<p dir="ltr">A major goal of applied electrocatalysis research has been the development of electrode materials that are active, selective, stable, and cost effective in producing electricity or desired products. In recent years, developments in <i>ab initio</i> methods for the simulation of catalyst surfaces, and electrochemical reactions occurring on them, have enabled the development of a fundamental understanding of the processes occurring at the solid-liquid interface at an atomistic scale. In combination with experiments, these calculations are helpful in elucidating design principles that can then inform electrocatalyst design. In this work, we describe the application of density functional theory, <i>ab initio</i> molecular dynamics, and high throughput materials informatics approaches to understand oxygen and carbon based electrochemistries, with relevance to electricity conversion and environmental protection. We also introduce an approach, based on a Born-Haber cycle analysis, to quantify adsorbate stabilization from solvent molecules that are ubiquitous for any electrochemical reaction occurring at solid-liquid interfaces.</p><p dir="ltr">The oxygen reduction reaction (ORR) occurs at the cathode in hydrogen fuel cells and, in conjunction with the hydrogen oxidation reaction (HOR) at the anode, produces electricity and water. While platinum group metals are the current state-of-the-art catalysts for the ORR, their high cost has necessitated an extensive search for alternatives. To this end, we investigated iron-nitrogen-carbon (Fe-N-C) catalysts, which are platinum group metal-free and have been shown experimentally to have reasonable activity compared to platinum. Despite their potential as cost effective materials, however, these catalysts are not durable over long-term operation of fuel cells, impeding their commercial adoption. The mechanisms of deactivation of the iron-nitrogen-carbon catalysts under aqueous acidic electrochemical reaction conditions remain debated, and deciphering them is complicated due to the complex structure of the catalyst. We attempt to address these challenges by first examining the structural aspects of the catalyst, sampling numerous potential active site configurations, determining their in-situ structure, and linking them to intrinsic activity and intrinsic stability descriptors. Our findings reveal that an activity-stability tradeoff exists, with the most active sites being most prone to stability issues. Additionally, we explored the role of hydrogen peroxide, a side product of ORR, in degrading Fe-N-C catalysts. This analysis demonstrated that hydrogen peroxide strongly oxidizes the catalyst surface, resulting in an activity loss in the catalyst. Based on these insights, we propose design principles to enhance the activity and stability of Fe-N-C catalysts.</p><p dir="ltr">In additional work, we compared the predictions for the Fe-N-C catalysts with ORR analysis on platinum catalysts, and we further analyzed the oxygen evolution reaction (OER) on iridium oxides and the carbon dioxide reduction reaction (CO₂R) on copper catalysts in water electrolyzers. For ORR on platinum, we identified the formation of hydroxyl and water adsorbate rings on stepped surfaces, akin to hexagonal rings found on terraces but largely absent on Fe-N-C catalysts. The ORR follows an associative mechanism involving proton coupled electron transfer to these ring structures. Furthermore, we provided activity descriptors that aligned with experimental observations, showing a higher activity on stepped surfaces compared to terraces. For OER on iridium oxides, we examined transformations of IrO₂ (110) surfaces, and we pinpointed oxidation of bridge and coordinatively unsaturated top sites as key charge transfer steps that correlate with peaks in cyclic voltammograms. Finally, for CO₂R on copper, we investigated the role of water as a proton source under neutral or alkaline conditions, providing insights into the effect of coverages of surface species on the kinetics of water dissociation that, in turn, can provide protons for CO₂ reduction and the competing hydrogen evolution reaction.</p><p dir="ltr">Through this work, we have gained a deeper understanding of the properties of various catalytic materials under conditions specific to each type of electrochemistry. We elucidated the relationships between the in-situ structure, activity, and stability for the electrocatalysts, and identified key factors influencing catalyst performance. Integrating such insights from a computational perspective with experimental approaches holds great potential in making significant advancements in developing sustainable energy technologies and ultimately contributing to a greener and more energy-efficient future.</p>

Accurate modeling of structural ensembles is essential for understanding molecular function, predicting molecular interactions, refining molecular potentials, protein engineering, drug discovery, and more. Here, we enhance molecular modeling through Bayesian Inference of Conformational Populations (BICePs), a highly versatile algorithm for reweighting simulated ensembles with experimental data. By incorporating replica-averaging, improved likelihood functions to better address systematic errors, and adopting variational optimization schemes, the utility of this algorithm in the refinement and validation of both structural ensembles and empirical models is unmatched. Utilizing a set of diverse experimental measurements, including NOE distances, chemical shifts, and vicinal J-coupling constants, we evaluated nine force fields for simulating the mini-protein chignolin, highlighting BICePs’ capability to correctly identify folded conformations and perform objective model selection. Additionally, we demonstrate how BICePs automates the parameterization of molecular potentials and forward models—computational frameworks that generate observable quantities—while properly accounting for all sources of random and systematic error. By reconciling prior knowledge of structural ensembles with solution-based experimental observations, BICePs not only offers a robust approach for evaluating the predictive accuracy of molecular models but also shows significant promise for future applications in computational chemistry and biophysics. / Chemistry

The first study project is based on modeling Earth abundant 3d transition-metal methoxide complexes with potentially redox-noninnocent ligands for methane C–H bond activation to form methanol (LnM-OMe + CH4 → LnM–Me + CH3OH). Three types of complex consisting of tridentate pincer terpyridine-like ligands, and different first-row transition metals (M = Ti, V, Cr, Mn, Fe, Co, Ni, and Cu) were modeled to elucidate the reaction mechanism as well as the effect of the metal identity on the thermodynamics and kinetics of a methane activation reaction. The calculations showed that the d electron count of the metal is a more significant factor than the metal's formal charge in controlling the thermodynamics and kinetics of C–H activation. These researches suggest that late 3d-metal methoxide complexes that favor σ-bond metathesis pathways for methane activation will yield lower barriers for C–H activation, and are more profitable catalyst for future studies. Second, subsequently, on the basis of the first project, density functional theory is used to analyze methane C−H activation by neutral and cationic nickel-methoxide complexes. This study identifies strategies to further lower the barriers for methane C−H activation through evaluation of supporting ligand modifications, solvent polarity, overall charge of complex, metal identity and counterion effects. Overall, neutral low coordinate complexes (e.g. bipyridine) are calculated to have lower activation barriers than the cationic complexes. For both neutral and cationic complexes, the methane C−H activation proceed via a σ-bond metathesis rather than an oxidative addition/reductive elimination pathway. Neutralizing the cationic catalyst models by a counterion, BF4-, has a considerable impact on reducing the methane activation barrier free energy. Third, theoretical studies were performed to explore the effects of appended s-block metal ion crown ethers upon the redox properties of nitridomanganese(V) salen complexes, [(salen)MnV(N)(Mn+-crown ether)]n+, where, M = Na+, K+, Ba2+, Sr2+ for 1Na, 1K, 1Ba, 1Sr complexes respectively; A = complex without Mn+-crown ether and B = without Mn+). The results of the calculations reveal that ΔGrxn(e ̶ ) and thus reduction potentials are quite sensitive to the point charge (q) of the s-block metal ions. Methane activation by A, 1K and 1Ba complexes proceeds via a hydrogen atom abstraction (HAA) pathway with reasonable barriers for all complexes with ~ 4 kcal/mol difference in energy, more favorable free energy barrier for the complexes with higher point charge of metal ion. Changes in predicted properties as a function of continuum solvent dielectric constant suggest that the primary effect of the appended s-block ion is via "through space" interactions. Finally, a comprehensive DFT study of the electrocatalytic oxidation of ammonia to dinitrogen by a ruthenium polypyridyl complex, [(tpy)(bpy)RuII(NH3)]2+ (complex a), and its NMe2-substituted derivative (b), is presented. The thermodynamics and kinetics of electron (ET) and proton transfer (PT) steps and transition states are calculated. NMe2 substitution on bpy reduces the ET steps on average 8 kcal/mol for complex b as compared to a. The calculations indicate that N–N formation occurs by ammonia nucleophilic attack/H-transfer via a nitrene intermediate, rather than a nitride intermediate. Comparison of the free energy profiles of Ru-b with its first-row Fe congener reveals that the thermodynamics are less favorable for the Fe-b model, especially for ET steps. The N-H bond dissociation free energies (BDFEs) for NH3 to form N2 show the following trend: Ru-b <Ru-a <Fe-b, indicating the lowest and most favorable BDFEs for Ru-b complex.

<p>In this thesis, we present novel computational methods and frameworks to address the challenges associated with the determination of free energy profiles for condensed-phase chemical reactions using combined quantum mechanical and molecular mechanical (QM/MM) approaches. We focus on overcoming issues related to force matching, molecular polarizability, and convergence of free energy profiles. First, we introduce a method called Reaction Path-Force Matching in Collective Variables (RP-FM-CV) that efficiently carries out ab initio QM/MM free energy simulations through mean force fitting. This method provides accurate and robust simulations of solution-phase chemical reactions by significantly reducing deviations on the collective variables forces, thereby bringing simulated free energy profiles closer to experimental and benchmark AI/MM results. Second, we explore the role of pairwise repulsive correcting potentials in generating converged free energy profiles for chemical reactions using QM/MM simulations. We develop a free energy correcting model that sheds light on the behavior of repulsive pairwise potentials with large force deviations in collective variables. Our findings contribute to a deeper understanding of force matching models, paving the way for more accurate predictions of free energy profiles in chemical reactions. Next, we address the underpolarization problem in semiempirical (SE) molecular orbital methods by introducing a hybrid framework called doubly polarized QM/MM (dp-QM/MM). This framework improves the response property of SE/MM methods through high-level molecular polarizability fitting using machine learning (ML)-derived corrective polarizabilities, referred to as chaperone polarizabilities. We demonstrate the effectiveness of the dp-QM/MM method in simulating the Menshutkin reaction in water, showing that ML chaperones significantly reduce the error in solute molecular polarizability, bringing simulated free energy profiles closer to experimental results. In summary, this thesis presents a series of novel methods and frameworks that improve the accuracy and reliability of free energy profile estimations in condensed-phase chemical reactions using QM/MM simulations. By addressing the challenges of force matching, molecular polarizability, and convergence, these advancements have the potential to impact various fields, including computational chemistry, materials science, and drug design.</p>

<p>Drug discovery and development has experienced a tremendous growth in the recent</p> <p>years, and methods to accelerate the process are necessary as the demand for effective drugs</p> <p>to treat a wide range of diseases continue to increase. Nevertheless, the majority of conventional</p> <p>techniques are labor-intensive or have relatively low yields. As a result, academia</p> <p>and the pharmaceutical industry are continuously seeking for rapid and efficient methods to</p> <p>accelerate the drug discovery pipeline. Therefore, in order to expedite the drug discovery</p> <p>process, recent developments in physical and artificial intelligence models have been utilized</p> <p>extensively. However, the overarching problem is how to use these cutting-edge advancements</p> <p>in artificial intelligence to enhance drug discovery? Therefore, this dissertation work</p> <p>focused on developing and applying artificial intelligence and physical models to accelerate</p> <p>the drug discovery pipeline at different stages. As the first study reported in the dissertation,</p> <p>the potential to apply graph neural network-based machine learning architectures</p> <p>with the assistance of molecular modeling features to identify plausible drug leads out of</p> <p>structurally similar chemical databases is assessed. Then, the capability of applying molecular</p> <p>modeling methods including molecular docking and molecular dynamics simulations to</p> <p>identify prospective targets and biological pathways for small molecular drugs is discussed</p> <p>and evaluated in the following chapter. Further, the capability of applying state-of-the-art</p> <p>deep learning architectures such as multi-layer perceptron and recurrent neural networks</p> <p>to optimize the formulation development stage has been assessed. Moreover, this dissertation</p> <p>has contributed to assist functionality identification of unknown compounds using</p> <p>simple machine learning based computational frameworks. The developed omics data analysis</p> <p>pipeline is then discussed in order to comprehend the effects of a particular treatment</p> <p>on the proteome and lipidome levels of cells. In conclusion, the potential for developing and</p> <p>utilizing various artificial intelligence-based approaches to accelerate the drug discovery and</p> <p>development process is explored in this research. Thus, these collaborative studies intend</p> <p>to contribute to ongoing acceleration efforts and advancements in the drug discovery and</p> <p>development field.</p>

Optics are a powerful probe of chemical structure that can often be linked to theoretical predictions, providing robustness as a measurement tool. Not only do optical interactions like second harmonic generation (SHG), single and two-photon excited fluorescence (TPEF), and infrared absorption provide chemical specificity at the molecular and macromolecular scale, but the ability to image enables mapping heterogeneous behavior across complex systems such as biological tissue. This thesis will discuss nonlinear and linear optics, leveraging theoretical predictions to provide frameworks for interpreting analytical measurement. In turn, the causal mechanistic understanding provided by these frameworks will enable structurally specific quantitative tools with a special emphasis on application in biological imaging. The thesis will begin with an introduction to 2nd order nonlinear optics and the polarization analysis thereof, covering both the Jones framework for polarization analysis and the design of experiment. Novel experimental architectures aimed at reducing 1/f noise in polarization analysis will be discussed, leveraging both rapid modulation in time through electro-optic modulators (Chapter 2), as well as fixed-optic spatial modulation approaches (Chapter 3). In addition, challenges in polarization-dependent imaging within turbid systems will be addressed with the discussion of a theoretical framework to model SHG occurring from unpolarized light (Chapter 4). The application of this framework to thick tissue imaging for analysis of collagen local structure can provide a method for characterizing changes in tissue morphology associated with some common cancers (Chapter 5). In addition to discussion of nonlinear optical phenomena, a novel mechanism for electric dipole allowed fluorescence-detected circular dichroism will be introduced (Chapter 6). Tackling challenges associated with label-free chemically specific imaging, the construction of a novel infrared hyperspectral microscope for chemical classification in complex mixtures will be presented (Chapter 7). The thesis will conclude with a discussion of the inherent disadvantages in taking the traditional paradigm of modeling and measuring chemistry separately and provide the multi-agent consensus equilibrium (MACE) framework as an alternative to the classic meet-in-the-middle approach (Chapter 8). Spanning topics from pure theoretical descriptions of light-matter interaction to full experimental work, this thesis aims to unify these two fronts. <br>

The question posed in this work is how one would model and predict the rotational spectrum of open-shell open-shell van der Waals complexes. There are two secondary questions that arise: the nature of radical-radical interactions in such systems and the modelling of the large amplitude motion of the constituent molecules. Four different systems were studied in this work, each providing part of the answer to the main question. Starting with the large amplitude motion, there are two theoretical approaches that may be adopted: to either model the whole complex as a semi-rigid molecule, or to perform quantum dynamical calculations. We recorded and analysed the rotational spectrum (using Fourier transform microwave spectroscopy) of the molecule of tertiary butyl acetate (TBAc) which exhibits a high degree of internal rotation; and of the weakly-bound complex between a neon atom and a nitrogen dioxide molecule (Ne-NO2). We used the semi-rigid approach for TBAc and the quantum dynamical approach for Ne-NO2. We also explored the compatibility of these two approaches. Moreover, we were able to predict and analyse the fine and hyperfine structure of the Ne-NO2 spectrum using spherical tensor operator algebra and the results of our dynamics calculations. To explore the nature of the interactions in an radical-radical van der Waals complex we calculated the PESs of the possible states that the complex may be formed in, when an oxygen and a nitrogen monoxide molecule meet on a plane using a number of high level ab initio methods. Finally, our conclusions were tested and applied when we performed the angular quantum dynamics to predict the rotational spectrum of the complex between an oxygen and a nitrogen dioxide molecule, and account for the effect of nuclear spin statistics in that system.

A major problem in the quantitative analysis of AC voltammetric data has been the variance in results between laboratories, often resulting from a reliance on "heuristic" methods of parameter estimation that are strongly dependent on the choices of the operator. In this thesis, an automatic method for parameter estimation will be tested in the context of experiments involving electron-transfer processes in solution-phase. It will be shown that this automatic method produces parameter estimates consistent with those from other methods and the literature in the case of the ferri-/ferrocyanide couple, and is able to explain inconsistency in published values of the rate parameter for the ferrocene/ferrocenium couple. When a coupled homogeneous reaction is considered in a theoretical study, parameter recovery is achieved with a higher degree of accuracy when simulated data resulting from a high frequency AC voltammetry waveform are used. When surface-confined reactions are considered, heterogeneity in the rate constant and formal potential make parameter estimation more challenging. In the final study, a method for incorporating these "dispersion" effects into voltammetric simulations is presented, and for the first time, a quantitive theoretical study of the impact of dispersion on measured current is undertaken.

Spin dynamics simulations are used to gain insight into important magnetic resonance experiments in the fields of chemistry, biochemistry, and physics. Presented in this thesis are investigations into how to accelerate these simulations by making them more efficient. Chapter 1 gives a brief introduction to the methods of spin dynamics simulation used in the rest of the thesis. The `exponential scaling problem' that formally limits the size of spin system that can be simulated is described. Chapter 2 provides a summary of methods that have been developed to overcome the exponential scaling problem in liquid state magnetic resonance. The possibility of utilizing the multiple processors prevalent in modern computers to accelerate spin dynamics simulations provides the impetus for the investigation found in Chapter 3. A number of different methods of parallelization leading to acceleration of spin dynamics simulations are derived and discussed. It is often the case that the parameters defining a spin system are time-dependent. This complicates the simulation of the spin dynamics of the system. Chapter 4 presents a method of simplifying such simulations by mapping the spin dynamics into a larger state space. This method is applied to simulations incorporating mechanical spinning of the sample with powder averaging. In Chapter 5, implementations of several magnetic resonance experiments are detailed. In so doing, use of techniques developed in Chapters 2 and 3 are exemplified. Further, specific details of these experiments are utilized to increase the efficiency of their simulation.

The past two decades have witnessed tremendous advances in the field of high accuracy, high mass resolution data acquisition of complex samples such as crude oils and the human proteome. With the development of Fourier transform ion cyclotron resonance mass spectrometry, the rapidly growing field of petroleomics has emerged, whose goal is to process and analyse the large volumes of complex and often poorly understood data on crude oils generated by mass spectrometry. As global oil resources deplete, oil companies are increasingly moving towards the extraction and refining of the still plentiful reserves of heavy, carbon rich and highly contaminated crude oil. It is essential that the oil industry gather the maximum possible amount of information about the crude oil prior to setting up the drilling infrastructure, in order to reduce processing costs. This project describes how machine learning can be used as a novel way to extract critical information from complex mass spectra which will aid in the processing of crude oils. The thesis discusses the experimental methods involved in acquiring high accuracy mass spectral data for a large and key industry-standard set of crude oil samples. These data are subsequently analysed to identify possible links between the raw mass spectra and certain physical properties of the oils, such as pour point and sulphur content. Methods including artificial neural networks and self organising maps are described and the use of spectral clustering and pattern recognition to classify crude oils is investigated. The main focus of the research, the creation of an original simulated annealing genetic algorithm hybrid technique (SAGA), is discussed in detail and the successes of modelling a number of different datasets using all described methods are outlined. Despite the complexity of the underlying mass spectrometry data, which reflects the considerable chemical diversity of the samples themselves, the results show that physical properties can be modelled with varying degrees of success. When modelling pour point temperatures, the artificial neural network achieved an average prediction error of less than 10% while SAGA predicted the same values with an average accuracy of more than 85%. It did not prove possible to model any of the other properties with such statistical significance; however improvements to feature extraction and pre-processing of the spectral data as well as enhancement of the modelling techniques should yield more consistent and statistically reliable results. These should in due course lead to a comprehensive model which the oil industry can use to process crude oil data using rapid and cost effective analytical methods.