No / Cocrystallization of modafinil, (1), and 1,4-diiodotetrafluorobenzene, (2), in toluene leads to the formation of a metastable modafinil channel hydrate containing an unusual hydrogen bonded dimer motif involving the modafinil molecules that is not seen in anhydrous forms of modafinil. Computational methodologies utilizing bias drift-free differential evolution optimization have been developed and applied to a series of molecular clusters and multicomponent crystals in the modafinil/water and modafinil/water/additive systems for the additive molecules (2) or toluene. These calculations show the channel hydrate is less energetically stable than the anhydrous modafinil but more stable than a cocrystal involving (1) and (2). This provides theoretical evidence for the observed instability of the channel hydrate. The mechanism for formation of the channel hydrate is found to proceed via templating of the modafinil molecules with the planar additive molecules, allowing the formation of the unusual hydrogen-bonded modafinil dimer. It is envisaged that the additive is then replaced by water molecules to form the channel hydrate. The formation of the channel hydrate is more likely in the presence of (2) compared to toluene due to the destabilizing effect of the larger iodine molecules protruding into neighboring modafinil clusters. / Science Foundation Ireland, IRCSET, UCC 2012 Strategic Research Fund

<div> <div> <div> <p>Since their commercialization in the early 1990’s, rechargeable lithium ion batteries (LIBs) have become ever-present in consumer electronics, and the share of electric vehicles within the transportation sector has become much more significant. <i>Ab initio</i> modeling techniques - namely density functional theory (DFT) - have played a signifcant role in describing the atomic scale nature of Li+ insertion and removal chemistry in LIB electrode materials, and have been pivotal in accelerating the design of energy dense battery materials based on their bulk properties. Despite these advances, there remains a knowledge gap with respect to understanding the many complex reactions that occur at the surfaces and interfaces of rechargeable battery materials. This work considers several case studies of surface and interfacial reactions in energy storage materials, using DFT modeling techniques to develop strategies that can rationally control the interfacial chemistry for optimal electrochemical performance. </p><p><br></p><p> </p><div> <div> <div> <p>The first portion of this thesis aims to understand the role of interfacial modification strategies toward mitigating Mn dissolution from the spinel LiMn2O4 (LMO) surface. First, a thermodynamic characterization of LMO surface structures is performed in order to develop models of LMO substrates for subsequent computational surface science studies. A subset of these surface models are then used analyze interfacial degradation processes through delithiation-driven stress buildup and crack formation, as well as reaction mechanisms for ethylene carbonate and hydrofluoric acid to form surface Mn2+ ions that are susceptible to dissolution. Surface passivation mechanisms using protective oxide and metallic coatings are then analyzed, which elucidate an electronic structure-based descriptor for structure-sensitive atomic layer growth mechanisms and describe the changes in lithiation reactions of coated electrodes through electronic band alignment at the solid-solid interface. These studies of protective coatings describe previously overlooked physics at the electrode-coating interface that can aid in further development of coated electrode materials. Using the LMO substrate models, a thermodynamic framework for evaluating the solubility limits and surface segregation tendencies of cationic dopants is described in the context of stabilizing LMO surfaces against Mn loss. </p><p><br></p><p> </p><div> <div> <div> <p>Next, solid-solid interfacial models are developed to evaluate the role of nanostructure in catalyzing the lithiation of NiO to form reduced Ni and Li2O as concurrent discharge products. Applying a Ni/NiO multilayer morphology, interfacial energies are evaluated using DFT and implemented into a classical nucleation model at a heterogeneous interface. These calculations, alongside <i>operando</i> X-ray scattering measurements, are used to explain atomic scale mechanisms that reduce voltage hysteresis in metal oxide LIB conversion chemistry. </p><p><br></p><p> </p><div> <div> <div> <p>The structure between a Li metal anode and the lithium lanthanum titanate solid electrolyte are subsequently analyzed as a model system to understand potential inter- facial stabilization mechanisms in solid-state batteries. This analysis combines bulk, surface, and interfacial thermodynamics with <i>ab initio</i> molecular dynamics simulations to monitor the evolution of the interfacial structure over short time scales, which provides insights into the onset of degradation mechanisms. It is shown that the reductive instability of Ti4+ is the primary driving force for interfacial decomposition reactions, and that a lanthanum oxide interlayer coating is expected to stabilize the interface based on both thermodynamic and electronic band alignment arguments. </p><p><br></p><p> </p><div> <div> <div> <p>In the last part of this thesis, charge transfer kinetics are studied for several applications using constrained DFT (cDFT) to account for electronic coupling and reorganization energies between donor and acceptor states. Charge hopping mechanisms to and from dichalcogenide-based electrocatalysts during O2 and CO2 reduction/evolution reactions in Li-O2 and Li-CO2 battery systems are first evaluated. Then, the role of the spatial separation Li+ vacancies and interstitials on hole and electron polaron hopping in the prototypical LixCoO2 cathode is analzyed. The results demonstrate that Marcus rate theories using cDFT-derived parameters can reproduce experimentally observed anisotropies in electronic conductivity, whereas conventional transition state theory analyses of polaron hopping do not. Overall, this proof-of-concept study provides a framework to understand how charged species are transported in battery electrodes and are dependent on charge compensating defects.</p><p><br></p> <p>Finally, the key insights from these studies are discussed in the context of future directions related to the understanding and design of materials for electrochemical energy conversion and storage. </p> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>

Computational methods were employed to investigate catalytic processes. First, DFT calculations predicted the important geometry metrics of a copper–nitrene complex. MCSCF calculations supported the open-shell singlet state as the ground state of a monomeric copper nitrene, which was consistent with the diamagnetic character deduced from experimental observations. The calculations predicted an elusive terminal copper nitrene intermediate. Second, DFT methods were carried out to investigate the mechanism of C–F bond activation by a low-coordinate cobalt(I) complex. The computational models suggested that oxidative addition, which is very rare for 3d metals, was preferred. A π–adduct of PhF was predicted to be a plausible intermediate via calculations. Third, DFT calculations were performed to study ancillary ligand effects on C(sp3)–N bond forming reductive elimination from alkylpalladium(II) amido complexes with different phosphine supporting ligands. The dimerization study of alkylpalladium(II) amido complexes indicated an unique arrangement of dative and covalent Pd-N bonds within the core four-membered ring of bimetallic complexes. In conclusion, computational methods enrich the arsenal of methods available to study catalytic processes in conjunction with experiments.

Molecules in the ‘X-C≡N’ chemical family serve as markers for chemical processes happening in various regions of space and are members of the prebiotic molecular pool, which makes them important in astrochemistry and astrobiology. Although these kinds of molecules have been identified in the interstellar medium, cometary comae, plumes of Enceladus, meteorites, and around young stellar objects, it is not clear which mechanisms are responsible for their formation. However, it has been suggested that they may serve as precursors to prebiotically important compounds, such as amino acids and nucleobases. In this work, a theoretical computational study was conducted using quantum mechanical approaches to predict properties of sixteen astrochemically relevant ‘X-C≡N’ molecules. To perform this study, General Atomic and Molecular Electronic Structure System (GAMESS(US)) and AutoGAMESS software were used to calculate optimized geometries, harmonic vibrational frequencies, infrared intensities, and Raman activities of each molecule using density functional theory (BLYP, B3LYP, PBE, and PBE0) and second order Møller-Plesset perturbation theory (MP2, SCS-MP2) paired with several basis sets (6-311++G(d,p), def2QZVPD, Sadlej-pVTZ, and aug-cc-pVQZ). Geometries and frequencies were additionally calculated using coupled cluster approaches (CCSD, CCSD(T), and CCSD(2)T) to help assess accuracy and reliability of the other calculations. For many of these species, experimentally and computationally determined Raman activities have not been reported in the literature. We assess the reliability of our calculations in comparison to previous works and discuss how the implementation of both Raman and infrared spectroscopy can offer new insights into potential reaction mechanisms linking these prebiotically relevant compounds.

Drug discovery has witnessed an increase in the application of in silico methods to complement existing in vitro and in vivo experiments, in an attempt to 'fail fast' and reduce the high attrition rates of clinical phases. Computer algorithms have been successfully employed for many tasks including biological target selection, hit identification, lead optimization, binding affinity determination, ADME and toxicity prediction, side-effect prediction, drug repurposing, and, in general, to direct experimental work. This thesis describes a multifaceted approach to virtual screening, to computationally identify small-molecule inhibitors against a biological target of interest. Conformer generation is a critical step in all virtual screening methods that make use of atomic 3D data. We therefore analysed the ability of computational tools to reproduce high quality, experimentally resolved conformations of organic small-molecules. We selected the best performing method (RDKit), and developed a protocol that generates a non-redundant conformer ensemble which tends to contain low-energy structures close to those experimentally observed. We then outline the steps we took to build a multi-million, small-molecule database (including molecule standardization and efficient exact, substructure and similarity searching capabilities), for use in our virtual screening experiments. We generated conformers and descriptors for the molecules in the database. We tagged a subset of the database as `drug-like' and clustered this to provide a reduced, diverse set of molecules for use in more computationally-intensive virtual screening protocols. We next describe a novel virtual screening method we developed, called Ligity, that makes use of known protein-ligand holo structures as queries to search the small-molecule database for putative actives. Ligity has been validated against targets from the DUD-E dataset, and has shown, on average, better performance than other 3D methods. We also show that performance improved when we fused the results from multiple input structures. This bodes well for Ligity's future use, especially when considering that protein structure databases such as the Protein Data Bank are growing exponentially every year. Lastly, we describe the fruitful application of structure-based and ligand-based virtual screening methods to Plasmodium falciparum Subtilisin-like Protease 1 (PfSUB1), an important drug target in the human stages of the life-cycle of the malaria parasite. Our ligand-based virtual screening study resulted in the discovery of novel PfSUB1 inhibitors. Further lead optimization of these compounds, to improve binding affinity in the nanomolar range, may promote them as drug candidates. In this thesis we postulate that the accuracy of computational tools in drug discovery may be enhanced to take advantage of the exponential increase of experimental data and the availability of cheaper computational power such as cloud computing.

Concerns over industrial emissions and nuclear waste have led to the need to study ways to sequester industrial gasses, and recycle nuclear fuel. Two projects were done to study solvent systems for these two problems using computational methods. Current methods for SO₂ sequestration are wasteful in that the gasses cannot be extracted from the solvent, and the solvent cannot be reused. One possible solution, which this work focuses on, is the use of an ionic liquid as a sequestration agent for the adsorption of SO₂. Separation technology for heavy elements has not changed for over 60 years and issues with radiation contamination and low efficiency lead to high solvent waste. Biphasic alkane-water extraction systems are a possible solution as they have been used for the extraction of heavy elements. This work focuses on characterizing the factors that control partitioning in biphasic systems which increase extraction efficiency.

Interactions between single and double stranded oligonucleotides with SiO2 surfaces and the interactions between oligonucleotides and immobilized oligomers have been studied computationally. The oligonucleotide is the 18-base-pair sequence for the survival motor neuron gene SMN1. The oligomer consisted of a 50 unit 2-hydroxyethyl methacrylate (PHEMA) molecule. A linker used to tether the oligonucleotide was either a 10 Å or a 30 Å long succinimdyl 4-[N-maleimidomethyl]cyclohexane-1-caroxylate (sulfo-SMCC-Cn). The surface consisted of a SiO2 crystal that was 50 Å long and 50 Å wide, one unit thick and covered with modified-(3-aminopropyl)trimethoxysilane (m-APTMS) molecules. It was determined that explicit water, sodium counterions and excess salt were necessary to produce computationally stable oligonucleotide structures on surfaces. Artificial partial charges were introduced to the surface, and linkers, oligomers and oligonucleotides were immobilized and studied. The linkers collapsed onto a positive but not onto a negative surface. Oligomers moved closer to the SiO2 surface regardless of the surface charge. Immobilized oligonucleotides tilted significantly from an initial upright position but did not collapse completely onto the surfaces. The interactions between immobilized oligonucleotides and oligomers were examined. The number of oligomers surrounding the oligonucleotide was varied between two and four. Single stranded oligonucleotides were prevented from interacting with the surface as they were inhibited by the presence of oligomers. Double stranded oligonucleotides collapsed onto the surface when only two oligomers were present but remained upright when four oligomers were present. This was due to the four oligomers interacting with one another and effectively shielding the surface. The oligomers interacted with the bases in the single stranded oligonucleotides, making them energetically accessible. Presence of a high density of oligomers prevented the dsDNA from collapsing onto the surface. These results suggest design criteria for preparation of mixed oligonucleotide and oligomer films for use in biosensors.

Water monomer and dimer encapsulations into D-2(22)-C-84 fullerene are evaluated. The encapsulation energy is computed at the M06-2X/6-31++G** level, and it is found that the monomer and dimer storage in C-84 yields an energy gain of 10.7 and 17.4kcalmol(-1), respectively. Encapsulation equilibrium constants are computed by using partition functions based on the M06-2X/6-31G** and M06-2X/6-31++G** molecular data. Under high-temperature/high-pressure conditions, similar to that for the encapsulation of rare gases in fullerenes, the computed (H2O)(2)@C-84-to-H2O@C-84 ratio is close to 1:2.

Computer-based drug design is a vital area of pharmaceutical chemistry; Quantitative Structure-Activity Relationships (QSARs), determined computationally from experimental observations, are crucial in identifying candidate drugs by early screening, saving time on synthesis and in vivo testing. This thesis investigates the viability and the practicalities of using Mass Spectra-based pseudo-molecular descriptors, in comparison with other molecular descriptor systems, to predict the carcinogenicity, mutagenicity and the Cltransport inhibiting ability of a variety of molecules, and in the first case, of chemotherapeutic drugs particularly. It does so by identifying a number of QSARs which link the physical properties of chemicals with their concomitant activities in a reliable and mathematical manner. First, this thesis confirms that carcinogenicity and mutagenicity are indeed predictable using a variety of Artificial Intelligence techniques, both supervised and unsupervised, information germane to pharmaceutical research groups interested in the preliminary screening of candidate anti-cancer drugs. Secondly, it demonstrates that Mass Spectral intensities possess great descriptive fidelity and shows that reducing the burden of dimensionality is not only important, but imperative; selecting this smaller set of orthogonal descriptors is best achieved using Principal Component Analysis as opposed to the selection of a set of the most frequent fragments, or the use of every peak up to a number determined by the boundaries of supervised learning. Thirdly, it introduces a novel system of backpropagation and demonstrates that it is more efficient than its principal competitor at monitoring a series of connection weights when applied to this area of research, which requires complex relationships. Finally, it promulgates some preliminary conclusions about which AI techniques are applicable to certain problem-scenarios, how these techniques might be applied, and the likelihood that that application will result in the identification of series of reliable QSARs.

The most critical biochemistry in an organism supports the central dogma of molecular biology: transcription of DNA to RNA and translation of RNA to peptide sequence. Proteins are then responsible for catalyzing, regulating and ensuring the fidelity of transcription and translation. At the heart of these processes lie selective biomolecular interactions and specific dynamics that are necessary for complex formation and catalytic activity. Through advanced biophysical and computational methods, it has become possible to probe these macromolecular dynamics and interactions at the molecular and atomic levels to tease out their underlying physical bases. To the end of a more thorough understanding of these physical bases, we have performed studies to probe the motions and interactions intrinsic to the function of biomolecular complexes: modeling the dual-base flipping strategy of alkylpurine glycosylase D, dynamically tracing evolution and epistasis in the 3-ketosteroid family of nuclear receptors, discovering the allosteric and conformational aspects of transcription regulation in liver receptor homologue 1, leveraging specific contacts in tyrosyl-DNA phosphodiesterase 2 for the development of novel inhibitor scaffolds, and detailing the experimentally observed connection between solvation and sequence-specific binding affinity in PU.1-DNA complexes at the atomic level. While each study seeks to solve system-specific problems, the collection outlines a general and broadly applicable description of the biophysical motivations of biochemical processes.