Over the course of the last century, electronic structure theory (or, alternatively, computational quantum chemistry) has grown from being a fledgling field to being a "full partner with experiment" [Goddard Science 1985, 227 (4689), 917--923]. Numerous instances of theory matching experiment to very high accuracy abound, with one excellent example being the high-accuracy ab initio thermochemical data laid out in the 2004 work of Tajti and co-workers [Tajti et al. J. Chem. Phys. 2004, 121, 11599] and another being the heats of formation and molecular structures computed by Feller and co-workers in 2008 [Feller et al. J. Chem. Phys. 2008, 129, 204105]. But as the authors of both studies point out, this very high accuracy comes at a very high cost. In fact, at this point in time, electronic structure theory does not suffer from an accuracy problem (as it did in its early days) but a cost problem; or, perhaps more precisely, it suffers from an accuracy-to-cost ratio problem. We can compute electronic energies to nearly any precision we like, as long as we are willing to pay the associated cost. And just what are these high computational costs? For the purposes of this work, we are primarily concerned with the way in which the computational cost of a given method scales with the system size; for notational purposes, we will often introduce a parameter, N, that is proportional to the system size. In the case of Hartree-Fock, a one-body wavefunction-based method, the scaling is formally N⁴, and post-Hartree-Fock methods fare even worse. The coupled cluster singles, doubles, and perturbative triples method [CCSD(T)], which is frequently referred to as the "gold standard" of quantum chemistry, has an N⁷ scaling, making it inapplicable to many systems of real-world import. If highly accurate correlated wavefunction methods are to be applied to larger systems of interest, it is crucial that we reduce their computational scaling. One very successful means of doing this relies on the fact that electron correlation is fundamentally a local phenomenon, and the recognition of this fact has led to the development of numerous local implementations of conventional many-body methods. One such method, the DLPNO-CCSD(T) method, was successfully used to calculate the energy of the protein crambin [Riplinger, et al. J. Chem. Phys 2013, 139, 134101]. In the following work, we discuss how the local nature of electron correlation can be exploited, both in terms of the occupied orbitals and the unoccupied (or virtual) orbitals. In the case of the former, we highlight some of the historical developments in orbital localization before applying orbital localization robustly to infinite periodic crystalline systems [Clement, et al. 2021, Submitted to J. Chem. Theory Comput.]. In the case of the latter, we discuss a number of different ways in which the virtual space can be compressed before presenting our pioneering work in the area of iteratively-optimized pair natural orbitals ("iPNOs") [Clement, et al. J. Chem. Theory Comput. 2018, 14 (9), 4581--4589]. Concerning the iPNOs, we were able to recover significant accuracy with respect to traditional PNOs (which are unchanged throughout the course of a correlated calculation) at a comparable truncation level, indicating that our improved PNOs are, in fact, an improved representation of the coupled cluster doubles amplitudes. For example, when studying the percent errors in the absolute correlation energies of a representative sample of weakly bound dimers chosen from the S66 test suite [Řezác, et al. J. Chem. Theory Comput. 2011, 7 (8), 2427--2438], we found that our iPNO-CCSD scheme outperformed the standard PNO-CCSD scheme at every truncation threshold (τ_PNO) studied. Both PNO-based methods were compared to the canonical CCSD method, with the iPNO-CCSD method being, on average, 1.9 times better than the PNO-CCSD method at τ_PNO = 10⁻⁷ and more than an order of magnitude better for τ_PNO < 10⁻¹⁰ [Clement, et al. J. Chem. Theory Comput 2018, 14 (9), 4581--4589]. When our improved PNOs are combined with the PNO-incompleteness correction proposed by Neese and co-workers [Neese, et al. J. Chem. Phys. 2009, 130, 114108; Neese, et al. J. Chem. Phys. 2009, 131, 064103], the results are truly astounding. For a truncation threshold of τ_PNO = 10⁻⁶, the mean average absolute error in binding energy for all 66 dimers from the S66 test set was 3 times smaller when the incompleteness-corrected iPNO-CCSD method was used relative to the incompleteness-corrected PNO-CCSD method [Clement, et al. J. Chem. Theory Comput. 2018, 14 (9), 4581--4589]. In the latter half of this work, we present our implementation of a limited-memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) based Pipek-Mezey Wannier function (PMWF) solver [Clement, et al. 2021 }, Submitted to J. Chem. Theory Comput.]. Although orbital localization in the context of the linear combination of atomic orbitals (LCAO) representation of periodic crystalline solids is not new [Marzari, et al. Rev. Mod. Phys. 2012, 84 (4), 1419--1475; Jònsson, et al. J. Chem. Theory Comput. 2017, 13} (2), 460--474], to our knowledge, this is the first implementation to be based on a BFGS solver. In addition, we are pleased to report that our novel BFGS-based solver is extremely robust in terms of the initial guess and the size of the history employed, with the final results and the time to solution, as measured in number of iterations required, being essentially independent of these initial choices. Furthermore, our BFGS-based solver converges much more quickly and consistently than either a steepest ascent (SA) or a non-linear conjugate gradient (CG) based solver, with this fact demonstrated for a number of 1-, 2-, and 3-dimensional systems. Armed with our real, localized Wannier functions, we are now in a position to pursue the application of local implementations of correlated many-body methods to the arena of periodic crystalline solids; a first step toward this goal will, most likely, be the study of PNOs, both conventional and iteratively-optimized, in this context. / Doctor of Philosophy / Increasingly, the study of chemistry is moving from the traditional wet lab to the realm of computers. The physical laws that govern the behavior of chemical systems, along with the corresponding mathematical expressions, have long been known. Rapid growth in computational technology has made solving these equations, at least in an approximate manner, relatively easy for a large number of molecular and solid systems. That the equations must be solved approximately is an unfortunate fact of life, stemming from the mathematical structure of the equations themselves, and much effort has been poured into developing better and better approximations, each trying to balance an acceptable level of accuracy loss with a realistic level of computational cost and complexity. But though there has been much progress in developing approximate computational chemistry methods, there is still great work to be done. Many chemical systems of real-world import (particularly biomolecules and potential pharmaceuticals) are simply too large to be treated with any methods that consistently deliver acceptable accuracy. As an example of the difficulties that come with trying to apply accurate computational methods to systems of interest, consider the seminal 2013 work of Riplinger and co-workers [Riplinger, et al. J. Chem. Phys. 2013, 139, 134101]. In this paper, they present the results of a calculation performed on the protein crambin. The method used was DLPNO-CCSD(T), an approximation to the "gold standard" computational method CCSD(T). The acronym DLPNO-CCSD(T) stands for "`domain-based local pair natural orbital coupled cluster with singles, doubles, and perturbative triples." In essence, this method exploits the fact that electron-electron interactions ("electron correlation") are a short-range phenomenon in order to represent the system in a mathematically more compact way. This focus on the locality of electron correlation is a crucial piece in the effort to bring down computational cost. When talking about computational cost, we will often talk about how the cost scales with the approximate system size N. In the case of CCSD(T), the cost scales as N⁷. To see what this means, consider two chemical systems A and B. If system B is twice as large as system A, then the same calculation run on both systems will take 2⁷ = 128 times longer on system B than on system A. The DLPNO-CCSD(T) method, on the other hand, scales linearly with the system size, provided the system is sufficiently large (we say that it is "asymptotically linearly scaling"), and so, for our example systems A and B, the calculation run on system B should only take twice as long as the calculation run on system A. But despite the favorable scaling afforded by the DLPNO-CCSD(T) method, the time to solution is still prohibitive. In the case of crambin, a relatively small protein with 644 atoms, the calculation took a little over 30 days. Clearly, such timescales are unworkable for the field of biochemical research, where the focus is often on the interactions between multiple proteins or other large biomolecules and where many more data points are required. In the work that follows, we discuss in more detail the genesis of the high costs that are associated with highly accurate computational methods, as well as some of the approximation techniques that have already been employed, with an emphasis on local correlation techniques. We then build off this foundation to discuss our own work and how we have extended such approximation techniques in an attempt to further increase the possible accuracy to cost ratio. In particular, we discuss how iteratively-optimized pair natural orbitals (the PNOs of the DLPNO-CCSD(T) method) can provide a more accurate but also more compact mathematical representation of the system relative to static PNOs [Clement, et al. J. Chem. Theory Comput. 2018, 14 (9), 4581--4589]. Additionally, we turn our attention to the problem of periodic infinite crystalline systems, a class of materials less commonly studied in the field of computational chemistry, and discuss how the local correlation techniques that have already been applied with great success to molecular systems can potentially be applied in this domain as well [Clement, et al. 2021, Submitted to J. Chem. Theory Comput.].

Modeling and simulation have played an essential role in understanding the effects of blast waves. However, a broad area of engineering problems, such as vehicle structures, buildings, bridges, or even the human body, can benefit by accurately predicting the response to blasts with little need for test or event data. This thesis reviews fundamental concepts of blast waves and explosives and discusses research in blast scaling. Blast scaling is a method that reduces the computational costs associated with modeling blasts by using empirical data and numerically calculating blast field parameters over time for various types and sizes of explosives. This computational efficiency is critical in studying blast waves' near and far-field effects. This thesis also reviews research to differentiate between free-air blasts and gun muzzle blasts and the progress of modeling the muzzle blast-structure interaction. The main focus of this thesis covers an investigation of different numerical and analytical solutions to a simple aerospace structure subjected to blast pressure. The thesis finally presents a tool that creates finite element loads utilizing muzzle blast scaling methods. This tool reduces modeling complexity and the need for multiple domains such as coupled computational fluid dynamics and finite element models by coupling blast scaling methods to a finite element model. / Master of Science / {Numerical integration methods have helped solve many complex problems in engineering and science due to their ability to solve non-linear equations that describe many phenomena. These methods are beneficial because of how well they lend to programming into a computer, and their impact has grown with the increases in computing power. In this thesis, ``modeling and simulation" refers to the characterization and prediction of an event's outcome through the use of computers and numerical techniques. Modeling and simulation play important roles in studying the effects of blast waves in many areas of engineering research such as aerospace, biomedical, naval, and civil. Their capability to predict the outcome of the interaction of a blast wave to vehicle structures, buildings, bridges, or even the human body while requiring limited experimental data has the chance to benefit a wide area of engineering problems. This thesis reviews fundamental concepts of blast waves, explosives, and research that has applied blast loading in modeling and simulation. This thesis describes the complexity of modeling an axially symmetric blast wave interaction by comparing the numerical and theoretical response blast loaded structure.

This dissertation is the compilation of two major innovations that rely on a common technique known as multidimensional scaling (MDS). MDS is a dimension-reduction method that takes high-dimensional data and creates low-dimensional versions. Project 1: Visualizations are useful when learning from high-dimensional data. However, visualizations, just as any data summary, can be misleading when they do not incorporate measures of uncertainty; e.g., uncertainty from the data or the dimension reduction algorithm used to create the visual display. We incorporate uncertainty into visualizations created by a weighted version of MDS called WMDS. Uncertainty exists in these visualizations on the variable weights, the coordinates of the display, and the fit of WMDS. We quantify these uncertainties using Bayesian models in a method we call Informative Probabilistic WMDS (IP-WMDS). Visually, we display estimated uncertainty in the form of color and ellipses, and practically, these uncertainties reflect trust in WMDS. Our results show that these displays of uncertainty highlight different aspects of the visualization, which can help inform analysts. Project 2: Analysis of network data has emerged as an active research area in statistics. Much of the focus of ongoing research has been on static networks that represent a single snapshot or aggregated historical data unchanging over time. However, most networks result from temporally-evolving systems that exhibit intrinsic dynamic behavior. Monitoring such temporally-varying networks to detect anomalous changes has applications in both social and physical sciences. In this work, we simulate data from models that rely on MDS, and we perform an evaluation study of the use of summary statistics for anomaly detection by incorporating principles from statistical process monitoring. In contrast to most previous studies, we deliberately incorporate temporal auto-correlation in our study. Other considerations in our comprehensive assessment include types and duration of anomaly, model type, and sparsity in temporally-evolving networks. We conclude that the use of summary statistics can be valuable tools for network monitoring and often perform better than more involved techniques. / Doctor of Philosophy / In this work, two main ideas in data visualization and anomaly detection in dynamic networks are further explored. For both ideas, a connecting theme is extensions of a method called Multidimensional Scaling (MDS). MDS is a dimension-reduction method that takes high-dimensional data (all $p$ dimensions) and creates a low-dimensional projection of the data. That is, relationships in a dataset with presumably a large number of dimensions or variables can be summarized into a lower number of, e.g., two, dimensions. For a given data, an analyst could use a scatterplot to observe the relationship between 2 variables initially. Then, by coloring points, changing the size of the points, or using different shapes for the points, perhaps another 3 to 4 more variables (in total around 7 variables) may be shown in the scatterplot. An advantage of MDS (or any dimension-reduction technique) is that relationships among the data can be viewed easily in a scatterplot regardless of the number of variables in the data. The interpretation of any MDS plot is that observations that are close together are relatively more similar than observations that are farther apart, i.e., proximity in the scatterplot indicates relative similarity. In the first project, we use a weighted version of MDS called Weighted Multidimensional Scaling (WMDS) where weights, which indicate a sense of importance, are placed on the variables of the data. The problem with any WMDS plot is that inaccuracies of the method are not included in the plot. For example, is an observation that appears to be an outlier, really an outlier? An analyst cannot confirm this without further context. Thus, we created a model to calculate, visualize, and interpret such inaccuracy or uncertainty in WMDS plots. Such modeling efforts help analysts facilitate exploratory data analysis. In the second project, the theme of MDS is extended to an application with dynamic networks. Dynamic networks are multiple snapshots of pairwise interactions (represented as edges) among a set of nodes (observations). Over time, changes may appear in some of the snapshots. We aim to detect such changes using a process monitoring approach on dynamic networks. Statistical monitoring approaches determine thresholds for in-control or expected behavior that are calculated from data with no signal. Then, the in-control thresholds are used to monitor newly collected data. We applied this approach on dynamic network data, and we utilized a detailed simulation study to better understand the performance of such monitoring. For the simulation study, data are generated from dynamic network models that use MDS. We found that monitoring summary statistics of the network were quite effective on data generated from these models. Thus, simple tools may be used as a first step to anomaly detection in dynamic networks.

A recently-proposed modification of the Thurstone-Mosteller method of paired comparisons makes possible the analysis of data involving tied observations. The modification includes the postulating of an angular response law such that the response proportions are scaled with arc sine transforms instead of with normal deviates. In this paper a comparison is made of the arc sine and normal curve scaling procedures in paired comparisons involving ties. This is done by applying both methods to data from to important fields of application. Comparisons are also made on several series of hypothetical data. The criterion of comparison is the goodness of fit between the observations and the expected numbers computed from the solution, as measured by means of a chi-square statistic. Computations of parameter estimates and chi-square statistics are made with the aid of an IBM-650, for which the necessary programs have been written. It is concluded that for data conforming well to the model as proposed, both scaling procedures tend to give results in satisfactory agreement with the observations. There is some evidence that, for the cases considered, the preference, if any, is for the normal curve procedure. / Master of Science

Quantum mechanical methods are becoming increasingly useful and applicable tools to complement and support experiment. Nonetheless, some barriers to further applications of theoretical models still remain. A coupled cluster singles and doubles (CCSD) calculation, a reliable ab initio method, scales approximately on the order of 𝑂(𝑁⁶), where 𝑁 is a measure of the system size. This unfortunately limits the use of such high-accuracy methods to relatively small systems. Coupled cluster property calculations must be used in conjunction with reduced-scaling methods in order to broaden the range of applications to larger systems. In this work, we introduce some of the underlying theory behind such calculations and test the performance of several local correlation techniques for polarizabilities, optical rotations, and excited state properties. In general, when the computational cost is significantly reduced, the necessary accuracy is lost. Polarizabilities are less sensitive to the truncation schemes than optical rotations, and the excitation data is often only in agreement with the canonical result for the first few excited states. Additionally, we present a novel application of equation-of-motion coupled cluster singles and doubles to simulated circularly polarized luminescence spectra of eight chiral ketones. Both the absorption in the ground state and emission from the excited states were examined. Extensive geometry analyses were performed, revealing that optimized structures at the density functional theory were adequate for the calculation accurate coupled cluster excitation data. / Ph. D.

Microbial fuel cells (MFCs) are potentially advantageous as an energy-efficient approach to wastewater treatment. For single-chamber tubular MFCs, anode effluent is used as catholyte instead of tap water or buffer solutions. Therefore, exposing cathode electrode to atmosphere could be also considered as a passive aeration for further aerobic oxidation of organics and nitrification. Based on several bench-scale studies, a 200-L scale MFC system with passive aeration process has been developed for treating actual municipal wastewater after primary clarification. The integrated system was able to remove over 80% organic contaminants and solid content from primary effluent. Through parallel and serial electricity connection, the power output of ~200 mW and the conversion efficiency of ~80% for charging capacitors were achieved by using commercially available energy harvesting device (BQ 25504). The treatment system is energy-efficient for the energy saving from aeration and sludge treatment while partial energy recovery as direct electricity can be utilized on site to power small electric devices. However, the post treatments are required to polish the effluent for nutrients removal. / Ph. D.

This dissertation explores several challenges that plague the hardware-software co-design of popular resource constrained real-time embedded systems. We specifically tackle existing real-world problems, and address them through our design solutions which are highly scalable, and have practical feasibility as verified through our solution implementation on real-world hardware. We address the problem of poor battery life in mobile embedded devices caused due to side-by-side execution of multiple applications in split-screen mode. Existing industry solutions either restricts the number of applications that can run simultaneously, limit their functionality, and/or increase the hardware capacity of the battery associated with the system. We exploit the gap in research on performance and power trade-off in smartphones to propose an integrated energy management solution, that judiciously minimizes the system-wide energy consumption with negligible effect on its quality of service (QoS). Another important real-world requirement in today's interconnected world is the need for security. In the domain of real-time computing, it is not only necessary to secure the system but also maintain its timeliness. Some example security mechanisms that may be used in a hard real-time system include, but are not limited to, security keys, protection of intellectual property (IP) of firmware and application software, one time password (OTP) for software certification on-the-fly, and authenticated computational off-loading. Existing design solutions require expensive, custom-built hardware with long time-to-market or time-to-deployment cycle. A readily available alternative is the use of trusted execution environment (TEE) on commercial off-the-shelf (COTS) embedded processors. However, utilizing TEE creates multiple challenges from a real-time perspective, which includes additional time overhead resulting in possible deadline misses. Second, trusted execution may adversely affect the deterministic execution of the system, as tasks running inside a TEE may need to communicate with other tasks that are executing on the native real-time operating system. We propose three different solutions to address the need for a new task model that can capture the complex relationship between performance and predictability for real-time tasks that require secure execution inside TEE. We also present novel task assignment and scheduling frameworks for real-time trusted execution on COTS processors to improve task set schedulability. We extensively assess the pros and cons of our proposed approaches in comparison to the state-of-the-art techniques in custom-built real-world hardware for feasibility, and simulated environments to test our solutions' scalability. / Doctor of Philosophy / Today's real-world problems demand real-time solutions. These solutions need to be practically feasible, and scale well with increasing end user demands. They also need to maintain a balance between system performance and predictability, while achieving minimum energy consumption. A recent example of technological design problem involves ways to improve the battery lifetime of mobile embedded devices, for example, smartphones, while still achieving the required performance objectives. For instance, smartphones that run Android OS has the capability to run multiple applications concurrently using a newly introduced split-screen mode of execution, where applications can run side-by-side at the same time on screen while using the same shared resources (e.g., CPU, memory bandwidth, peripheral devices etc.). While this can improve the overall performance of the system, it can also lead to increased energy consumption, thereby directly affecting the battery life. Another technological design problem involves ways to protect confidential proprietary information from being siphoned out of devices by external attackers. Let us consider a surveillance unmanned aerial vehicle (UAV) as an example. The UAV must perform sensitive tasks, such as obtaining coordinates of interest for surveillance, within a given time duration, also known as task deadline. However, an attacker may learn how the UAV communicates with ground control, and take control of the UAV, along with the sensitive information it carries. Therefore, it is crucial to protect such sensitive information from access by an unauthorized party, while maintaining the system's task deadlines. In this dissertation, we explore these two real-world design problems in depth, observe the challenges associated with them, while presenting several solutions to tackle the issues. We extensively assess the pros and cons of our proposed approaches in comparison to the state-of- the-art techniques in custom-built real-world hardware, and simulated environments to test our solutions' scalability.

Wave function based electronic structure methods have became a robust and reliable tool for the prediction and interpretation of the results of chemical experiments. However, they suffer from very steep scaling behavior with respect to an increase in the size of the system as well as very slow convergence of the correlation energy with respect to the basis set size. Thus these methods are limited to small systems of up to a dozen atoms. The first of these issues can be efficiently resolved by exploiting the local nature of electron correlation effects while the second problem is alleviated by the use of explicitly correlated R12/F12 methods. Since R12/F12 methods are central to this work, we start by reviewing their modern formulation. Next, we present the explicitly correlated second-order Mo ller-Plesset (MP2-F12) method in which all nontrivial post-mean-field steps are formulated with linear computational complexity in system size [Pavov{s}evi'c et al., {em J. Chem. Phys.} {bf 144}, 144109 (2016)]. The two key ideas are the use of pair-natural orbitals for compact representation of wave function amplitudes and the use of domain approximation to impose the block sparsity. This development utilizes the concepts for sparse representation of tensors described in the context of the DLPNO-MP2 method by Neese, Valeev and co-workers [Pinski et al., {em J. Chem. Phys.} {bf 143}, 034108 (2015)]. Novel developments reported here include the use of domains not only for the projected atomic orbitals, but also for the complementary auxiliary basis set (CABS) used to approximate the three- and four-electron integrals of the F12 theory, and a simplification of the standard B intermediate of the F12 theory that avoids computation of four-index two-electron integrals that involve two CABS indices. For quasi-1-dimensional systems (n-alkanes) the bigO{N} DLPNO-MP2-F12 method becomes less expensive than the conventional bigO{N^{5}} MP2-F12 for $n$ between 10 and 15, for double- and triple-zeta basis sets; for the largest alkane, C$_{200}$H$_{402}$, in def2-TZVP basis the observed computational complexity is $N^{sim1.6}$, largely due to the cubic cost of computing the mean-field operators. The method reproduces the canonical MP2-F12 energy with high precision: 99.9% of the canonical correlation energy is recovered with the default truncation parameters. Although its cost is significantly higher than that of DLPNO-MP2 method, the cost increase is compensated by the great reduction of the basis set error due to explicit correlation. We extend this formalism to develop a linear-scaling coupled-cluster singles and doubles with perturbative inclusion of triples and explicitly correlated geminals [Pavov{s}evi'c et al., {em J. Chem. Phys.} {bf 146}, 174108 (2017)]. Even for conservative truncation levels, the method rapidly reaches near-linear complexity in realistic basis sets; e.g., an effective scaling exponent of 1.49 was obtained for n-alkanes with up to 200 carbon atoms in a def2-TZVP basis set. The robustness of the method is benchmarked against the massively parallel implementation of the conventional explicitly correlated coupled-cluster for a 20-water cluster; the total dissociation energy of the cluster ($sim$186 kcal/mol) is affected by the reduced-scaling approximations by only $sim$0.4 kcal/mol. The reduced-scaling explicitly correlated CCSD(T) method is used to examine the binding energies of several systems in the L7 benchmark data set of noncovalent interactions. Additionally, we discuss a massively parallel implementation of the Laplace transform perturbative triple correction (T) to the DF-CCSD energy within density fitting framework. This work is closely related to the work by Scuseria and co-workers [Constans et al., {em J. Chem. Phys.} {bf 113}, 10451 (2000)]. The accuracy of quadrature with respect to the number of quadrature points has been investigated on systems of the 18-water cluster, uracil dimer and pentacene dimer. In the case of the 18-water cluster, the $mu text{E}_{text{h}}$ accuracy is achieved with only 3 quadrature points. For the uracil dimer and pentacene dimer, 6 or more quadrature points are required to achieve $mu text{E}_{text{h}}$ accuracy; however, binding energy of $<$1 kcal/mol is obtained with 4 quadrature points. We observe an excellent strong scaling behavior on distributed-memory commodity cluster for the 18-water cluster. Furthermore, the Laplace transform formulation of (T) performs faster than the canonical (T) in the case of studied systems. The efficiency of the method has been furthermore tested on a DNA base-pair, a system with more than one thousand basis functions. Lastly, we discuss an explicitly correlated formalism for the second-order single-particle Green's function method (GF2-F12) that does not assume the popular diagonal approximation, and describes the energy dependence of the explicitly correlated terms [Pavov{s}evi'c et al., {em J. Chem. Phys.} {bf 147}, 121101 (2017)]. For small and medium organic molecules the basis set errors of ionization potentials of GF2-F12 are radically improved relative to GF2: the performance of GF2-F12/aug-cc-pVDZ is better than that of GF2/aug-cc-pVQZ, at a significantly lower cost. / Ph. D. / Chemistry has traditionally been considered an experimental science; however, since the dawn of quantum mechanics, scientists have investigated the possibility of predicting the outcomes of chemical experiments via the use of mathematical models. All molecular properties are encoded in the motion of the electrons, which can be quantitatively described by the many-body Schrödinger equation. However, the Schrödinger equation is too complicated to be solved exactly for realistic molecular systems, and so we must rely on approximations. The most popular way to solve the Schrödinger equation when high accuracy is required are the coupled-cluster (CC) family of methods. These methods can provide unsurpassed accuracy; one particularly accurate and popular method is the coupled-cluster singles and doubles with perturbative inclusion of triples (CCSD(T)) method. The CCSD(T) method is known as the “gold standard” of quantum chemistry, and, when combined with a high quality basis set, it gives highly accurate predictions (that is, close to the experimental results) for a variety of chemical properties. However, this method has a very steep scaling behavior with a computational cost of N⁷ , where N is the measure of the system size. This means that if we double the size of the system, the computation time will increase by roughly two orders of magnitude. Another problem is that this method shows very slow convergence to the complete basis set (CBS) limit. Thus, in order to reduce the basis set error caused by the incompleteness of the basis set, more than 100 basis functions per atom should be used, limiting this method to small systems of up to a dozen atoms. These two issues can be efficiently resolved by exploiting the local nature of electron correlation effects (reduced-scaling techniques) and by using explicitly correlated R12/F12 methods. The main focus of this thesis is to bridge the gap between reduced-scaling techniques and the explicit correlation formalism and to allow highly accurate calculations on large molecular systems with several hundred of atoms. As our first contribution to this field, we present a linear-scaling formulation of the explicitly correlated second-order Møller-Plesset method (MP2-F12) [Pavoŝević et al., J. Chem. Phys. 144, 144109 (2016)]. This is achieved by the use of pair-natural orbitals (PNOs) for the compact representation of the unoccupied space. The method shows near-linear scaling behavior on the linear alkane chains with a computational scaling of N^1.6 for the largest alkane, C₂₀₀H₄₀₂, recovering more than 99.9% of correlation energy. The MP2-F12 method is intrinsically inadequate if high accuracy is required, but our formulation of the linear-scaling MP2-F12 method lays a solid foundation for the accurate linear-scaling explicitly correlated coupled-cluster singles and doubles method with perturbative inclusion of triples (PNO-CCSD(T)-F12) [Pavoŝević et al., J. Chem. Phys. 146, 174108 (2017)]. We have demonstrated that the PNO-CCSD(T)-F12 method shows a near-linear scaling behavior of N^1.5 . The error introduced by reduce-scaling approximations is only 0.4 kcal/mol of the binding energy with respect to the canonical result in the case of a 20-water cluster which is much lower than the required chemical accuracy defined as 1 kcal/mol. Furthermore, the reduced-scaling explicitly correlated CCSD(T) method is used to examine the binding ener- gies of large molecular systems that are far beyond the reach of the conventional CCSD(T) method. Our prediction of the binding energy for of the coronene dimer is the most accurate theoretical estimate of binding energy of the coronene dimer to this date. Such a system is an example of an organic semiconductor used for light conversion. However, the modeling of light harvesting materials requires an accurate knowledge of ionization potentials (IP) and electron affinities (EA). We describe [Pavoŝević et al., J. Chem. Phys. 147, 121101 (2017)] how to incorporate an explicit correlation correction into the Green’s function formalism (GF2) that is used for the calculation of IPs. We show that the GF2-F12 method removes errors associated with the basis sets, allowing extremely accurate predictions of IPs to be made at a significantly lower cost than the parent GF2 method. The work presented in this thesis will set a stage for further developments in reduced-scaling explicitly correlated methods. Furthermore it will be a useful benchmarking method for parametrizing the popular DFT functionals making accurate predictions of the relative stability of different forms of pharmaceuticals. Due to the simplicity and generality of the GF2-F12 method, it has the potential to be used to augment more accurate Green’s function methods, such as NR2, allowing for the accurate prediction of IPs and EAs of large molecular and periodic systems.

This dissertation is a collection of papers on anomalous phenomena in physics, biology, and sociology. These phenomena are primarily analyzed in terms of their temporal and spatiotemporal statistical properties. The analysis is based on both numerical simulations and, in some cases, real-world physiological and sociological data. The primary methods of analysis are diffusion entropy analysis, power spectral analysis, multifractal analysis, and survival (or waiting-time) analysis.

<p>Density functional theory is a formally exact theory to describe ground state properties due to the existence of the exact functional. In practice, the usefulness of density functional theory relies on the accuracy of density functional approximations. After decades of effort of functional developments, the present-day state-of-the-art density functional approximations have achieved reasonably good accuracy for small systems. However, the error grows with system size. One of the dominant errors intrinsic in the mainstream density functional approximations is the delocalization error, which arises because of the violation of Perdew-Parr-Levy-Balduz (PPLB) linearity condition. The PPLB condition governs the formulation of the density functional theory for fractional-charge systems, for which the ground state energy for the exact functional, as a function of the fractional electron number, should yield a series of line-segments across the integer points. In this dissertation, by imposing the PPLB condition in a local, size-consistent way, we develop the local scaling correction (LSC) and its updated version, the localized orbital scaling correction (LOSC), which largely improve upon the mainstream density functional approximations across system sizes. With the LOSC, we open a door towards a systematic elimination of delocalization error. Besides the ground state functional development, we also develop a gentlest ascent dynamics approach for accessing the excited states via time-independent ground state density functionals. This is also useful for exploring Kohn-Sham energy landscapes of approximate density functionals. I will also review the PPLB formulation of density functional theory for fractionally charged systems, and show that it is equivalent to the formulation normally used for fractional system calculations under certain assumptions. Furthermore, I will examine the behavior of the fractional system energy as a function of the fractional number of electrons for different mainstream functionals, and relate it to their errors for integer systems.</p> / Dissertation