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Related papers: Quantitative three-dimensional local order analysi…

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The complexity of condensed matter arises from emergent behaviors that cannot be understood by analyzing individual constituents in isolation. While traditional condensed-matter approaches-developed primarily for ideal crystalline…

Materials Science · Physics 2025-09-30 Elisabetta Nocerino

The exceptional mechanical strengths of medium and high-entropy alloys have been attributed to hardening in random solid solutions. Here, we evidence non-random chemical mixings in CrCoNi alloys, resulting from short range ordering. A novel…

Materials Science · Physics 2022-06-07 H. W. Hsiao , R. Feng , H. Ni , K. An , J. D. Poplawsky , P. K. Liaw , J. M. Zuo

We present a new variational model for computing the electronic first-order density matrix of a crystalline material in presence of a local defect. A natural way to obtain variational discretizations of this model is to expand the…

Other Condensed Matter · Physics 2009-11-13 Eric Cances , Amelie Deleurence , Mathieu Lewin

Local, or short-range, order in disordered alloys is an important and exciting phenomenon which is quantified in electron, X-ray and neutron scattering experiments. It is discussed in many excellent reviews and books, as well as in the…

Statistical Mechanics · Physics 2007-05-23 Igor Tsatskis

Obtaining conductance spectra for a concentration of disordered impurities distributed over a nanoscale device with sensing capabilities is a well-defined problem. However, to do this inversely, i.e., extracting information about the…

Mesoscale and Nanoscale Physics · Physics 2021-12-01 S. Mukim , J. O'Brien , M. Abarashi , M. S. Ferreira , C. G. Rocha

The phase coexistence of chemically ordered L1_0 and chemically disordered structures within binary alloys is investigated, using the NiMn system as an example. Theoretical and numerical predictions of the signatures one might expect in…

Materials Science · Physics 2007-05-23 Rafael C. Howell , Steven D. Conradson , Angel J. Garcia-Adeva

The large amount of powder diffraction data for which the corresponding crystal structures have not yet been identified suggests the existence of numerous undiscovered, physically relevant crystal structure prototypes. In this paper, we…

Materials Science · Physics 2024-10-31 Abhijith S. Parackal , Rhys E. A. Goodall , Felix A. Faber , Rickard Armiento

In metal nanoparticles (NPs) supracrystals, the metallic core provides some key properties, e.g. magnetization, plasmonic response or conductivity, with the ligand molecules giving rise to others like solubility, assembly or interaction…

Disordered nanostructures are commonly encountered in many nanophotonic systems, from colloid dispersions for sensing, to heterostructured photocatalysts. Randomness, however, imposes severe challenges for nanophotonics modeling, often…

Diffraction is the most common method to solve for unknown or partially known crystal structures. However, it remains a challenge to determine the crystal structure of a new material that may have nanoscale size or heterogeneities. Here we…

The diffusion of electronic wave packets in one-dimensional systems with on-site, binary disorder is numerically investigated within the framework of a single-band tight-binding model. Fractal properties are incorporated by assuming that…

Disordered Systems and Neural Networks · Physics 2008-07-07 P. R. Wells , J. d'Albuquerque e Castro , S. L. A. de Queiroz

Disordered systems like liquids, gels, glasses, or granular materials are not only ubiquitous in daily life and in industrial applications but they are also crucial for the mechanical stability of cells or the transport of chemical and…

Disordered Systems and Neural Networks · Physics 2020-07-06 Zhen Zhang , Walter Kob

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…

The study of energy transport properties in heterogeneous materials has attracted scientific interest for more than a century, and it continues to offer fundamental and rich questions. One of the unanswered challenges is to extend Anderson…

A major challenge in materials science is the determination of the structure of nanometer sized objects. Here we present a novel approach that uses a generative machine learning model based on diffusion processes that is trained on 45,229…

Computational Physics · Physics 2024-11-01 Gabe Guo , Tristan Saidi , Maxwell Terban , Michele Valsecchi , Simon JL Billinge , Hod Lipson

The recently developed information-theoretic approach to crystallographic symmetry classifications and quantifications in two dimensions (2D) from digital transmission electron and scanning probe microscope images is adapted for the…

Materials Science · Physics 2022-08-10 Peter Moeck , Lukas von Koch

The ability to rapidly develop materials with desired properties has a transformative impact on a broad range of emerging technologies. In this work, we introduce a new framework based on the diffusion model, a recent generative machine…

High entropy oxides (HEOs) are a rapidly growing class of compositionally complex ceramics in which configurational disorder is engineered to unlock novel functionality. While average crystallographic symmetry is often retained, local…

Many crystalline materials show chemical short range order and relaxation of neighboring atoms. Local structural information can be obtained by analyzing the atomic pair distribution function (PDF) obtained from powder diffraction data. In…

Materials Science · Physics 2007-05-23 Th. Proffen , V. Petkov , S. J. L. Billinge , T. Vogt

Solving crystal structures from kinematical X-ray or electron diffraction patterns of single crystals requires many more diffracted beams to be recorded than there are atoms in the structure, since the phases of the structure factors can…

Materials Science · Physics 2023-02-13 Christoph T. Koch