Determination of domain distribution by analysis of LEED beam profiles

Kein Inhaltsverzeichnis

Diffuse LEED intensities of disordered crystal surfaces : II. Multiple scattering on disordered overlayers

The diffraction of low energy electrons from disordered overlayers adsorbed on ordered substrates is treated theoretically by an extension of Beeby's multiple scattering method. A lattice gas model is assumed for the disordered adsorbate layer. Multiple scattering within a certain area around each atom — each atom of the overlayer and within the ordered substrate — is treated self-consistently, the remaining contributions to the total scattering amplitude being averaged. The theory can be used in the limiting cases of random distribution and of long range order within the adsorbate layer

Diffuse LEED intensities of disordered crystal surfaces : IV. Application of the disorder theory

The principles of the statistical disorder theory are discussed briefly. The theory is applied to a model of the disordered (101)Au surface with the characteristic (1 × 2) supersstructure. A fit procedure is described, by which the experimental angular intensity profiles are used directly to determine the disorder parameters and the interaction energies between the chains of surface atoms

Diffuse LEED intensities of disordered crystal surfaces : I. Correlations between statistics and multiple diffraction

It is shown that the diffraction of slow electrons from disordered crystal surfaces is correlated with the problem of thermodynamical statistics. The correlation functions are completely determined by the self-energies and interaction energies of neighboring complexes. These quantities solve the problem of a-priori probabilities and the cooperative phenomenon of correlation functions of these complexes. If the calculation of a certain set of multiple scattering amplitudes is possible, the remaining problem of determining the diffuse LEED pattern becomes solvable. The calculation of angular beam profiles follows the same lines as already described for the kinematic theory of X-ray diffraction

Diffuse LEED intensities of disordered crystal surfaces : III. LEED investigation of the disordered (110) surface of gold

The LEED pattern of clean (101) surfaces of Au show a characteristic (1 × 2) superstructure. The diffuseness of reflections in the reciprocal [010] direction is caused by one-dimensional disorder of chains, strictly ordered into spatial [10 ] direction. There is a transition from this disordered superstructure to the normal (1 × 1) structure at 420 + 15°C. The angular profiles of the and (01) beam are measured at various temperatures and with constant energy and angles of incidence of the primary beam. The beam profiles are deconvoluted approximately with the instrument response function

Learning Mixtures of Gaussians in High Dimensions

Efficiently learning mixture of Gaussians is a fundamental problem in statistics and learning theory. Given samples coming from a random one out of k Gaussian distributions in Rn, the learning problem asks to estimate the means and the covariance matrices of these Gaussians. This learning problem arises in many areas ranging from the natural sciences to the social sciences, and has also found many machine learning applications. Unfortunately, learning mixture of Gaussians is an information theoretically hard problem: in order to learn the parameters up to a reasonable accuracy, the number of samples required is exponential in the number of Gaussian components in the worst case. In this work, we show that provided we are in high enough dimensions, the class of Gaussian mixtures is learnable in its most general form under a smoothed analysis framework, where the parameters are randomly perturbed from an adversarial starting point. In particular, given samples from a mixture of Gaussians with randomly perturbed parameters, when n > {\Omega}(k^2), we give an algorithm that learns the parameters with polynomial running time and using polynomial number of samples. The central algorithmic ideas consist of new ways to decompose the moment tensor of the Gaussian mixture by exploiting its structural properties. The symmetries of this tensor are derived from the combinatorial structure of higher order moments of Gaussian distributions (sometimes referred to as Isserlis' theorem or Wick's theorem). We also develop new tools for bounding smallest singular values of structured random matrices, which could be useful in other smoothed analysis settings

Quasars, pulsars, black holes and HEAO's

Astronomical surveys are discussed by large X-ray, gamma ray, and cosmic ray instruments carried onboard high-energy astronomy observatories. Quasars, pulsars, black holes, and the ultimate benefits of the new astronomy are briefly discussed

Multilayer distortion in the reconstructed (110) surface of Au

A new LEED intensity analysis of the reconstructed Au(110)-(1×2) surface results in a modification of the missing row model with considerable distortions which are at least three layers deep. The top layer spacing is contracted by about 20%, the second layer exhibits a lateral pairing displacement of 0.07 Å and the third layer is buckled by 0.24 Å. Distortions in deeper layers seem to be probable but have not been considered in this analysis. The inter-atomic distances in the distorted surface region show both an expansion and a contraction compared to the bulk value and range from 5% contraction to about 4% expansion

Neural-Network Quantum States, String-Bond States, and Chiral Topological States

Neural-Network Quantum States have been recently introduced as an Ansatz for describing the wave function of quantum many-body systems. We show that there are strong connections between Neural-Network Quantum States in the form of Restricted Boltzmann Machines and some classes of Tensor-Network states in arbitrary dimensions. In particular we demonstrate that short-range Restricted Boltzmann Machines are Entangled Plaquette States, while fully connected Restricted Boltzmann Machines are String-Bond States with a nonlocal geometry and low bond dimension. These results shed light on the underlying architecture of Restricted Boltzmann Machines and their efficiency at representing many-body quantum states. String-Bond States also provide a generic way of enhancing the power of Neural-Network Quantum States and a natural generalization to systems with larger local Hilbert space. We compare the advantages and drawbacks of these different classes of states and present a method to combine them together. This allows us to benefit from both the entanglement structure of Tensor Networks and the efficiency of Neural-Network Quantum States into a single Ansatz capable of targeting the wave function of strongly correlated systems. While it remains a challenge to describe states with chiral topological order using traditional Tensor Networks, we show that Neural-Network Quantum States and their String-Bond States extension can describe a lattice Fractional Quantum Hall state exactly. In addition, we provide numerical evidence that Neural-Network Quantum States can approximate a chiral spin liquid with better accuracy than Entangled Plaquette States and local String-Bond States. Our results demonstrate the efficiency of neural networks to describe complex quantum wave functions and pave the way towards the use of String-Bond States as a tool in more traditional machine-learning applications.Comment: 15 pages, 7 figure