Search results for "Disordered material"

showing 2 items of 2 documents

Inverse simulated annealing: Improvements and application to amorphous InSb

2014

An improved inverse simulated annealing method is presented to determine the structure of complex disordered systems from first principles in agreement with available experimental data or desired predetermined target properties. The effectiveness of this method is demonstrated by revisiting the structure of amorphous InSb. The resulting network is mostly tetrahedral and in excellent agreement with available experimental data.

Materials scienceGeneral Computer ScienceGeneral Physics and AstronomyInverseFOS: Physical sciencesDisordered material02 engineering and technology01 natural sciencesMolecular physicsSimulated annealingCondensed Matter::Materials Science0103 physical sciencesGeneral Materials Science010306 general physicsStructure determinationFIS/03 - FISICA DELLA MATERIAQuenchingCondensed Matter - Materials ScienceInverse designExperimental dataMaterials Science (cond-mat.mtrl-sci)General ChemistryDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksComputational Physics (physics.comp-ph)021001 nanoscience & nanotechnologyAmorphous solidComputational MathematicsMechanics of MaterialsSimulated annealingTetrahedron0210 nano-technologyPhysics - Computational Physics
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CLEASE: a versatile and user-friendly implementation of cluster expansion method

2018

Materials exhibiting a substitutional disorder such as multicomponent alloys and mixed metal oxides/oxyfluorides are of great importance in many scientific and technological sectors. Disordered materials constitute an overwhelmingly large configurational space, which makes it practically impossible to be explored manually using first-principles calculations such as density functional theory due to the high computational costs. Consequently, the use of methods such as cluster expansion (CE) is vital in enhancing our understanding of the disordered materials. CE dramatically reduces the computational cost by mapping the first-principles calculation results on to a Hamiltonian which is much fa…

Materials sciencetilastomenetelmätFOS: Physical sciencesBinary number02 engineering and technology114 Physical sciences01 natural sciencesComputational sciencesymbols.namesake0103 physical sciencesAlloysbattery materialGeneral Materials Sciencemetalliseoksetmateriaalitiede010306 general physicsMonte CarloCondensed Matter - Materials ScienceUser FriendlyMixed metalMaterials Science (cond-mat.mtrl-sci)disordered materials021001 nanoscience & nanotechnologyCondensed Matter Physicscluster expansionComplex materialsMonte Carlo -menetelmätRegularization (physics)symbolsDensity functional theory0210 nano-technologyHamiltonian (quantum mechanics)Cluster expansionJournal of Physics: Condensed Matter
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