Isotropic gap formation, localization, and waveguiding in mesoscale Yukawa-potential amorphous structures

Amorphous photonic structures are mesoscopic optical structures described by electrical permittivity distributions with underlying spatial randomness. They offer a unique platform for studying a broad set of electromagnetic phenomena, including transverse Anderson localization, enhanced wave transport, and suppressed diffusion in random media. Despite this, at a more practical level, there is insufficient work on both understanding the nature of optical transport and the conditions conducive to vector-wave localization in these planar structures, as well as their potential applications to photonic nanodevices. In this study, we fill this gap by investigating experimentally and theoretically the characteristics of optical transport in a class of amorphous photonic structures and by demonstrating their use to some basic waveguiding nanostructures. We demonstrate that these 2-D structures have unique isotropic and asymmetric band gaps for in-plane propagation, controlled from first principles by varying the scattering strength and whose properties are elucidated by establishing an analogy between photon and carrier transport in amorphous semiconductors. We further observe Urbach band tails in these random structures and uncover their relation to frequency- and disorder-dependent Anderson-like localized modes through the modified Ioffe-Regel criterion and their mean free path - localization length character. Finally, we illustrate that our amorphous structures can serve as a versatile platform in which photonic devices such as disorder-localized waveguides can be readily implemented.Comment: 9 pages, 4 figure

3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning

Generative models have been successfully used to synthesize completely novel images, text, music and speech. As such, they present an exciting opportunity for the design of new materials for functional applications. So far, generative deep-learning methods applied to molecular and drug discovery have yet to produce stable and novel 3-D crystal structures across multiple material classes. To that end, we herein present an autoencoder-based generative deep-representation learning pipeline for geometrically optimized 3-D crystal structures that simultaneously predicts the values of eight target properties. The system is highly general, as demonstrated through creation of novel materials from three separate material classes: binary alloys, ternary perovskites and Heusler compounds. Comparison of these generated structures to those optimized via electronic-structure calculations shows that our generated materials are valid and geometrically optimized

Machine Learning for Structural Characterization and Generation: Applications to Small-Angle Scattering and Electron Microscopy

The era of big-data-driven science has brought to light the need for new methodologies to process and extract otherwise-unattainable insights from the vast amounts of data generated by materials and nanostructural characterization methods. Small-angle scattering (SAS) and electron microscopy (EM) experiments yield rich data sets that contain structural and morphological information of nanostructures. Exploiting data-driven methods to extract these insights is a natural fit. Additionally, the volume of data produced in the previous computational and simulation age of science has led to the establishment of extensive repositories of structures. These resources present opportunities for functional-property prediction to reveal novel uses for existing structures and for deep generative models to design new structures for a wide range of applications. This thesis is concerned with the development of machine learning (ML) algorithms for the characterization and generation of materials and nanostructures. Chapter 1 discusses characterization and generation in the data-driven age of science and reviews the application of ML to aid these processes, with a focus on SAS and EM for characterization. Chapter 2 provides a technical outline of ML in general, including the specific methods that are employed in subsequent chapters to develop models for processing SAS and EM data for characterization, as well as atomic structures for property prediction and generation. In Chapter 3, a convolutional neural network-based segmentation algorithm is developed to detect and locate nanoparticles in EM images. This constitutes the particle segmentation module of ImageDataExtractor, an open-source software tool developed therein for extracting information from EM images in an automated fashion. Techniques from Chapter 3 are employed in Chapter 4 to calculate SAS intensity functions from morphological and position information of nanostructures obtained from 2-dimensional EM images. In Chapter 5, the focus shifts to SAS, where a multi-task neural network is developed to concurrently identify the size and shape-parameters of the scatterers that produced a given SAS intensity. Chapter 6 constitutes the property prediction and structural generation portion of this thesis, in which a deep generative model of inorganic crystal structures is developed alongside a graph neural network to predict the properties of the generated structures. Finally, concluding remarks and avenues for future research are discussed in Chapter 7

Bayesian Particle Instance Segmentation for Electron Microscopy Image Quantification.

Automating the analysis portion of materials characterization by electron microscopy (EM) has the potential to accelerate the process of scientific discovery. To this end, we present a Bayesian deep-learning model for semantic segmentation and localization of particle instances in EM images. These segmentations can subsequently be used to compute quantitative measures such as particle-size distributions, radial- distribution functions, average sizes, and aspect ratios of the particles in an image. Moreover, by making use of the epistemic uncertainty of our model, we obtain uncertainty estimates of its outputs and use these to filter out false-positive predictions and hence produce more accurate quantitative measures. We incorporate our method into the ImageDataExtractor package, as ImageDataExtractor 2.0, which affords a full pipeline to automatically extract particle information for large-scale data-driven materials discovery. Finally, we present and make publicly available the Electron Microscopy Particle Segmentation (EMPS) data set. This is the first human-labeled particle instance segmentation data set, consisting of 465 EM images and their corresponding semantic instance segmentation maps

Analysis of the Relationship Between Tax Burden and Labor Force Participation Rates in OECD Countries

İşgücüne katılım oranı zaman içerisinde ülke ekonomileri için çok büyük önem arz etmeyebaşlamıştır. Özellikle Sanayi Devriminin gerçekleşmesiyle yaygınlaşan kitlesel üretim hammadelereolan talebi arttırmış ve dolayısıyla isgücüne olan ihtiyaç son derece artış göstermiştir. Dolayısıylaişgücüne katılım oranı ve onun belirleyicileri politika yapıcılar için önemle üzerinde durulan bir konuhaline gelmiştir. Bu çalışmanın amacı OECD ülkelerinde vergi yükünün işgücüne katılım oranı üzeri-ndeki etkisini tespit etmektir. Vergi yükü ve işgücüne katılım oranı arasındaki ilişkiyi tespit ede-bilmek için çalışma da literatür ile uyumlu 28 OECD ülkesi için 1990–2017 yıllarına ait makro verilerkullanılmıştır ve ülke seçimleri, verilerin ulaşılabilirliğine göre yapılmıştır. Analiz yöntemi olarakpanel veri analizi kullanılmış olup, otokorelasyon ve yatay kesit bağımlığı sorunlarının üstesindengelmek için GLS Period SUR modeli ve PCSE cross-sectional covariance metodu kullanılmıştır.Sonuçlar vergi yükü ile işgücüne katılım oranının ters orantılı olduğunu göstermekteİThe labor force participation rate has become a vital indicator for economies, especially after theindustrial revolution. Because of the mass production after the industrial revolution, the necessityof raw materials increased, which also brought a rise in labor demand. Thus, the labor force partici-pation rate and its determinants have become essential subjects for policymakers. The purposeof the study is to find out and evaluate the effect of the tax burden on labor force participationrate in Organisation for Economic Co-operation and Development (OECD) countries. The selectedcountries are determined in accordance with the availability of the data. Panel data estimationswere implemented to data set, and due to cross-sectional dependency and autocorrelation prob-lems, the GLS period SURestimation method and cross-sectional covariance methods were used.The findings revealed that tax burden has a negative and significant impact on the labor forceparticipation rate</p

The role of agricultural development cooperatives in establishing social capital

This paper examines a model of agricultural development specifically centered on the role of cooperatives in augmenting social capital. The aim of the study is to investigate the impact of agricultural cooperatives on social capital formation and improved livelihoods in eight villages in the Aegean Region of Turkey (Bademler, Bagarasi, Borezli, Godence, Karakuzu, Kizikli, Kuyumcu and Tire). We collected data using face-to-face semi-structured surveys. The results from frequency tables and social network analysis (SNA) support the hypothesis that membership in an agricultural development cooperative is a significant factor, which not only affects trust and augments social capital but also improves livelihoods in terms of income perception and eating habits

Calculating small-angle scattering intensity functions from electron-microscopy images.

We outline procedures to calculate small-angle scattering (SAS) intensity functions from 2-dimensional electron-microscopy (EM) images. Two types of scattering systems were considered: (a) the sample is a set of particles confined to a plane; or (b) the sample is modelled as parallel, infinitely long cylinders that extend into the image plane. In each case, an EM image is segmented into particle instances and the background, whereby coordinates and morphological parameters are computed and used to calculate the constituents of the SAS-intensity function. We compare our results with experimental SAS data, discuss limitations, both general and case specific, and outline some applications of this method which could potentially complement experimental SAS.BASF/Royal Academy of Engineering (Research Chair in Data Driven Molecular Engineering of Functional Materials) and the STFC via the ISIS Neutron and Muon Source