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Nearest and higher neighbor distances as well as bond length distributions (static and thermal) of the In_xGa_(1-x)As (0<x<1) semiconductor alloys have been obtained from high real-space resolution atomic pair distribution functions (PDFs). Using this structural information, we modeled the local atomic displacements in In_xGa_(1-x)As alloys. From a supercell model based on the Kirkwood potential, we obtained 3-D As and (In,Ga) ensemble averaged probability distributions. This clearly shows that As atom displacements are highly directional and can be represented as a combination of <100> and <111> displacements. Examination of the Kirkwood model indicates that the standard deviation (sigma) of the static disorder on the (In,Ga) sublattice is around 60% of the value on the As sublattice and the (In,Ga) atomic displacements are much more isotropic than those on the As sublattice. The single crystal diffuse scattering calculated from the Kirkwood model shows that atomic displacements are most strongly correlated along <110> directions.
High real-space resolution atomic pair distribution functions (PDF)s from the alloy series Ga_1-xIn_xAs have been obtained using high-energy x-ray diffraction. The first peak in the PDF is resolved as a doublet due to the presence of two nearest neig
We report a structural transition found in Ca10(Ir4As8)(Fe2-xIrxAs2)5, which exhibits superconductivity at 16 K. The c-axis parameter is doubled below a structural transition temperature of approximately 100 K, while the tetragonal symmetry with spac
The evolution of the optical phonons in layered semiconductor alloys SnSe1-xSx is studied as a function of the composition by using polarized Raman spectroscopy with six different excitation wavelengths (784.8, 632.8, 532, 514.5, 488, and 441.6 nm).
In the present paper we report an in-situ high-energy X-ray diffraction analysis of MgB2 tapes during the preparation process. The experiment was performed in a specifically designed furnace working in reducing atmosphere, compatible with the Laue di
We propose a method of neural evolution structures (NESs) combining artificial neural networks (ANNs) and evolutionary algorithms (EAs) to generate High Entropy Alloys (HEAs) structures. Our inverse design approach is based on pair distribution funct