superficies de carbón activado estudiadas (prístina, dopada o funcionalizada) y distintos fármacos de uso masivo en la población (5-fluorouracilo, dacarbazina, mesalazina y paracetamol). Mediante el estudio de las características estructurales y las propiedades electrónicas del sistema (distancias de enlace, energías de adsorción, densidades de estados y cargas electrónicas, entre otras), logramos comprender la naturaleza de las interacciones producidas, determinamos las condiciones óptimas para la adsorción y predecimos las condiciones esperadas para la liberación del fármaco. Inicialmente se estudia la adsorción del fármaco 5-fluorouracilo en la superficie de carbón activado prístina y dopada con aluminio. Luego, se analizan las interacciones entre el fármaco dacarbazina y las superficies de carbón activado prístina y funcionalizada con el grupo funcional carboxilo (-COOH), a diferentes intervalos de pH. A continuación, para mejorar la adsorción del fármaco mesalazina se analizan tres superficies de carbón activado: prístina, funcionalizada con el grupo amino (-NH2) y bi-funcionalizada con los grupos amino-carboxilo (-NH2–COOH), a diferente pH. Por último, se analiza la adsorción del fármaco paracetamol en dos adsorbentes comerciales con diferentes propiedades texturales: los carbonos activados CAT y CARBOPAL, realizando un estudio teórico-experimental. Sostenemos que un mejor conocimiento de las propiedades de adsorción de la superficie modificada del carbón activado conducirá a más y mejores aplicaciones de este material como soporte de fármacos. / In this thesis we contribute to the understanding of systems that contain molecules of pharmacological interest adsorbed on a vehicular surface of activated carbon, in order to develop targeted drugs with greater efficacy and less toxicity in the future. The results are mostly theoretical and provide us with data on the interactions between the studied activated carbon surfaces (pristine, doped or functionalized) and different drugs widely used (5-fluorouracil, dacarbazine, mesalazine and paracetamol). By studying the structural characteristics and the electronic properties of the system (bond distances, adsorption energies, densities of states, and electronic charges, among others), we understand the nature of the interactions, determined the optimal conditions for adsorption and predict the conditions for the drug release. Initially, the adsorption of 5-fluorouracil on the activated carbon surface, pristine and doped with aluminum, is studied. Then, the interactions between dacarbazine and the pristine and functionalized activated carbon surface with the carboxyl functional group (- COOH) at different pH ranges were analyzed. Next, to improve the adsorption of mesalazine, its adsorption on three activated carbon surfaces, pristine, functionalized with the amino group (-NH2) and bi-functionalized with the amino-carboxyl groups (-NH2 - COOH) is analyzed at different pH. Finally, the adsorption of paracetamol in two commercial adsorbents of different textural properties, CAT and CARBOPAL activated carbons, is analyzed carrying out a theoretical-experimental study. We argue that a better understanding of the modified-surface adsorption properties of activated carbon will lead to more and better applications of this material as a drug carrier.

Computational chemistry and machine learning approaches are combined to understand the mechanisms, derive activity trends, and ultimately to search for active electrocatalysts for the electrochemical oxidation of ammonia (AOR) and nitrate reduction (NO3RR). Both re- actions play vital roles within the nitrogen cycle and have important applications within tackling current environmental issues. Mechanisms are studied through the use of density functional theory (DFT) for AOR and NO3RR, subsequently a descriptor based approach is used to understand activity trends on a wide range of electrocatalysts. For AOR inter- pretable machine learning is used in conjunction with active learning to screen for active and stable ternary electrocatalysts. We find Pt3RuCo, Pt3RuNi and Pt3RuFe show great activity, and are further validated via experimental results. By leveraging the advantages of the interpretible machine learning model we elucidate the underlying electronic factors for the stronger *N binding which leads to the observed improved activity. For NO3RR an interpretible machine learning model is used to understand ways to bypass the stringent limitations put on the electrocatalytic activity due to the *N vs *NO3 scaling relations. It is found that the *N binding energy can be tuned while leaving the *NO3 binding energy unaffected by ensuring that the subsurface atom interacts strongly with the *N. Based on this analysis we suggest the B2 CuPd as a potential active electrocatalyst for this reaction, which is further validated by experiments / Doctor of Philosophy / The chemical reactions that makeup the nitrogen cycle have played a pivotal role in human society, consider the fact that one of the most impactful achievements of the 20th century was the conversion of nitrogen (N2) to ammonia (NH3) via the Haber-Bosch process. The key class of materials to facilitate such transformations are called catalysts, which provide a reactive surface for the reaction to occur at reasonable reaction rates. Using quantum chemistry we can understand how various reactions proceed on the catalyst surface and how the catalyst can be designed to maximize the reaction rate. Specifically here we are interested in the electrochemical oxidation of ammonia (AOR) and reduction of nitrate (NO3RR), which have important energy and environmental applications. The atomistic insight provided by quantum chemistry helps us understand the reaction mechanism and key hurdles in developing new catalysts. Machine learning can then be leveraged in various ways to find novel catalysts. For AOR machine learning finds novel active catalysts from a diverse design space, which are then experimentally tested and verified. Through the use of our machine learning algorithm (TinNet) we also provide new insights into why the catalysts are more active, and suggest novel physics that can help design active catalysts. For NO3RR we use machine learning as a tool to help us understand the hurdles in catalyst design better which then guides our catalyst discovery. It is shown that CuPd could be a potential candidate and is also verified via experimental synthesis and performance testing.

Self-interaction error (SIE) is a commonly known problem that most Kohn-Sham density functional theory (KS-DFT) approximate functionals display to varying extents. It originates from the incomplete cancellation of the Coulomb self-repulsion by the approximate exchange functionals. This is one of the major challenges for DFT, and therefore increasing our understanding of it could have great benefits for future use of DFT. Herein we advance techniques to dissect, understand, and textit{avoid} SIE in new ways. Considering that KS-DFT requires solving the self-consistent field (SCF) equations, we first present a robust and economical SCF solver - the "Quasi-Newton Unitary Optimization with Trust region"(QUOTR) solver.[Slattery, et. al. textit{Phys. Chem. Chem. Phys.}, textbf{2024}, 26, 6557-6573] To be robust, the solver is a direct-minimization solver equipped with a trust region (TR); to be economical, the solver uses an L-BFGS approximate Hessian and a physically-relevant preconditioner. Coupling these two aspects together is a solver for the TR subproblem that exploits the low-rank structure of the L-BFGS Hessian. We demonstrate that QUOTR is useful, not only for obtaining KS-DFT wave functions in difficult cases, but also for solving for Hartree-Fock (HF) orbitals in challenging chemical systems containing Cr or Fm. Although not able to beat the low cost of traditional Roothaan-Hall (RH) solvers with acceleration, QUOTR is robust in its convergence at only a modest increase in computational cost. The many examples of SCF convergence problems when using semi-local KS-DFT functionals are known to be the result of a vanishing HOMO-LUMO gap, which is further the result of SIE. A major motivation for developing QUOTR came from our desire to understand the "true" (albeit unphysical) ground state solutions in cases where KS-DFT could not be converged by a traditional diagonalization-based SCF solver. We reinvestigate the relationship between the vanishing HOMO-LUMO gap and SCF non-convergence using our QUOTR solver. A set of difficult biological systems that had previously been shown to display convergence problems [Rudberg, et. al. textit{J. Phys.: Condens. Matter}, textbf{2012}, 24, 072202] was selected for deeper analysis. In addition to being able to obtain converged solutions, we analyze the resulting densities matrices in comparison to HF. The source of the vanishing HOMO-LUMO gaps is demonstrated to be incompatible eigenspectrums of spatially distant fragments in the peptides. We show that by using a local solver (QUOTR) with an appropriate initial guess, that a non-Aufbau filled stationary point can be found for vacuum-separated charged fragments. A systematic scan of all 20 natural amino acids for some common DFAs is used to examine the prevalence of predicted non-Aufbau filling. We find that hybrid functionals improve upon GGAs more than meta-generalized gradient approximations (GGAs) do, and range-separated functionals are much better - though not completely solving the problem. Having addressed SIE in biomolecules in terms of where charges comes from and where it goes, we finally analyze SIE on an orbital-by-orbital basis. We define the textit{genuine} exchange energy as the difference between the HF energy and the (self-interaction free) orthogonal Hartree product wave function energy. We propose that the Edmiston-Ruedenberg [Edmiston, et. al. textit{Rev. Mod. Phys.}, textbf{1963}, 35, 457-464] localized HF orbitals are the most appropriate HF frame for this analysis, due to their connection with the Hartree product wave function. Although the use of HF orbitals to quantify the genuine exchange energy is an approximation, we demonstrate that the error of total exchange energy is approximately 10-15% for a set of small molecules. The good performance of two popular GGAs is shown to arise with considerable error cancellation between orbitals (particularly core and valence). We also examine two orbital-dependent DFAs: the Perdew-Zunger self-interaction correction (PZ-SIC), and a generalization of the Hartree-Fock-Gopinathan (HFG) method. / Doctor of Philosophy / Much of theoretical chemistry is built on a formalism where electrons are described by single-particle functions, known as orbitals. Because the orbitals influence each other through a variety of interactions, it is not trivial to determine accurate approximations to the best set of orbitals for any given chemical system. The method for obtaining optimal mathematical forms of the orbitals is called the self-consistent field (SCF) procedure. In essence solving the SCF equations is a nonlinear optimization problem, requiring iterative solution techniques. Both of the major camps in the field of theoretical chemistry, density functional theory (DFT) and wave function methods, almost always start with an SCF calculation. In wave function methods, SCF is typically only the first step, in this case the SCF is done with an approximation called Hartree-Fock (HF), and a further calculation will include many-body effects through other means which are more computationally costly. In Kohn-Sham DFT (the most popular variety), the SCF equations contain approximations to part of the energy known as density functional approximations (DFAs) that in principle account for all many-body effects not included exactly. Thus, for KS-DFT, solving the SCF equations is basically all that needs to be done for a complete description of the system. For this reason DFT is attractive from a cost perspective, but the DFAs are only approximations and this can introduce unphysical errors. In this work, we focus on providing new solutions and perspectives on a major contributor to poor performance of KS-DFT calculations, the self-interaction error (SIE), which is not present in HF. The origin of the SIE in approximate KS-DFT is how the 2-body interactions of electrons are approximated. Classical electrostatics provides a simple formula for the repulsion energy of like-charged particles. When the quantum mechanical electron probability density is treated as a classical charge density then a single electron will unphysically interact with itself. What is missing is the nonclassical ``exchange" interaction, which in the HF method cancels the Coulomb self-repulsion. In KS-DFT this second contribution is approximated, and this causes the cancellation to be inexact. Thus, it is the treatment of these two energy contributions in fundamentally different ways that causes SIE to appear in approximate KS-DFT, but not in wave function methods. Problems with convergence of the SCF equations are more prevalent for approximate KS-DFT than for HF, and this has been attributed to SIE. To address the issue of poor SCF convergence we developed the ``Quasi-Newton Unitary Optimization with Trust region" (QUOTR) SCF solver. [Slattery, et. al. textit{Phys. Chem. Chem. Phys.}, textbf{2024}, 26, 6557-6573] This solver is robust in converging to minima in the energy surface, while also being economical in computational cost. In cref{ch:quotr} we demonstrate the usefulness of QUOTR for solving not only problems where KS-DFT SCF convergence is difficult, but also cases where even HF SCF is not simple. With the QUOTR solver in hand, in cref{ch:sie_pep} we reexamine the SCF non-convergence problem for some approximate KS-DFT functionals when applied to biological systems in vacuum. By comparing the spatial distribution of the electron density of our KS-DFT solutions to that of HF, we are able to pinpoint regions of the biological systems that donate and receive electron density relative to HF. As is already known, how approximate KS-DFT treats charged groups often is generally the source of the error. Therefore, we performed a systematic scan of all 20 naturally occurring amino acids to find combinations that might be problematic. Finally, in cref{ch:orb_anatomy} we investigate the source of SIE in approximate KS-DFT functionals on an orbital-by-orbital basis, and attempt to remove it from the calculation a priori. While the total density is unique, the orbital decomposition within KS-DFT is not. We therefore, first justify our choice of a particular set of orbitals as the most physically reasonable for our analysis. We find that two popular functionals display much error cancellation between orbitals to achieve good overall results. Two generalizations of KS-DFT for orbital-dependent approaches, which attempt to be free of SIE, are examined. Taken as a whole, this work examines SIE in KS-DFT both by improving convergence of the SCF procedure despite the presence of SIE (in order to understand what is really happening), and by dissecting the orbital structure of SIE, including for methods that attempt to be free of SIE.

Although the solid state may not usually be thought of as an environment suitable for chemical reactions under mild conditions, a growing number of organic compounds are known to undergo interesting and, in many cases, practically useful chemistry in the molecular crystal phase. Of particular interest are photochemical reactions occurring in molecular crystals, which possess a number of characteristic features that make them attractive to study using the methods of theoretical chemistry. Firstly, molecular packing and steric effects strongly influence the mechanistic course of reactions in the crystal phase, which in some cases enables clean and controllable chemistry, including synthetic reactions as well as reversibly switchable isomerisations accompanied by a change of the macroscopic properties of the crystal, such as shape and colour. Secondly, in part due to their fast (subpicosecond) timescales and relatively low conversion rates (of the order of a few per cent), many of these reactions present challenges to experimental techniques, which computer simulation methods are uniquely positioned to overcome. Finally, these systems lend themselves well to simulation using a hybrid combination of two ab initio electronic structure methods, one of which is used to describe the electronic excitation of a reactive molecule while the other is applied to the surrounding bulk lattice. This thesis describes the computational modelling of two such reactions: the syn-anti photoisomerisation of 7-(2-pyridyl)indole and the reversible cis-enol⇄trans-keto photoisomerisation of N-salicylidene-2-chloroaniline. The solid-state mechanisms and rates of both reactions are computed using the TD-DFT/DFT hybrid method, in the latter case validating a previously postulated reaction mechanism. Furthermore, the thermal (ground-state) tautomerisation reaction in the photochromic and non-photochromic polymorphs of N-salicylidene-2-chloroaniline is investigated through calculations at the DFT level of theory. The results of these calculations indicate that both polymorphs are thermochromic, but tautomeric equilibrium in the non-photochromic polymorph is more sensitive to temperature than in the photochromic polymorph. Additionally, a critical assessment is presented of the accuracy of the various emphab initio methods employed throughout this work.

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While much is known about the toxicity of polychlorinated biphenyls (PCBs), there are tens of thousands of natural and synthetic chemicals in the environment that can activate the aryl hydrocarbon receptor (AhR) and thus cause toxicity. Since it would be difficult to conduct studies of the toxicity of each and every compound, here is presented a new model based on first-principles taking into account the basic electronic and electron trans- fer characteristics of PCBs, but can be used to predict the toxicities of other AhR-active compounds. The predictive model is based on Density Functional Theory. The model predicts that the energy gap between highest occupied (HOMO) and lowest unoccupied (LUMO) molecular orbitals is the overarching indicator of toxicity of PCBs, but not the only factor. The model explains why chlorination of both para-positions is required for maximum toxic potency. To rank potency of PCBs, the dipole moment in relation to the most chemically active chlorine-sites is critical. The theory is consistent with the accepted toxic equivalency factor (TEF) model for these molecules and is also able to improve on ranking toxic potency of PCBs with similar TEFs. This new model also includes a 13th dioxin-like PCB, PCB 74, not considered in the current TEF model developed by the World Health Organization (WHO). The model was applied to HOMO-LUMO gap mea- surements of a set of PCBs and the measurements are consistent with the model. Values of HOMO-LUMO gap can also be used to predict bio-accumulation of PCBs. The model provides an in silico method to screen a wide range of chemicals to predict their ability to act as an AhR agonist.

The magnetic properties of the (FexNi1-x)2B family of materials are explored using DFT calculations utilizing the FPLO and SPR-KKR code packages. It is found that a uniaxial magnetocrystalline anisotropy exists at around x = 0.8 with a magnetocrystalline anisotropy energy at around 0.3 MJ/m^3. A calculation of the lattice constant for these materials were attempted but failed due to the emergence of local minima and the calculations of magnetic properties were instead done using lattice parameters interpolated between known experimental values.