Metal organic frameworks (MOFs) are a class of nanoporous materials composed through self-assembly of inorganic and organic structural building units (SBUs). MOFs show great promise for many applications due to their record-breaking internal surface areas and tunable pore chemistry. This thesis work focuses on gas separation applications of MOFs in the context of carbon capture and storage (CCS) technologies. CCS technologies are expected to play a key role in the mitigation of anthropogenic CO2 emissions in the near future. In the first part of the thesis, robust machine learning quantitative structure-property relationship (QSPR) models are developed to predict CO2 working capacity and CO2/H2 selectivity for pre-combustion carbon capture using the most topologically diverse database of hypothetical MOF structures constructed to date (358,400 MOFs, 1166 network topologies). The support vector regression (SVR) models are developed on a training set of 35,840 MOFs (10% of the database) and validated on the remaining 322,560 MOFs. The most accurate models for CO2 working capacities (R2 = 0.944) and CO2/H2 selectivities (R2 = 0.876) are built from a combination of six geometric descriptors and three novel y-range normalized atomic-property-weighted radial distribution function (AP-RDF) descriptors. 309 common MOFs are identified between the grand canonical Monte Carlo (GCMC) calculated and SVR-predicted top-1000 high-performing MOFs ranked according to a normalized adsorbent performance score. This work shows that SVR models can indeed account for the topological diversity exhibited by MOFs. In the second project of this thesis, computational simulations are performed on a MOF, CALF-20, to examine its chemical and physical properties which are linked to its exceptional water-resisting ability. We predict the atomic positions in the crystal structure of the bulk phase of CALF-20, for which only a powder X-ray diffraction pattern is available, from a single crystal X-ray diffraction pattern of a metastable phase of CALF-20. Using the predicted CALF-20 structure, we simulate adsorption isotherms of CO2 and N2 under dry and humid conditions which are in excellent agreement with experiment. Snapshots of the CALF-20 undergoing water sorption simulations reveal that water molecules in a given pore adsorb and desorb together due to hydrogen bonding. Binding sites and binding energies of CO2 and water in CALF-20 show that the preferential CO2 uptake at low relative humidities is driven by the stronger binding energy of CO2 in the MOF, and the sharp increase in water uptake at higher relative humidities is driven by the strong intermolecular interactions between water. In the third project of this thesis, we use computational simulations to investigate the effects of residual solvent on Ni-BPM’s CH4 and N2 adsorption properties. Single crystal X-ray diffraction data shows that there are two sets of positions (Set 1 and 2) that can be occupied by the 10 residual DMSO molecules in the Ni-BPM framework. GCMC simulations of CH4 and N2 uptake in Ni-BPM reveal that CH4 uptake is in closest agreement with experiment when the 10 DMSO’s are placed among the two sets of positions in equal ratio (Mixed Set). Severe under-prediction and over-prediction of CH4 uptake are observed when the DMSO’s are placed in Set1 and Set 2 positions, respectively. Through binding site analysis, the CH4 binding sites within the Ni-BPM framework are found to overlap with the Set 1 DMSO positions but not with the Set 2 DMSO positions which explains the deviations in CH4 uptake observed for these cases. Binding energy calculations reveal that CH4 molecules are most stabilized when the DMSO’s are in the Mixed Set of positions.

Nitrilotriacetic Acid (NTA) is an organic ligand which has been extensively studied due to its biological significance and excellent chelating properties. Nitrilotripropionic Acid (NTPA) is a ligand that is believed to possess similar properties to NTA, but has not been as extensively studied. It has been experimentally determined that metal complexes of NTA are orders of magnitude stronger than those formed with NTPA. This is surprising, especially considering that the ligands do not differ that much from each other. NTPA contains an additional –CH2– group in each of the acid containing arms as compared to NTA. The aim of these studies were to explain, theoretically, why this is the case. Analyses were conducted with a number of software programs including, Gaussian 03, Schrödinger Maestro and AIM 2000. All Density Functional Theory (DFT) studies were conducted in solvent at the RB3LYP/6-311+G(d,p) level of theory in conjunction with a number of different solvation models. En route to explaining why the complexes differ in stability a new methodology was utilized (isodesmic reactions) in which the four stepwise protonation constants of both NTA and NTPA were successfully predicted; in fact these were the most accurate values predicted to date by DFT methods. The final step of these studies focused on predicting stability constants of metal (Zn2+ and Ni2+) complexes of NTA and NTPA. These predictions were not as accurate as those achieved for the prediction of protonation constants; however, success was achieved in predicting the trend – complexes with NTA are orders of magnitude stronger than complexes formed with NTPA. The most important observation revealed that H–clashes and C–H∙∙∙O hydrogen bonds present in M(NTPA) complexes, which are not present in M(NTA) complexes, result in the formation of additional rings which contributes to the formation of a cage. It was discovered that the H-clashes present in the M(NTPA) complexes were contributing to the overall stability of the molecule. This is completely contradictory to a previous explanation in which H-clashes, being a result of steric crowding, resulted in destabilization of a complex. If the H-clashes were not present in the M(NTPA) complexes there would not be enough stabilizing factors present in the molecule which will inevitably result in the non-existence of M(NTPA) complexes. Copyright / Dissertation (MSc)--University of Pretoria, 2010. / Chemistry / unrestricted

The Effective Fragment Potential (EFP) is a quantum-mechanical based model used to describe non-covalent interactions of small molecules or fragments. It can be used along with fully <i>ab initio</i> methods to study the electronic properties of complex systems, such as solvated chromophores or proteins. For this purpose, the system is divided into two regions: one modeled with quantum mechanics and the other with EFP. The interaction between the QM region and the effective fragments has popularly been described through electrostatics and polarization only. This thesis focuses on the development of the QM/EFP exchange-repulsion term, as well as the evaluation of the dispersion term and a charge-penetration correction. The goal of is to determine how these terms can increase the accuracy of QM/EFP calculations without an increase in their computational cost.

Computational chemistry has led to the greater understanding of the molecular world, from the interaction of molecules, to the composition of molecular species and materials. Of the families of computational chemistry approaches available, the main families of electronic structure methods that are capable of accurate and/or reliable predictions of energetic, structural, and spectroscopic properties are ab initio methods and density functional theory (DFT). The focus of this dissertation is to improve the accuracy of predictions and computational efficiency (with respect to memory, disk space, and computer processing time) of some computational chemistry methods, which, in turn, can extend the size of molecule that can be addressed, and, for other methods, DFT, in particular, gain greater insight into which DFT methods are more reliable than others. Much, though not all, of the focus of this dissertation is upon transition metal species – species for which much less method development has been targeted or insight about method performance has been well established. The ab initio approach that has been targeted in this work is the correlation consistent composite approach (ccCA), which has proven to be a robust, ab initio computational method for main group and first row transition metal-containing molecules yielding, on average, accurate thermodynamic properties, i.e., within 1 kcal/mol of experiment for main group species and within 3 kcal/mol of experiment for first row transition metal molecules. In order to make ccCA applicable to systems containing any element from the periodic table, development of the method for second row transition metals and heavier elements, including lower p-block (5p and 6p) elements was pursued. The resulting method, the relativistic pseudopotential variant of ccCA (rp-ccCA), and its application are detailed for second row transition metals and lower p-block elements. Because of the computational cost of ab initio methods, DFT is a popular choice for the study of transition metals. Despite this, the most reliable density functionals for the prediction of energetic properties (e.g. enthalpy of formation, ionization potential, electron affinity, dissociation energy) of transition metal species, have not been clearly identified. The examination of DFT performance for first and second row transition metal thermochemistry (i.e., enthalpies of formation) was conducted and density functionals for the study of these species were identified. And, finally, to address the accuracy of spectroscopic and energetic properties, improvements for a series of density functionals have been established. In both DFT and ab initio methods, the harmonic approximation is typically employed. This neglect of anharmonic effects, such as those related to vibrational properties (e.g. zero-point vibrational energies, thermal contributions to enthalpy and entropy) of molecules, generally results in computational predictions that are not in agreement with experiment. To correct for the neglect of anharmonicity, scale factors can be applied to these vibrational properties, resulting in better alignment with experimental observations. Scale factors for DFT in conjunction with both the correlation and polarization consistent basis sets have been developed in this work.

A fundamental understanding of ultrafast nonequilibrium dynamical processes in molecular aggregates is crucially important for the design of nanodevices that utilize quantum mechanical effects. However, understanding the coupled electron-phonon dynamics of such high-dimensional systems remains a challenging issue. As a result of the ever-growing computational power that is available, realistic parameterization of model Hamiltonians and implementation of sophisticated quantum dynamics algorithms have become indispensable tools for gaining insight into these processes. The focus of this dissertation is the development and implementation of approximate path integral-based methods to compute the time-evolution as well as linear and nonlinear spectroscopic signals of molecular aggregates following photo-excitation. The developments and applications presented here are geared toward gaining a better understanding of the role that electron-phonon coupling plays in framing ultrafast excitation energy transfer networks in photosynthetic light-harvesting complexes. The ultrafast excitation energy transfer dynamics that occurs upon photo-excitation of a network of electronically coupled chromophores is remarkably sensitive to the strength of electronic coupling as well as the frequencies and coupling strengths that characterize electron-phonon interactions. Based on approximations to the diabatic representation of molecular Hamiltonians, energetic models of condensed phase molecular aggregates can be parameterized from a first principles description. Often times, computational parameterization of these models reveals comparable magnitudes for intermolecular electronic couplings and electron-phonon couplings, negating the applicability of popular perturbative algorithms (such as those based on Forster or Redfield theory) for describing their time-evolution. Moreover, non-perturbative exact methods (e.g. stochastic Schrodinger equations and the Hierarchical Equations of Motion) are generally inefficient for all but a few specific limiting forms of electron-phonon coupling, or make assumptions about autocorrelation timescales of the vibrational environment. Because of the failure of the energetic parameters determined through recent ab initio studies of natural molecular aggregates to abide by the rather restrictive requirements for efficient application of the above-mentioned methods, the development of approximate non-perturbative algorithms for predicting nonequilibrium dynamical properties of such systems is a central theme in this dissertation. Following a general introductory section describing the basic concepts that are fundamental to the remainder of the thesis, the derivation of path integral dynamics methods is presented. These include a cartesian phase space path integral derivation of the truncated Wigner approximation as applied to the Meyer-Miller-Stock-Thoss mapping model for describing vibronic systems as well as a novel derivation of the Partially Linearized Density Matrix algorithm, highlighting its emergence as a leading order approximation to an, in principle, exact expression for the density matrix. An algorithm for computing the nonlinear response function for higher-order optical spectroscopy signals is presented within the framework of the partially linearized density matrix formalism. Time-resolved two-dimensional electronic spectra are computed and compared with exact results as well as standard perturbation theory-based results, highlighting the accuracy and efficiency of the developed method. Additionally, the recently popularized symmetrical quasi-classical method for computing the reduced density matrix dynamics is extended for computing linear optical spectroscopy signals, and compared with results from the partially linearized density matrix treatment. A generalization of the model Hamiltonian form utilized in recent ab initio studies is presented, allowing for direct vibrational energy relaxation due to coupling between intramolecular normal modes and their environment. The consequences of including these interactions within a model Hamiltonian that is inspired by energetic parameters found in studies of a photosynthetic light-harvesting complex are highlighted in the context of density matrix dynamics and time-resolved two-dimensional electronic spectroscopy. The results indicate that this physical process can be utilized as a means of optimizing the efficiency of excitation energy transfer and localization. Inspired by ab initio characterization of model Hamiltonians for molecular aggregates, a new approximate semiclassical propagator for describing the time-evolution of a system consisting of discrete electronic states in the presence of both high-frequency harmonic vibrational modes as well as slow environmental DOFs with arbitrary potentials is presented. Results indicate that this algorithm provides a more accurate description in this parameter regime than standard linearized path integral methods such as the partially linearized density matrix algorithm and the truncated Wigner approximation. Finally, preliminary results of dynamics involving non-perturbative field-matter interactions is presented with emphasis on strategically shaped pulses, field design through optimal control, and non-perturbative pump-probe spectroscopy.

No description available.

<p>Here we describe recent technical implementations and modifications to the libefp package as well as applications of those implementations. Applications of the EFP method to biologically relevant systems are provided on a benchmark EFP-SAPT-CCSD study on the SSI dataset along with suggested basis set recommendations and a study on the pairwise EFP total energy decomposition on Factor Xa. We also report the technical overview of two computational tools we believe will lower the human barrier to utilizing the EFP method - iSpiEFP and EFPdB.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / Molecular recognition plays an important role in biological systems. The purpose of this study was to get a better understanding of the process by incorporating computational tools.Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) method and Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PBSA) method, the end-point free energy calculations provide the binding free energy the can be used to rank-order protein–ligand structures in virtual screening for compound or target identification. Free energy calculations were performed on a diverse set of 11 proteins bound to 14 small molecules was carried out for. A direct comparison was taken between the calculated free energy and the experimental isothermal titration calorimetry (ITC) data. Four and three systems in MM-GBSA and MM-PBSA calculations, respectively, reproduced the ITC free energy within 1 kcal•mol–1. MM-GBSA exhibited better rank-ordering with a Spearman ρ of 0.68 compared to 0.40 for MM-PBSA with dielectric constant (ε = 1). The rank-ordering performance of MM-PBSA improved with increasing ε (ρ = 0.91 for ε = 10), but the contributions of electrostatics became significantly lower at larger ε level, suggesting that the only nonpolar and entropy components contribute to the improved results. Our previously developed scoring function, Support Vector Regression Knowledge-Based (SVRKB), resulted in excellent rank-ordering (ρ = 0.81) when applied into MD simulations. Filtering MD snapshots by prescoring protein–ligand complexes with a machine learning-based approach (SVMSP) resulted in a significant improvement in the MM-PBSA results (ε = 1) from ρ = 0.40 to ρ = 0.81. Finally, the nonpolar components in the free energy calculations showed strong correlation to the ITC free energy while the electrostatic components did not; the computed entropies did not correlate with the ITC entropy. Explicit-solvent molecular dynamics (MD) simulations offer an opportunity to sample multiple conformational states of a protein-ligand system in molecular recognition. SVMSP is a target-specific rescoring method that combines machine learning with statistical potentials. We evaluate the performance of SVMSP in its ability to enrich chemical libraries docked to MD structures. Seven proteins from the Directory of Useful Decoys (DUD) were involved in the study. We followed an innovative approach by training SVMSP scoring models using MD structures (SVMSPMD). The resulting models remarkably improved enrichment in two cases. We also explored approaches for a prior identification of MD snapshots with high enrichment power from an MD simulation in the absence of active compounds. SVMSP rescoring of protein–compound MD structures was applied for the search of small-molecule inhibitors of the mitochondrial enzyme aldehyde dehydrogenase 2 (ALDH2). Rank-ordering of a commercial library of 50,000 compounds docked to MD optimized structures of ALDH2 led to five small-molecule inhibitors. Four compounds had IC50s below 5 μM. These compounds serve as leads for the design and synthesis of more potent and selective ALDH2 inhibitors.

No description available.

People are exposed to low-level ionizing radiation from both natural sources and manmade sources throughout their lives. Because radiation can cause cancers and genetic defects, it is very important to know the mechanism involved in the radiation process. As the damage mainly occurs on the DNA, DNA base derivatives crystalline complexes, which are similar to the base pairs of the DNA double helix, are used in the study. Theoretical calculations are performed on the co-crystals 1-Methylthymine:9-Methyladenine and 1-Methyluracil:9-Ethyladenine and on the radicals observed in the experiment.The calculated aisotropic hyperfine constants and isotropic fermi contact couplings show good agreement with the experimental results.