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Phase transitions among Mg2SiO4 and its high-pressure polymorphs (wadsleyite and ringwoodite) are central to mantle dynamics and deep-mantle material cycling. However, the locations and Pressure-Temperature (P-T) dependences of these phase…

Geophysics · Physics 2026-02-03 Siyu Zhou , Daohong Liu , Chuanyu Zhang , Yu He , Xuben Wang , Xiaopan Zuo

The manufacture of solid oxide fuel cells (SOFCs) involves fabrication of a multilayer ceramic structure, for which constrained sintering is a key processing step in many cases. Defects are often observed in the sintered structure, but…

Materials Science · Physics 2015-02-09 Xin Wang , Zhangwei Chen , Alan Atkinson

Clustering is a fundamental technique in data analysis and machine learning, used to group similar data points together. Among various clustering methods, the Minimum Sum-of-Squares Clustering (MSSC) is one of the most widely used. MSSC…

Optimization and Control · Mathematics 2025-10-08 Anna Livia Croella , Veronica Piccialli , Antonio M. Sudoso

The mixed spinel, MgFe2O4 has been synthesized by ball-milling assisted sintering method. X-ray diffraction study confirms formation of cubic MgFe2O4 and the lattice parameter values calculated are a = b = c = 8.369(3) {\AA}. Vibrating…

Materials Science · Physics 2018-10-01 B. Santhosh Kumar , N. Praveen Shankar , N. Aparna devi , C. Venkateswaran

Aluminum-Copper (Al-Cu) alloys are essential materials for weight reduction critical structures in the aerospace and automotive industries, yet achieving their maximum ultrahigh-strength potential remains limited by nanoscale defect control…

Materials Science · Physics 2026-01-05 Amirhossein Abedini , Behzad Mehrafrooz , Iyad Alabd Alhafez , Arash Kardani

Four-dimensional scanning transmission electron microscopy (4D-STEM) enables mapping of diffraction information with nanometer-scale spatial resolution, offering detailed insight into local structure, orientation, and strain. However, as…

Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling are finding widespread use in applied statistics and machine learning. These often lead to difficult computational problems, which are increasingly being solved on parallel and…

Machine Learning · Statistics 2018-06-05 Alexander Terenin , Eric P. Xing

Crystallization is a fundamental process in materials science, providing the primary route for the realization of a wide range of new materials. Crystallization rates are also considered to be useful probes of glass-forming ability. At the…

Solids with spatial variations in the crystalline axes naturally evolve into cells or grains separated by sharp walls. Such variations are mathematically described using the Nye dislocation density tensor. At high temperatures,…

Materials Science · Physics 2009-11-11 Surachate Limkumnerd , James P. Sethna

The resent progress in the technology of transparent ceramics extends the application of CW and pulsed lasers. The parameters of the transparent ceramics are comparable with the single crystals both for active elements (Nd:YAG, Yb:YAG,…

Materials Science · Physics 2025-01-17 Mykhailo Chaika

Axial segregation of a binary mixture of grains in a rotating drum is studied using Molecular Dynamics (MD) simulations. A force scheme leading to a constant restitution coefficient is used and shows that axial segregation is possible…

Other Condensed Matter · Physics 2009-11-10 Nicolas Taberlet , Wolfgang Losert , Patrick Richard

We present temperature-dependent x-ray diffraction and temperature- and field-dependent Raman scattering studies of single crystal Mn3O4, which reveal the novel magnetostructural phases that evolve in the spinels due to the interplay…

Strongly Correlated Electrons · Physics 2015-05-14 M. Kim , X. M. Chen , E. Fradkin , P. Abbamonte , S. L. Cooper

Microwave energy can be advantageously used for materials processing as it provides high heating rates and homogeneous temperature field distribution. These features are partly due to the large microwave penetration depth into dielectric…

We present a new sink particle algorithm developed for the Adaptive Mesh Refinement code RAMSES. Our main addition is the use of a clump finder to identify density peaks and their associated regions (the peak patches). This allows us to…

Solar and Stellar Astrophysics · Physics 2015-06-23 Andreas Bleuler , Romain Teyssier

Approximate Markov chain Monte Carlo (MCMC) offers the promise of more rapid sampling at the cost of more biased inference. Since standard MCMC diagnostics fail to detect these biases, researchers have developed computable Stein discrepancy…

Machine Learning · Statistics 2020-10-16 Jackson Gorham , Lester Mackey

The material properties of polycrystals are strongly affected by the evolution and coarsening of their internal grain structures. Yet, studying this process is challenging due to the complex interactions within grain boundary networks.…

In this work, we introduce a simple modification of the Monte Carlo algorithm, which we call step Monte Carlo (sMC). The sMC approach allows to simulate processes far from equilibrium and obtain information about the dynamic properties of…

Other Condensed Matter · Physics 2023-12-15 Dariusz Sztenkiel

Sequential Monte Carlo (SMC) methods are a class of techniques to sample approximately from any sequence of probability distributions using a combination of importance sampling and resampling steps. This paper is concerned with the…

Statistics Theory · Mathematics 2012-03-05 Pierre Del Moral , Arnaud Doucet , Ajay Jasra

Hypotheses: A quantitative molecular-thermodynamic theory of the growth of giant wormlike micelles of nonionic surfactants can be developed on the basis of a generalized model, which includes the classical 'phase separation' and 'mass…

Abnormal grain growth in the presence of second phase particles is investigated with the help of a two-dimensional Monte Carlo simulation. An aggregate of equiaxed grains is considered with constant grain boundary energy and mobility. The…

Materials Science · Physics 2007-05-23 Rene Messina , Michele Soucail , Ladislas Kubin
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