Computational modeling of defects in nanoscale device materials

This PhD thesis concerns the computational modeling of the electronic and atomic structure of point defects in technologically relevant materials. Identifying the atomistic origin of defects observed in the electrical characteristics of electronic devices has been a long-term goal of first-principles methods. First principles simulations are performed in this thesis, consisting of density functional theory (DFT) supplemented with many body perturbation theory (MBPT) methods, of native defects in bulk and slab models of In0.53Ga0.47As. The latter consist of (100) - oriented surfaces passivated with A12O3. Our results indicate that the experimentally extracted midgap interface state density (Dit) peaks are not the result of defects directly at the semiconductor/oxide interface, but originate from defects in a more bulk-like chemical environment. This conclusion is reached by considering the energy of charge transition levels for defects at the interface as a function of distance from the oxide. Our work provides insight into the types of defects responsible for the observed departure from ideal electrical behaviour in III-V metal-oxidesemiconductor (MOS) capacitors. In addition, the formation energetics and electron scattering properties of point defects in carbon nanotubes (CNTs) are studied using DFT in conjunction with Green’s function based techniques. The latter are applied to evaluate the low-temperature, low-bias Landauer conductance spectrum from which mesoscopic transport properties such as the elastic mean free path and localization length of technologically relevant CNT sizes can be estimated from computationally tractable CNT models. Our calculations show that at CNT diameters pertinent to interconnect applications, the 555777 divacancy defect results in increased scattering and hence higher electrical resistance for electron transport near the Fermi level.

Computational study of the growth of copper thin films by atomic layer deposition

Copper is the main interconnect material in microelectronic devices, and a 2 nm-thick continuous Cu film seed layer needs to be deposited to produce microelectronic devices with the smallest features and more functionality. Atomic layer deposition (ALD) is the most suitable method to deposit such thin films. However, the reaction mechanism and the surface chemistry of copper ALD remain unclear, which is deterring the development of better precursors and design of new ALD processes. In this thesis, we study the surface chemistries during ALD of copper by means of density functional theory (DFT). To understand the effect of temperature and pressure on the composition of copper with substrates, we used ab initio atomistic thermodynamics to obtain phase diagram of the Cu(111)/SiO2(0001) interface. We found that the interfacial oxide Cu2O phases prefer high oxygen pressure and low temperature while the silicide phases are stable at low oxygen pressure and high temperature for Cu/SiO2 interface, which is in good agreement with experimental observations. Understanding the precursor adsorption on surfaces is important for understanding the surface chemistry and reaction mechanism of the Cu ALD process. Focusing on two common Cu ALD precursors, Cu(dmap)2 and Cu(acac)2, we studied the precursor adsorption on Cu surfaces by means of van der Waals (vdW) inclusive DFT methods. We found that the adsorption energies and adsorption geometries are dependent on the adsorption sites and on the method used to include vdW in the DFT calculation. Both precursor molecules are partially decomposed and the Cu cations are partially reduced in their chemisorbed structure. It is found that clean cleavage of the ligand−metal bond is one of the requirements for selecting precursors for ALD of metals. 2 Bonding between surface and an atom in the ligand which is not coordinated with the Cu may result in impurities in the thin film. To have insight into the reaction mechanism of a full ALD cycle of Cu ALD, we proposed reaction pathways based on activation energies and reaction energies for a range of surface reactions between Cu(dmap)2 and Et2Zn. The butane formation and desorption steps are found to be extremely exothermic, explaining the ALD reaction scheme of original experimental work. Endothermic ligand diffusion and re-ordering steps may result in residual dmap ligands blocking surface sites at the end of the Et2Zn pulse, and in residual Zn being reduced and incorporated as an impurity. This may lead to very slow growth rate, as was the case in the experimental work. By investigating the reduction of CuO to metallic Cu, we elucidated the role of the reducing agent in indirect ALD of Cu. We found that CuO bulk is protected from reduction during vacuum annealing by the CuO surface and that H2 is required in order to reduce that surface, which shows that the strength of reducing agent is important to obtain fully reduced metal thin films during indirect ALD processes. Overall, in this thesis, we studied the surface chemistries and reaction mechanisms of Cu ALD processes and the nucleation of Cu to form a thin film.

Additions à la bibliographie de Charles Porset sur les sciences du langage de Port-Royal aux Idéologues (1660-1818)

info:eu-repo/semantics/published

Comprehensive search for new phases and compounds in binary alloy systems based on platinum-group metals, using a computational first-principles approach

We report a comprehensive study of the binary systems of the platinum-group metals with the transition metals, using high-throughput first-principles calculations. These computations predict stability of new compounds in 28 binary systems where no compounds have been reported in the literature experimentally and a few dozen of as-yet unreported compounds in additional systems. Our calculations also identify stable structures at compound compositions that have been previously reported without detailed structural data and indicate that some experimentally reported compounds may actually be unstable at low temperatures. With these results, we construct enhanced structure maps for the binary alloys of platinum-group metals. These maps are much more complete, systematic, and predictive than those based on empirical results alone.

Novel Computational Protein Design Algorithms with Applications to Cystic Fibrosis and HIV

Proteins are essential components of cells and are crucial for catalyzing reactions, signaling, recognition, motility, recycling, and structural stability. This diversity of function suggests that nature is only scratching the surface of protein functional space. Protein function is determined by structure, which in turn is determined predominantly by amino acid sequence. Protein design aims to explore protein sequence and conformational space to design novel proteins with new or improved function. The vast number of possible protein sequences makes exploring the space a challenging problem.

Computational structure-based protein design (CSPD) allows for the rational design of proteins. Because of the large search space, CSPD methods must balance search accuracy and modeling simplifications. We have developed algorithms that allow for the accurate and efficient search of protein conformational space. Specifically, we focus on algorithms that maintain provability, account for protein flexibility, and use ensemble-based rankings. We present several novel algorithms for incorporating improved flexibility into CSPD with continuous rotamers. We applied these algorithms to two biomedically important design problems. We designed peptide inhibitors of the cystic fibrosis agonist CAL that were able to restore function of the vital cystic fibrosis protein CFTR. We also designed improved HIV antibodies and nanobodies to combat HIV infections.

Computational inference of neural information flow networks.

Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.

Computational Journalism: from Answering Question to Questioning Answers and Raising Good Questions

Our media is saturated with claims of ``facts'' made from data. Database research has in the past focused on how to answer queries, but has not devoted much attention to discerning more subtle qualities of the resulting claims, e.g., is a claim ``cherry-picking''? This paper proposes a Query Response Surface (QRS) based framework that models claims based on structured data as parameterized queries. A key insight is that we can learn a lot about a claim by perturbing its parameters and seeing how its conclusion changes. This framework lets us formulate and tackle practical fact-checking tasks --- reverse-engineering vague claims, and countering questionable claims --- as computational problems. Within the QRS based framework, we take one step further, and propose a problem along with efficient algorithms for finding high-quality claims of a given form from data, i.e. raising good questions, in the first place. This is achieved to using a limited number of high-valued claims to represent high-valued regions of the QRS. Besides the general purpose high-quality claim finding problem, lead-finding can be tailored towards specific claim quality measures, also defined within the QRS framework. An example of uniqueness-based lead-finding is presented for ``one-of-the-few'' claims, landing in interpretable high-quality claims, and an adjustable mechanism for ranking objects, e.g. NBA players, based on what claims can be made for them. Finally, we study the use of visualization as a powerful way of conveying results of a large number of claims. An efficient two stage sampling algorithm is proposed for generating input of 2d scatter plot with heatmap, evalutaing a limited amount of data, while preserving the two essential visual features, namely outliers and clusters. For all the problems, we present real-world examples and experiments that demonstrate the power of our model, efficiency of our algorithms, and usefulness of their results.

Computational Methods for RNA Structure Validation and Improvement.

With increasing recognition of the roles RNA molecules and RNA/protein complexes play in an unexpected variety of biological processes, understanding of RNA structure-function relationships is of high current importance. To make clean biological interpretations from three-dimensional structures, it is imperative to have high-quality, accurate RNA crystal structures available, and the community has thoroughly embraced that goal. However, due to the many degrees of freedom inherent in RNA structure (especially for the backbone), it is a significant challenge to succeed in building accurate experimental models for RNA structures. This chapter describes the tools and techniques our research group and our collaborators have developed over the years to help RNA structural biologists both evaluate and achieve better accuracy. Expert analysis of large, high-resolution, quality-conscious RNA datasets provides the fundamental information that enables automated methods for robust and efficient error diagnosis in validating RNA structures at all resolutions. The even more crucial goal of correcting the diagnosed outliers has steadily developed toward highly effective, computationally based techniques. Automation enables solving complex issues in large RNA structures, but cannot circumvent the need for thoughtful examination of local details, and so we also provide some guidance for interpreting and acting on the results of current structure validation for RNA.

Computational Inference of Genome-Wide Protein-DNA Interactions Using High-Throughput Genomic Data

Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape.

We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations.

We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites.

Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets.

This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.

A computational analysis of antisense off-targets in prokaryotic organisms

© 2014 .The adoption of antisense gene silencing as a novel disinfectant for prokaryotic organisms is hindered by poor silencing efficiencies. Few studies have considered the effects of off-targets on silencing efficiencies, especially in prokaryotic organisms. In this computational study, a novel algorithm was developed that determined and sorted the number of off-targets as a function of alignment length in Escherichia coli K-12 MG1655 and Mycobacterium tuberculosis H37Rv. The mean number of off-targets per a single location was calculated to be 14.1. ±. 13.3 and 36.1. ±. 58.5 for the genomes of E. coli K-12 MG1655 and M. tuberculosis H37Rv, respectively. Furthermore, when the entire transcriptome was analyzed, it was found that there was no general gene location that could be targeted to minimize or maximize the number of off-targets. In an effort to determine the effects of off-targets on silencing efficiencies, previously published studies were used. Analyses with acpP, ino1, and marORAB revealed a statistically significant relationship between the number of short alignment length off-targets hybrids and the efficacy of the antisense gene silencing, suggesting that the minimization of off-targets may be beneficial for antisense gene silencing in prokaryotic organisms.

The role of questioning at university teaching : some thoughts from the agricultural and environmenal sciences perspectives

p.141-151

Economic theory and environmental sciences, a historic review : from physiocrats to institutionalists

p.57-70

Economic theory and environmental sciences, a historic review : from physiocrats to institutionalists

p.57-70

Modelling of industrial multi-physics processes—a key role for computational mechanics.

In the analysis of industrial processes, there is an increasing emphasis on systems governed by interacting continuum phenomena. Mathematical models of such multi-physics processes can only be achieved for practical simulations through computational solution procedures—computational mechanics. Examples of such multi-physics systems in the context of metals processing are used to explore some of the key issues. Finite-volume methods on unstructured meshes are proposed as a means to achieve efficient rapid solutions to such systems. Issues associated with the software design, the exploitation of high performance computers, and the concept of the virtual computational-mechanics modelling laboratory are also addressed in this context.

From ALIBI to COLUMBUS. The long march to self-aware, reflective computational models of humor

One thing is (a) to develop a system that handles some task to one's satisfaction, and also has a universally recognized myrthful side to its output. Another thing is (b) to provide an analysis of why you are getting such a byproduct. Yet another thing is (c) to develop a model that incorporates reflection about some phenomenon in humor for its own sake. This paper selects for discussion especially Alibi, going on to describe the preliminaries of Columbus. The former, which fits in (a), is a planner with an explanatory capability. It invents pretexts. It's no legal defense, but it is relevant to evidential thinking in AI & Law. Some of the output pretext are myrthful. Not in the sense they are silly: they are not. A key factor seems to be the very alacrity at explaining out detail after detail of globally damning evidence. I attempt a reanalysis of Alibi in respect of (b). As to Columbus, it fits instead in (c). We introduce here the basics of this (unimplemented) model, developed to account for a sample text in parody.