scholarly journals Monte Carlo analysis of neutron diffuse scattering data

2006 ◽  
Vol 385-386 ◽  
pp. 1352-1354 ◽  
Author(s):  
D.J. Goossens ◽  
A.P. Heerdegen ◽  
T.R. Welberry ◽  
M.J. Gutmann
Keyword(s):  
Monte Carlo ◽  
Diffuse Scattering ◽  
Monte Carlo Analysis ◽  
Scattering Data

scholarly journals rmc-discord: reverse Monte Carlo refinement of diffuse scattering and correlated disorder from single crystals

2021 ◽  
Vol 54 (6) ◽  
Author(s):  
Zachary J. Morgan ◽  
Haidong D. Zhou ◽  
Bryan C. Chakoumakos ◽  
Feng Ye
Keyword(s):  
Monte Carlo ◽  
Single Crystals ◽  
Diffuse Scattering ◽  
Electrostatic Interactions ◽  
Three Dimensional ◽  
Real Space ◽  
Scattering Data ◽  
Reverse Monte Carlo ◽  
Pair Correlations ◽  
Spin Pair

A user-friendly program has been developed to analyze diffuse scattering from single crystals with the reverse Monte Carlo method. The approach allows for refinement of correlated disorder from atomistic supercells with magnetic or structural (occupational and/or displacive) disorder. The program is written in Python and optimized for performance and efficiency. Refinements of two user cases obtained with legacy neutron-scattering data demonstrate the effectiveness of the approach and the developed program. It is shown with bixbyite, a naturally occurring magnetic mineral, that the calculated three-dimensional spin-pair correlations are resolved with finer real-space resolution compared with the pair distribution function calculated directly from the reciprocal-space pattern. With the triangular lattice Ba3Co2O6(CO3)0.7, refinements of occupational and displacive disorder are combined to extract the one-dimensional intra-chain correlations of carbonate molecules that move toward neighboring vacant sites to accommodate strain induced by electrostatic interactions. The program is packaged with a graphical user interface and extensible to serve the needs of single-crystal diffractometer instruments that collect diffuse-scattering data.

Monte Carlo Simulations of Phase Transformations in Fe-Cr Alloys

1992 ◽  
Vol 291 ◽  
Author(s):  
L. Reinhard ◽  
P. E. A. Turchi
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Phase Transformations ◽  
Phase Stability ◽  
First Principles ◽  
Diffuse Scattering ◽  
Scattering Data ◽  
Stability Properties ◽  
Energy Parameters ◽  
Random State

ABSTRACTPhase stability properties of bcc based Fe-Cr alloys are examined in the framework of the first-principles KKR-CPA-GPM formalism and Monte Carlo simulations. For Fe-rich alloys, ordered configurations are found stable with respect to the random state of the alloy, but unstable with respect to the pure Fe and Cr metals. The results are compared with the ones obtained by using energy parameters extracted from experimental diffuse scattering data.

Simulation Study of Strain-Related Morphology in YBa2Cu3−x(Al,Fe)xO7

1992 ◽  
Vol 291 ◽  
Author(s):  
Zhi-Xiong Cai ◽  
Yimei Zhu ◽  
David O. Welch
Keyword(s):  
Monte Carlo ◽  
Oxygen Concentration ◽  
Diffuse Scattering ◽  
Lattice Gas ◽  
Lattice Gas Model ◽  
Scattering Data ◽  
Concentration Wave ◽  
Scattering Intensity ◽  
Impurity Atoms ◽  
Diffuse Scattering Intensity

ABSTRACTA new simulation method which combines the merits of Monte Carlo simulation of a lattice gas model and the continuum elasticity theory is described. This method treats the elastic strain energy due to concentration fluctuation of interstitial as a perturbation of a lattice gas model Hamiltonian. We illustrate this method by calculating the diffuse scattering intensity of YBa2Cu3O7 systems doped with trivalent impurity atoms M such as Fe or Al. The oxygen concentration wave amplitudes cq were obtained from Monte Carlo simulations of an anisotropic lattice gas model which represents well the interaction between oxygen atoms in this system. These results are in turn used to calculate the diffuse X-ray scattering intensity caused by the displacement field using concentration wave/displacement wave approach. The results suggest that the small orthorhombic domains associated with the oxygen “cross-link” around impurity atoms M causes the diffuse scattering intensity to fall off with oxygen concentration wave vector q as 1/q2for small qand as 1/q4 for larger q.We also show that the size of such domain can be obtained from diffuse scattering data.

A refinement strategy for Monte Carlo modelling of diffuse scattering from molecular crystal systems

2010 ◽  
Vol 43 (4) ◽  
pp. 913-915 ◽  
Author(s):  
E. J. Chan ◽  
T. R. Welberry ◽  
D. J. Goossens ◽  
A. P. Heerdegen
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Empirical Formula ◽  
Molecular Crystal ◽  
Diffuse Scattering ◽  
Molecular Crystals ◽  
Scattering Data ◽  
New Strategy ◽  
Refinement Strategy ◽  
Spring Constants

A new strategy for modelling diffuse scattering from molecular crystals using Monte Carlo simulation is described. The use of harmonic (Hooke's law) springs to representeffectiveintermolecular interactions is preserved, in order to minimize computer requirements, but use is now made of a simple empirical formula to specify spring constants and provide an objective means for limiting the number of springs needed to be used. The method has been tested on diffuse scattering data obtained for form I of paracetamol.

scholarly journals Ligand-Induced Conformational Changes in Tissue Transglutaminase: Monte Carlo Analysis of Small-Angle Scattering Data

2000 ◽  
Vol 78 (6) ◽  
pp. 3240-3251 ◽  
Author(s):  
Paolo Mariani ◽  
Flavio Carsughi ◽  
Francesco Spinozzi ◽  
Sandro Romanzetti ◽  
Gerd Meier ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Conformational Changes ◽  
Small Angle ◽  
Tissue Transglutaminase ◽  
Monte Carlo Analysis ◽  
Small Angle Scattering ◽  
Scattering Data ◽  
Angle Scattering

Generation of Correlated Logistic-Normal Random Variates for Medical Decision Trees

1998 ◽  
Vol 37 (03) ◽  
pp. 235-238 ◽  
Author(s):  
M. El-Taha ◽  
D. E. Clark
Keyword(s):  
Monte Carlo ◽  
Decision Trees ◽  
Computer Algebra ◽  
Taylor Series ◽  
Computer Algebra System ◽  
Random Variable ◽  
Monte Carlo Analysis ◽  
Normal Random Variable ◽  
Medical Decision ◽  
Theoretical Results

AbstractA Logistic-Normal random variable (Y) is obtained from a Normal random variable (X) by the relation Y = (ex)/(1 + ex). In Monte-Carlo analysis of decision trees, Logistic-Normal random variates may be used to model the branching probabilities. In some cases, the probabilities to be modeled may not be independent, and a method for generating correlated Logistic-Normal random variates would be useful. A technique for generating correlated Normal random variates has been previously described. Using Taylor Series approximations and the algebraic definitions of variance and covariance, we describe methods for estimating the means, variances, and covariances of Normal random variates which, after translation using the above formula, will result in Logistic-Normal random variates having approximately the desired means, variances, and covariances. Multiple simulations of the method using the Mathematica computer algebra system show satisfactory agreement with the theoretical results.

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