Results 51 to 60 of about 12,987 (191)
Soft quasicrystals - Why are they stable?
, 2006 In the last two years we have witnessed the exciting experimental discovery of soft matter with nontrivial quasiperiodic long-range order - a new form of matter termed a soft quasicrystal.
Anderson PW +25 more
core +2 more sources
Automatic Determination of Quasicrystalline Patterns from Microscopy Images
Advanced Intelligent Discovery, EarlyView.This work introduces a user‐friendly machine learning tool to automatically extract and visualize quasicrystalline tiling patterns from atomically resolved microscopy images. It uses feature clustering, nearest‐neighbor analysis, and support vector machines. The method is broadly applicable to various quasicrystalline systems and is released as part of
Tano Kim Kender +2 more
wiley +1 more source
Quasicrystals - Discovery, Structure and Properties [PDF]
Kemija u Industriji, 2012 Quasicrystals are solid materials the structures of which can have crystallographically forbidden symmetries, e.g. of the fifth order, and do not have the translational invariance charactistic for classical crystals.
Stilinović, V., Brückler, F. M.
doaj
Self-assembly of two-dimensional binary quasicrystals: A possible route to a DNA quasicrystal
, 2016 We use Monte Carlo simulations and free-energy techniques to show that binary solutions of penta- and hexavalent two-dimensional patchy particles can form thermodynamically stable quasicrystals even at very narrow patch widths, provided their patch ...
Doye, Jonathan P. K. +3 more
core +1 more source
Symmetry of Magnetically Ordered Quasicrystals [PDF]
, 1998 The notion of magnetic symmetry is reexamined in light of the recent observation of long range magnetic order in icosahedral quasicrystals [Charrier et al., Phys. Rev. Lett. 78, 4637 (1997)].
B. Charrier +17 more
core +2 more sources
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
Advanced Intelligent Discovery, EarlyView.This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
Crystals, 2016 A rigorous method for obtaining the diffraction patterns of quasicrystals is presented. Diffraction patterns are an essential analytical tool in the study of quasicrystals, since they can be used to determine their photonic resonances.
Farhad A. Namin, Douglas H. Werner
doaj +1 more source
Generalized dynamics of moving dislocations in quasicrystals
, 2010 A theoretical framework for dislocation dynamics in quasicrystals is provided according to the continuum theory of dislocations. Firstly, we present the fundamental theory for moving dislocations in quasicrystals giving the dislocation density tensors ...
Agiasofitou, Eleni +2 more
core +1 more source
Algorithmic Design of Disordered Networks With Arbitrary Coordination: Application to Biophotonics
Advanced Functional Materials, Volume 36, Issue 38, 11 May 2026.Predictive Design of Disordered Networks: Disordered network‐like morphologies are abundant in nature, from cytoskeletal networks to bone structures and chalcogenide glasses. These structures are naturally hard to characterize. A new algorithmic tool extends the established Wooten–Weaire–Winer (WWW) algorithm to valencies above 4.
Florin Hemmann +3 more
wiley +1 more source
Journal of Non-Crystalline Solids, 1985 We introduce the concept of quasicrystals and discuss their diffraction patterns. The idea of local isomorphism is argued to be a physically meaningful one.
Dov Levine, Paul J. Steinhardt
openaire +2 more sources