Protein-small molecule interactions play a central role in various aspects of the structural and functional organization of the cell and are therefore integral for drug discovery. The most comprehensive structural characterization of small molecule binding sites is provided by X-ray crystallography. However, it is often time-consuming and challenging to perform direct experimental analysis. Therefore, it is necessary to have computational methods that can predict binding site locations on unbound structures with accuracy close to that provided by X-ray crystallography. This thesis details four projects which involve the development of a fragment benchmark set, evaluation of allosteric sites in G Protein-Coupled Receptors (GPCRs), computational modeling of binding pocket dynamics, and the development of an Application Program Interface (API) framework for High-Performance Computing (HPC) centers. The first project provides a benchmark set for testing hot spot identification methods, emphasizing application to fragment-based drug discovery. Using the solvent mapping server, FTMap, which finds small molecule binding hot spots on proteins, we compared our benchmark set to an existing benchmark set that with a different method of construction. The second project details the effort to identify allosteric binding sites on GPCRs. We demonstrate that FTMap successfully identifies structurally determined allosteric sites in bound crystal structures and unbound structures. The project was further expanded to evaluate the conservation of allosteric sites across different classes, families, and types of GPCRs. The third project provides a structure-based analysis of cryptic site openings. Cryptic sites are pockets formed in ligand-bound proteins but not observed in unbound protein structures. Through analysis of crystal structures supplemented by molecular dynamics (MD) with enhanced sampling techniques, it was shown that cryptic sites can be grouped into three types: 1) “genuine” cryptic sites, which do not form without ligand binding, 2) spontaneously forming cryptic sites, and 3) cryptic sites impacted by mutations or off-site ligand binding. The fourth project presents an API framework for increasing the accessibility of HPC resources.

Liquid water is the most significant substance on earth. Its anomalous properties, stemming from its unique hydrogen bond (HB) network, contribute to its fundamental importance across a broad range of fields, including biochemistry, meteorology, and ecology. The HB network is governed by the interplay between covalent bonds, HBs, and van der Waals interactions, sensitive to even a slight alternation of these interaction strengths. Various scattering experiments and spectroscopy techniques have been developed to probe the effect of changes in HB network on the ensemble-averaged value of these properties. As complementary, ab initio Molecular dynamics (AIMD), combined with the machine learning techniques, can provide the information on atomic scale and help us identify the contributions from the water molecules in different local structures to these properties. This dissertation focuses on investigating the relationship between spectra probing the unoccupied states and local structures in NaCl solutions, as well as the connection between density and local structures in liquid water. The first part employs the GW-Bethe-Salpeter-Equation (GW-BSE) approach to reproduce theoretical XAS spectra of NaCl solutions and compare them with those of pure water. The introduction of ions disrupts the HB network, leading to the localization of excitons which causes the observable changes in the spectra. The second part delves into the investigation of the density anomaly of liquid water at atmospheric pressure. Three different molecular dynamics trajectories at each temperature from 290K to 390K with 10K interval are simulated using distinct machine-learning potential models. These models are trained on input data from density functional theory calculations based on different approximate exchange-correlation functionals, illustrating the impact of varying local structures on the density. Subsequently, Voronoi Polyhedra analysis is employed to establish a quantitative connection between the changes in density and the alternations of local structures in liquid water at different temperatures. / Physics

<p>In this thesis we present the idea of using parallel phrases for word alignment. Each parallel phrase is extracted from a set of manual word alignments and contains a number of source and target words and their corresponding alignments. If a parallel phrase matches a new sentence pair, its word alignments can be applied to the new sentence. There are several advantages of using phrases for word alignment. First, longer text segments include more  context and will be more likely to produce correct word alignments than shorter segments or single words. More importantly, the use of longer phrases makesit possible to generalize words in the phrase by replacing words by parts-of-speech or other grammatical information. In this way, the number of words covered by the extracted phrases can go beyond the words and phrases that were present in the original set of manually aligned sentences. We present  experiments with phrase-based word alignment on three types of English–Swedish parallel corpora: a software manual, a novel and proceedings of the European Parliament. In order to find a balance between improved coverage and high alignment accuracy we investigated different properties of generalised phrases to identify which types of phrases are likely to produce accurate alignments on new data. Finally, we have compared phrase-based word alignments to state-of-the-art statistical alignment with encouraging results. We show that phrase-based word alignments can be used to enhance statistical word alignment. To evaluate word alignments an English–Swedish reference set for the Europarl corpus was constructed. The guidelines for producing this reference alignment are presented in the thesis.</p>

Capturing the effects of detailed-chemistry on turbulent combustion processes is a central challenge faced by the numerical combustion community. However, the inherent complexity and non-linear nature of both turbulence and chemistry require that combustion models rely heavily on engineering approximations to remain computationally tractable. This thesis proposes a computationally efficient algorithm for modelling detailed-chemistry effects in turbulent diffusion flames and numerically predicting the associated flame properties. The cornerstone of this combustion modelling tool is the use of parallel Adaptive Mesh Refinement (AMR) scheme with the recently proposed Flame Prolongation of Intrinsic low-dimensional manifold (FPI) tabulated-chemistry approach for modelling complex chemistry. The effect of turbulence on the mean chemistry is incorporated using a Presumed Conditional Moment (PCM) approach based on a beta-probability density function (PDF). The two-equation k-w turbulence model is used for modelling the effects of the unresolved turbulence on the mean flow field. The finite-rate of methane-air combustion is represented here by using the GRI-Mech 3.0 scheme. This detailed mechanism is used to build the FPI tables. A state of the art numerical scheme based on a parallel block-based solution-adaptive algorithm has been developed to solve the Favre-averaged Navier-Stokes (FANS) and other governing partial-differential equations using a second-order accurate, fully-coupled finite-volume formulation on body-fitted, multi-block, quadrilateral/hexahedral mesh for two-dimensional and three-dimensional flow geometries, respectively. A standard fourth-order Runge-Kutta time-marching scheme is used for time-accurate temporal discretizations. Numerical predictions of three different diffusion flames configurations are considered in the present work: a laminar counter-flow flame; a laminar co-flow diffusion flame; and a Sydney bluff-body turbulent reacting flow. Comparisons are made between the predicted results of the present FPI scheme and Steady Laminar Flamelet Model (SLFM) approach for diffusion flames. The effects of grid resolution on the predicted overall flame solutions are also assessed. Other non-reacting flows have also been considered to further validate other aspects of the numerical scheme. The present schemes predict results which are in good agreement with published experimental results and reduces the computational cost involved in modelling turbulent diffusion flames significantly, both in terms of storage and processing time.

Capturing the effects of detailed-chemistry on turbulent combustion processes is a central challenge faced by the numerical combustion community. However, the inherent complexity and non-linear nature of both turbulence and chemistry require that combustion models rely heavily on engineering approximations to remain computationally tractable. This thesis proposes a computationally efficient algorithm for modelling detailed-chemistry effects in turbulent diffusion flames and numerically predicting the associated flame properties. The cornerstone of this combustion modelling tool is the use of parallel Adaptive Mesh Refinement (AMR) scheme with the recently proposed Flame Prolongation of Intrinsic low-dimensional manifold (FPI) tabulated-chemistry approach for modelling complex chemistry. The effect of turbulence on the mean chemistry is incorporated using a Presumed Conditional Moment (PCM) approach based on a beta-probability density function (PDF). The two-equation k-w turbulence model is used for modelling the effects of the unresolved turbulence on the mean flow field. The finite-rate of methane-air combustion is represented here by using the GRI-Mech 3.0 scheme. This detailed mechanism is used to build the FPI tables. A state of the art numerical scheme based on a parallel block-based solution-adaptive algorithm has been developed to solve the Favre-averaged Navier-Stokes (FANS) and other governing partial-differential equations using a second-order accurate, fully-coupled finite-volume formulation on body-fitted, multi-block, quadrilateral/hexahedral mesh for two-dimensional and three-dimensional flow geometries, respectively. A standard fourth-order Runge-Kutta time-marching scheme is used for time-accurate temporal discretizations. Numerical predictions of three different diffusion flames configurations are considered in the present work: a laminar counter-flow flame; a laminar co-flow diffusion flame; and a Sydney bluff-body turbulent reacting flow. Comparisons are made between the predicted results of the present FPI scheme and Steady Laminar Flamelet Model (SLFM) approach for diffusion flames. The effects of grid resolution on the predicted overall flame solutions are also assessed. Other non-reacting flows have also been considered to further validate other aspects of the numerical scheme. The present schemes predict results which are in good agreement with published experimental results and reduces the computational cost involved in modelling turbulent diffusion flames significantly, both in terms of storage and processing time.

This research examines how the contents and structure of a story may be enriched by computational means. A review of pertinent semantic theory and previous work on the structural analysis of folktales is presented. Merits and limitations of several content-generation systems are discussed. The research develops three mechanisms - elaboration, interpolation, and continuity fixes - to enhance story content, address issues of rigid structure, and fix problems with the logical progression of a story. Elaboration works by adding or modifying information contained within a story to provide detailed descriptions of an event. Interpolation works by adding detail between high-level story elements dictated by a story grammar. Both methods search for appropriate semantic functions contained in a lexicon. Rules are developed to ensure that the selection of functions is consistent with the context of the story. Control strategies for both mechanisms are proposed that restrict the quantity and content of candidate functions. Finally, a method of checking and correcting inconsistencies in story continuity is proposed. Continuity checks are performed using semantic threads that connect an object or character to a sequence of events. Unexplained changes in state or location are fixed with interpolation. The mechanisms are demonstrated with simple examples drawn from folktales, and the effectiveness of each is discussed. While the thesis focuses on folktales, it forms the basis for further work on the generation of more complex stories in the greater realm of fiction. / Thesis (Ph.D, Computing) -- Queen's University, 2010-10-12 11:24:33.536

Quantum revolution has a great potential to impose massive impact on information technology. Point defects in solid-state materials such as NV center in diamond have been demonstrated to be promising qubit candidates. Defect levels in band gaps are analogous to molecular orbitals, serving as an excellent platform for quantum applications. Atomically thin two-dimensional materials are under the spotlight in recent years, as the sheet-like geometry brings advantages for operations of quantum defects. That includes the realization of patterned qubit fabrication, operation at room temperature, and improvement of coherence time through a highly-efficient isotope purification process. Although using point defects in 2D materials is a promising route toward quantum applications, searching for viable defects satisfying the criteria of magneto-optical properties for quantum applications is challenging. Thanks to the continued development of density functional theory, sophisticated multi-electron systems can be accurately simulated on the atomistic level to evaluate multiple ground-state properties, including total energy, magnetic polarization, and atomic orbitals. In addition to that, implementing constrained DFT renders the insight of excited-state properties. Benefited from the application of data-science tools in material science, we are now capable of performing data-driven analysis based on high-throughput computational techniques, including data mining/storage and automatic discovery workflow. Adopting the above tools and physical-principle-enabled symmetry analysis, we are able to identify a large set of quantum defects in a vast material space. We show that antisite defects in 2D transition metal dichalcogenides (TMDs) can provide a general platform for controllable solid-state spin qubit systems. Using high-throughput atomistic simulations that are enabled by a symmetry-based hypothesis, we identify several neutral antisite defects in TMDs that create defect levels deep in the bulk band gaps and host a paramagnetic triplet ground state. Our in-depth analysis reveals the presence of optical transitions and triplet-singlet intersystem crossing processes for fingerprinting these defect qubits. Finally, as an illustrative example, we discuss the initialization and readout principles of an antisite qubit in WS2, which is expected to be stable against interlayer interactions in a multilayer structure for qubit isolation and protection in future qubit-based devices. Motivated by the insight gained from the study of antisite defect qubits in TMDs, we significantly expanded the searching domain to all the binary 2D materials. As mentioned above, searching for defects with triplet ground states is one of the most crucial steps to identify more quantum defects that support multiple quantum functionalities. We design a comprehensive workflow for screening promising quantum defects based on the site-symmetry-based hypothesis. The discovery efforts reveal that the symmetry-enabled discovery workflow of quantum defects significantly increases the probability of finding triplet defects. To identify multiple functionalities for these quantum defects, including qubits and quantum emitters, the magneto-optical properties of triplet defects are comprehensively calculated. We demonstrate that 45 antisite defects in the various hosts, including post-transition metal monochalcogenides (PTMCs) and transition metal dichalcogenides (TMDs) are promising quantum defects. Most importantly, we propose that 16 antisites (both anion and cation based) in PTMCs can serve as the most promising quantum defect platform based on 2D materials, due to their well-defined defect levels, optimal magneto-optical properties, and the availability of host materials. This set of data-driven discovery efforts opens a new pathway for creating scalable, room-temperature spin qubits in 2D materials, including TMDs, PTMCs, and beyond. The comprehensive defect data created in this work, combined with experimental verification and demonstration in the future, will eventually lead to the fertilization of a 2D defect design platform that facilitates the design of point defects in 2D material families for multiple quantum functionalities, including quantum emitters, quantum sensor, transductor, and more. / Physics

Kohn-Sham density functional theory is a widely-used method to predict the ground-state total energies and densities of interacting correlated electrons in atoms, molecules, clusters, solids, and liquids. In principle, exact results for these properties can be found by solving self-consistent one-electron Schrödinger equations based upon density functionals for the energy. In practice, the density functional for the exchange-correlation contribution to the energy must be approximated for the sake of computational efficiency. More accurate but still computationally efficient approximations are being developed by the satisfaction of exact constraints. These include the SCAN (strongly constrained and appropriately normed) semi-local density functional. We used the pressure induced structural phase transition of solids to validate SCAN. To predict an accurate critical pressure, a method must account for a small energy difference between close-lying phases which have very different electronic structures. We computed the critical pressure for the structural phase transition of 25 group IV, III-V, and II-VI compounds using the local density approximation (LDA), Perdew-Burke-Ernzerhof (PBE), and SCAN. LDA systematically underestimates the critical pressures as reported in a previous study. PBE which often improves upon LDA performances yields under- or overestimated pressures in many cases. SCAN, on the other hand, predicts accurate critical pressures with an accuracy comparable to the computationally expensive methods like the quantum Monte Carlo (QMC), random phase approximation (RPA), and the hybrid functional HSE06, in the cases where pressures with these methods are reported. The impressive success of the approximate density functionals, however, comes at a price. There is an incomplete cancellation of the hartree and approximate exchange energies for one-electron densities, giving rise to a spurious interaction of an electron with itself. This is called the self-interaction error (SIE). Perdew-Zunger self-interaction correction (PZ SIC) makes an approximate density functional SIE free for all one-electron density. The transition states, which involve stretched bonds, are poorly described by the semilocal density functionals. Thus LDA, PBE, and SCAN predict too low barrier height for a chemical reaction. We tested the Perdew-Zunger self-interaction correction (PZ SIC) for the barrier heights of the representative test set BH6. We found that the barrier heights are greatly improved when we go from LDA to PBE to SCAN. We also tested the PZ SIC for the atomization energies of the molecular test set AE6. SCAN predicts very accurate atomization energies, whereas SCAN-SIC severely worsens the atomization energies. We attribute such worsening to the noded localized orbitals, over which the PZ energy is minimized. The nodality of the orbital density is a consequence of the orthogonality criterion for overlapping real orbitals, and this nodality increases when free atoms bind to form a molecule. This explains why the error in the atomization energies is reduced when the PZ energy is minimized using complex orbitals, which yield nodeless orbital densities. The complex orbitals, however, do not completely eliminate the error. The remaining error is attributed to the fact that PZ SIC loses the exactness of LDA, PBE, or SCAN for densities that vary slowly over space, calling for a generalization of the PZ theory. / Physics

Yes

The safe design and operation of Process plants requires an ability to predict hazard consequences reliably. One particular hazard is a jet fire that might arise from the ignition of an accidental release of pressurised gas or liquid. On offshore gas and oil production platforms and also on land-based gas facilities, accidental releases might occur of high pressure natural gas sometimes containing higher molecular weight components. Industries continue to seek efficient and cost-effective means of protecting their plants and personnel from the hazards of fires. Following disasters which occurred in the past, the need for effective mitigation systems has, once again, been highlighted. Mitigation systems involving agents such as halons, which are perceived to be environmentally damaging, are currently out of favour and interest has revived in the use of water sprays. The research work presented here addresses the problem of the suppression of a compartment jet fire by water sprays. This involved studying the interaction between water spray and a turbulent jet flame inside a compartment of dimension 6x2.4x2.4 m3. The fuel used for the jet fire was propane emerging from a 2.0 cm diameter vertical nozzle and at a mass flow rate of 0.1 kg/s. The objectives of the research are to investigate the mitigation of compartment jet fires by using water sprays by the application of a computational fluid dynamics (CFD) methodology incorporating iv Summary combustion and a radiation model to study the jet fire behaviour and the temperature distribution in a compartment. In order to achieve the above objectives, it is necessary to produce a workable CFD model of an offshore module. The radiative heat exchange is considered in the modelling by using the Discrete Transfer Radiation Method (DTRM). The study of the sprays requires details of the individual drops' sizes. The Malvern Particle Sizer was used to measure the drop size of water sprays from the different spray nozzles which have been investigated in this study. The obtained drop sizes of the spray nozzles investigated are used to model the spray in FLUENT, which is a well developed CFD package used in industry and university research. The research started with the CFD modelling of the compartment fire, followed by experimental work done at the university laboratory at Buxton to validate the result of the modelling. In contrast to previous studies in which the combustion reaction was treated as a simple heat source this CFD has included a model of the combustion reaction. Comparisons are made between the experimental data and the predictions of different scenarios (i. e. steady state, different water spray arrangement and time dependent). The predicted temperature distributions from FLUENT, which includes radiation and surface heat transfer, are found to be in close agreement with the experimental data. Modelling results showed that the current version of the CFD code is able to provide a satisfactory and practical means of modelling jet fire and extinguishment processes.