This dissertation focuses on the computational investigation of gold(I) acyclic diaminocarbene (ADC) complexes and their application in homogeneous gold(I) catalysis. Chapter 2 is an in-depth computational investigation of the σ- and π-bonding interactions that make up the gold-carbene bond. Due to the inherent conformation flexibility of ADC ligands, distortions of the carbene plane can arise that disrupt orbital overlap between the lone pairs on the adjacent nitrogen atoms and the empty p-orbital of the carbene. This study investigated the affect these distortions have on the strength of the σ- and π-bonding interactions. This investigation demonstrated that while these distortions can affect the σ- and π-bonding interactions, the ADC ligand have to become highly distorted before any significant change in energy of either the σ- or π-bonding interactions occurs. Chapter 3 is a collaborative investigation between experimental and computational methods, DFT calculations were employed to support the experimental catalytic results and determine the role that steric effects have in controlling the regioselectivity of a long-standing electronically controlled gold(I)-catalyzed tandem 1,6-enyne cyclization/hydroarylation reaction with indole. This study demonstrated that by sterically hindering nucleophilic attack of indole at the favored position, nucleophilic attack would occur at a secondary position leading to the selective formation of the electronically unfavored product. Chapter 4 is a collaborative investigation between experimental and computational methods. DFT calculations were employed to investigate and rationalize the importance of secondary non-covalent interactions and their influence on the enantioselectivity of a gold(I)-catalyzed intramolecular hydroamination of allene reaction. Through computational investigation of the enantiodetermining step, and the non-covalent interactions present between 2′-aryl substituent and the rest of the catalyst, it was determined that the presence of CF3 group on the 3,5-position of the 2′-aryl ring is crucial to maintaining a more rigid chiral pocket leading to higher enantiomeric excesses in this dynamic system. This increased rigidity is believed to be attributable to the several weak non-covalent interactions that arise between the allene substrate or diisopropyl N-substituent and the fluorine atoms of the CF3 groups.

Stereodivergent and enantiodivergent pathways for the Friedel–Crafts reactions were computationally studied with DFT methods. This study aims to explain recently observed solvent-dependent regioselectivity, and enantioselectivity when cinchona catalyst is used. Deprotonation reaction, Frontier Kohn-Sham orbitals, dual descriptors, Mulliken charges, and Hirshfeld atomic charge for reactant were calculated and analyzed. The most probable position of electrophilic attack and nucleophilic attack in-silico predicted aligns with experimental observations. The calculation of the transition states on the anionic and neutral model in a vacuum show preference for the electrophilic attack in the para position. In comparison to the anionic system, the presence of potassium cation improves ortho/para selectivity and increases the energy barrier. For the key enantioselective step, 12 transition states were calculated which covers 4 representative product such: (R)-ortho, (S)-ortho, (R)-para, and (S)-para. The computational study suggests, that the presence of the cesium cation is essential for the arrangement of the reactant and catalyst in the transition state, which leads to observed selectivity.

Brief explanation and history of cinchona based Phase Transfer Catalysis (PTC). Studied aryl acetates in PTC, encompassing napthoyl, 6-methoxy napthoyl, phenyl and protected 4-hydroxy phenyl acetates. Investigated means of controlling the selectivity of the PTC reaction by changing the electrophile size, the ether side group size or by addition of inorganic salts. Found that either small or aromatic electophiles increased enantioselectivity more than aliphatic electrophiles, and that increasing the size of ether protecting group also increased selectivity. Positive effects of salt addition included either decreasing reaction time or increasing enantiomeric excess. Applied findings towards the synthesis of S-equol. Computational experiments working towards deducing the transition state between PTC and aryl acetate substrates.

<p>Automated reaction prediction has the potential to elucidate complex reaction networks for many applications in chemical engineering, including materials degradation, drug design, combustion chemistry and biomass conversion. Unlike traditional reaction mechanism elucidation methods that rely on manual setup of quantum chemistry calculations, automated reaction prediction avoids tedious trial-and-error learning processes and greatly reduces the risk of leaving out important reactions. Despite these promising advantages, the potential of automated reaction prediction as a general-purpose tool is still largely unrealized, due to high computational cost and inconsistent reaction coverage. Therefore, this dissertation develops methods to simultaneously reduce the computational cost and increase the reaction coverage. Specifically, the computational cost is reduced by the development of more efficient transition state (TS) localization workflows and fast molecular and reaction property prediction packages, while the reaction coverage is increased by a comprehensive reaction space exploration based on mathematically defined elementary reaction steps. These components are implemented in two open-source packages, one is TAFFI (Topology Automated Force-Field Interactions) component increment theory (TCIT) and the other is Yet Another Reaction Program (YARP).</p> <p><br></p> <p>The first package, TCIT, is the first component increment theory based molecular property prediction package. TCIT is based on the locality assumption, which decomposes molecular thermochemistry properties into the summation of the contributions of each subgraph. In contrast to the traditional "group" increment theory, TCIT treats each subgraph as the central atom plus its nearest and next-nearest neighboring atoms, and consistently parameterizes the contribution of each component according to purely quantum chemistry calculations. Although all parameterizations are based on quantum chemical calculations, when benchmarked against experimental data, TCIT provides more accurate predictions compared to traditional methods using the same experimental dataset for parameterization. With TCIT, the molecular properties (e.g., enthalpy of formation) and reaction properties (e.g., enthalpy of reaction) can be accurately predicted in an on-the-fly manner. The second package, YARP, is developed for automated reaction space exploration and deep reaction network prediction. By optimizing the reaction enumeration, geometry initialization, and transition state convergence algorithms that are common to many prediction methodologies, YARP (re)discovers both established and unreported reaction pathways and products while simultaneously reducing the cost of reaction characterization nearly 100-fold and increasing convergence of transition states, comparing with recent benchmarks. In addition, an updated version of YARP, YARP v2.0, further reduces the cost of reaction characterization from 100-fold to 300-fold, while increasing the reaction coverage beyond the scope of elementary reaction steps. This combination of ultra-low cost and high reaction-coverage creates opportunities to explore the reactivity of larger systems and more complex reaction networks for applications like chemical degradation, where computational cost is a bottleneck.</p> <p><br></p> <p>The power of TCIT and YARP has been demonstrated by a broad range of applications. In the first application, YARP was used to explore the reactivity of unimolecular and bimolecular reactants, comprising a total of 581 reactions involving 51 distinct reactants. The algorithm discovered all established reaction pathways, where such comparisons are possible, while also revealing a much richer reactivity landscape, including lower barrier reaction pathways and a strong dependence of reaction conformation in the apparent barriers of the reported reactions. Secondly, YARP was applied to the search for prebiotic chemical pathways, which is a long-standing puzzle that has generated a menagerie of competing hypotheses with limited experimental prospects for falsification. With YARP, the space of organic molecules that can be formed within four polar or pericyclic reactions from water and hydrogen cyanide (HCN) was comprehensively explored. A surprisingly diverse reactivity landscape was revealed within just a few steps of these simple molecules and reaction pathways to several biologically relevant molecules were discovered involving lower activation energies and fewer reaction steps compared with recently proposed alternatives. In the third application, predicting the reaction network of glucose pyrolysis, YARP generated by far the largest and most complex reaction network in the domain of biomass pyrolysis and discovered many unexpected reaction mechanisms. Further, motivated by the fact that existing reaction transition state (TS) databases are comparatively small and lack chemical diversity, YARP, together with the concept of a graphically defined model reaction, were utilized to address the data gap by comprehensively characterizing a reaction space associated with C, H, O, and N containing molecules with up to 10 heavy (non-hydrogen) atoms. The resulting dataset, namely Reaction Graph Depth 1 (RGD1) dataset, is composed of 176,992 organic reactions possessing at least one validated TS, activation energy, enthalpy of reaction, reactant and product geometries, frequencies, and atom-mapping. The RGD1 dataset represents the largest and most chemically diverse TS dataset published to date and should find immediate use in developing novel machine learning models for predicting reaction properties. In addition to exploring the molecular reaction space, YARP was also extended to explore and characterize reaction networks in heterogeneous catalysis systems. With ethylene oligomerization on silica-supported single site Ga catalysts as a model system, YARP illustrates how a comprehensive reaction network can be generated by using only graph-based rules for exploring the network and elementary constraints based on activation energy and system size for identifying network terminations. The automated reaction exploration (re)discovered the Ga-alkyl-centered Cossee-Arlman mechanism that is hypothesized to drive major product formation while also predicting several new pathways for producing alkanes and coke precursors. The diverse scope of these applications and milestone quality of many of the reaction networks produced by YARP  illustrate that automated reaction prediction is approaching a general-purpose capability.</p>

<p>Radical polymers bearing open-shell moieties at their pendant sites offer potential advantages in processing, stability, and optoelectronic properties compared to conventional doped conjugated polymers. The rapid development of radical-containing polymers has occurred across various applications in energy storage devices and electronic systems. However, significant gaps still exist in understanding the key structure-property-function relationships governing charge transport phenomena in these materials. Most reported radical conductors primarily rely on (2,2,6,6-tetramethylpiperidin-1-yl)oxyl (TEMPO) radicals, which raises fundamental questions about the ultimate limits of charge transport capabilities and the impact of radical chemistry choice on material deficiencies. Moreover, an understanding gap persists when it comes to connecting the computable electronic features of individual units and the charge transport behavior of these materials in condensed phases. This dissertation seeks to address these gaps by developing a molecular understanding of charge transport in radical-bearing materials through a combined computational and experimental approach.</p> <p><br></p> <p>The initial stage of this dissertation investigated the impact of dimeric orientations and interactions on charge transport by conducting a density functional theory (DFT) study on a diverse set of open-shell chemistries relevant to radical conductors. The results revealed the anomalously high reorganization energies of the TEMPO radical due to strong spin-localization, which may result in inefficient charge transfer. Additionally, a significant mismatch was identified between dimeric conformations favored by intermolecular interactions and those maximizing charge transfer. This study provided new insights into the impact of steric hindrance and spin delocalization on elementary charge transfer steps and suggests opportunities for exploiting directing interactions to enhance charge transport in these materials.</p> <p><br></p> <p>Building upon these findings, we established a direct relationship between the molecular architecture and intrinsic charge transport properties. To accomplish this, single-molecule characterization methods (i.e., break junction techniques) were implemented to study the nanoscale charge transport properties of radical-containing oligomeric nonconjugated molecules. Temperature-dependent measurements and molecular modeling revealed that the presence of radicals improves tunneling at the nanoscale. Integrating open-shell moieties into nonconjugated molecular structures significantly enhances charge transport, thereby characterizing charge transport through radicals at the individual level and opening new avenues for implementing molecular engineering in the field of nanoelectronics.</p> <p><br></p> <p>To further connect the electronic properties of repeat units with the condensed-phase charge transport behavior of radical polymers, a quantum chemical study was carried out to explicitly evaluate the interplay between polymer design, open-shell chemistries, and intramolecular charge transport. After comprehensive conformational sampling of the configurational space of radical polymers, we determined their anticipated intrachain charge transport values by utilizing graph-based transport metrics. We show that charge transport in radical polymers primarily hinges on the choice of radical chemistry, which in turn affects the optimal selection of backbone chemistry and spacer group to ensure proper radical alignment and prevent undesired trap states. These findings highlight the potential for a substantial synthetic exploration in radical polymers for radical conductors.</p> <p><br></p> <p>In summary, this dissertation provides compelling evidence of radical-mediated charge transport and suggests potential design guidelines to enhance the charge transfer behavior of radical-containing polymer materials. Furthermore, these findings inform future research directions in fine-tuning molecular engineering and modular design to enable the development of radical-based materials and their end-use applications in organic electronics.</p>

Redox reactions are crucial to biological processes that protect organisms against oxidative stress. Metalloenzymes, such as cytochrome c peroxidases which reduce excess hydrogen peroxide into water in the periplasm of multiple bacterial organisms, play a key role in detoxification mechanisms. While accurate computational tools can be used to simulate ground state redox potentials in biomolecules, adapting such approaches to properly describe redox reactions in transition metal complexes, particularly in hemes in heterogeneous protein environments, remains a significant challenge. Here we present the results of polarizable hybrid QM/MM studies of the reduction potentials of two heme sites in the cytochrome c peroxidase of Nitrosomonas europaea. The simulated redox potential of the catalytic site Low Potential (LP) is in good agreement with the experiment, while for the High Potential (HP) heme the computational estimate significantly overestimate the experimental value. We have found that environment polarization shifts the computed value of the redox potential of the catalytic LP heme by 1.3 V, while it does not affect that of the non-catalytic High Potential (HP) heme. We demonstrate that it is necessary to account for mutual polarization of heme site and the protein environment when describing redox processes, particularly those that involve more charged heme sites. We have explored the role of various factors such as heme geometries, axial ligands, propionate side chains, and electrostatic field of the protein in tuning the redox potentials of hemes in NeCcP. The fluctuations in computed vertical ionization and electron attachment energies are predominantly affected by fluctuations in the electrostatic field of the environment but not by fluctuations in heme geometries. We attribute the difference in computed LP and HP heme reduction potentials of 0.05 V and 1.15 V, respectively, to different axial ligands and electrostatic interactions of the hemes with the protein environment. / 2023-06-30T00:00:00Z

Computational chemistry is a branch of modern chemistry that utilizes the computers to solve chemical problems. The fundamental of computational chemistry is Schrödinger equation. To solve the equation, researchers developed many methods based on BornOppenheimer Approximation, such as Hartree-Fock method and DFT method, etc. Computational chemistry is now widely used on reaction mechanism study and new chemical designing. In the first project described in Chapter 3, we designed phosphine oxide modified Ag3, Au3 and Cu3 nanocluster catalysts with DFT method. We found that these catalysts were able to catalyze the activation of H2 by cleaving the H-H bond asymmetrically. The activated catalyst-2H complex can be further used as reducing agent to hydrogenate CO molecule to afford HCHO. The mechanism study of these catalysts showed that the electron transfer from electron-rich metal clusters to O atom on the phosphine oxide ligand is the major driving force for H2 activation. In addition, different substituent groups on phosphine oxide ligand were tested. Both H affinity of metal and the substituent groups on ligand can both affect the activation energy. Another project described in Chapter 4 is the modelling of catalyst with DFT. We chose borane/NHC frustrated Lewis pair (FLP) catalyzed methane activation reaction as example to establish a relationship between activation energy and catalysts’ physical properties. After performing simulation, we further proved the well-accepted theory that the electron transfer is the main driving force of catalysis. Furthermore, we were able to establish a linear relationship for each borane between activation energy and the geometrical mean value of HOMO/LUMO energy gap (ΔEMO). Based on that, we introduced the formation energy of borane/NHC complex (ΔEF) and successfully established a generalized relationship between Ea and geometrical mean value of ΔEMO and ΔEF. This model can be used to predict reactivity of catalysts.

Low energy electron (LEE) driven chemistry underpins a wide range of interdisciplinary fields, including radiation biology, redox chemistry, astrochemistry and biomaterial design. A growing interest in the chemistry of LEEs concerns the radiative damage to DNA. Studies have found that LEEs can induce single and double-strand breaks in DNA by forming a negative ion resonance (NIR). These processes are remarkably site-specific and have been utilized to synthesize radiosensitizers, which aid in identifying target cells in hypoxic tumors in radiation therapy. Despite the prevalence of LEE-induced reactions, computational studies of such processes are limited compared to thermal and photochemical reactions. The relative scarcity in computational studies of LEE-induced reactions stems from the difficulties in the theoretical treatment of NIRs. In our work, we report new developments on the application of quantum chemical methods to NIRs. We demonstrate that the combination of approaches developed for resonances with multi reference electronic structure methods enables the computation of various types of NIRs in a single calculation. Additionally, we show that multi-reference methods can also quantify the mixing between NIRs. It is observed that the mixing between resonances can have significant consequences on their lifetimes. We also report the development of a new technique, the continuum remover Feshbach projection operator approach, which uses the conventional methods developed for bound states to characterize resonances. We show that this new approach is straightforward to implement with standard electronic structure packages, it is efficient, and provides promising results. / Chemistry

The knowledge of the packing behaviour of small organic compounds in crystal lattices is of great importance for industries dealing with solid state materials. The properties of materials depend on how the molecules arrange themselves in a crystalline environment. Crystal structure prediction provides a theoretical approach through the application of computational strategies to seek possible crystal packing arrangements (or polymorphs) a compound may adopt. Based on the chemical diagrams, this thesis investigates polymorphism of several small organic compounds. Plausible crystal packings of those compounds are generated, and their lattice energies are minimised using molecular mechanics and/or quantum mechanics methods. Most of the work presented here is conducted using two software packages commercially available in this field, Polymorph Predictor of Materials Studio 4.0 and GRACE 1.0. In general, the computational techniques implemented in GRACE are very good at reproducing the geometries of the crystal structures corresponding to the experimental observations of the compounds, in addition to describing their solid state energetics correctly. Complementing the CSP results obtained using GRACE with isostructurality offers a route by which new potential polymorphs of the targeted compounds might be crystallised using the existing experimental data. Based on all calculations in this thesis, four new potential polymorphs for four different compounds, which have not yet been determined experimentally, are predicted to exist and may be obtained under the right crystallisation conditions. One polymorph is expected to crystallise under pressure. The remaining three polymorphs might be obtained by using a seeding technique or the utilisation of suitable tailor made additives. / University of Bradford

<p dir="ltr">Chemical reaction networks govern polymer degradation and contain critical design information regarding specific susceptibilities, degradation pathways, and degradants. However, predicting reaction pathways and characterizing complete reaction networks has been hindered by high computational costs because of the vast number of possible reactions at deeper levels of network exploration. In the first section, an exploration policy based on Dijkstra's algorithm on YARP using the reaction rate as a cost function was shown to provide a tractable means of exploring the pyrolytic degradation network of a representative commodity polymer, PEG. The resulting network is the largest reported to date for this system and includes pathways out to all degradants observed in earlier mass spectrometry studies. The initial degradation pathway predictions were validated by complementary experimental analysis of pyrolyzed PEG samples by ESI-MS. These findings demonstrate that reaction network characterization is reaching sufficient maturity to be used as an exploratory tool for investigating materials degradation and interpreting experimental degradation studies.</p>