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Machine learning has demonstrated remarkable promise for solving the trajectory generation problem and in paving the way for online use of trajectory optimization for resource-constrained spacecraft. However, a key shortcoming in current…

Robotics · Computer Science 2025-01-03 Julia Briden , Breanna Johnson , Richard Linares , Abhishek Cauligi

Solid solution is an important way to enhance the structural and functional performances of materials. In this work, we develop a structural modeling approach to solid solutions based on the similar atomic environment (SAE). We propose the…

Computational Physics · Physics 2020-05-18 Fuyang Tian , De-Ye Lin , Xingyu Gao , Ya-Fan Zhao , Hai-Feng Song

A method is suggested for estimation of structural properties of amorphous fullerene and its derivatives produced by vacuum annealing. The method is based on the fitting of the neutron or x-ray powder diffraction patterns for scattering…

Materials Science · Physics 2014-12-24 V. S. Neverov , P. A. Borisova , A. B. Kukushkin , V. V. Voloshinov

Modern technology for producing extremely bright and coherent X-ray laser pulses provides the possibility to acquire a large number of diffraction patterns from individual biological nanoparticles, including proteins, viruses, and DNA.…

Methodology · Statistics 2018-07-11 Stefan Engblom , Carl Nettelblad , Jing Liu

Leveraging strong optoelectronic responses to external stimuli, such as temperature and electric fields, is central to the development of advanced photonic technologies, including adaptive photodetectors and reconfigurable photovoltaic…

Materials Science · Physics 2026-02-25 Pol Benítez , Cibrán López , Edgardo Saucedo , Claudio Cazorla

In the previous paper of this series [D. P. Varn, G. S. Canright, and J. P. Crutchfield, Physical Review B, submitted] we detailed a procedure--epsilon-machine spectral reconstruction--to discover and analyze patterns and disorder in…

Materials Science · Physics 2007-05-23 D. P. Varn , G. S. Canright , J. P. Crutchfield

Chemical disorder, originating from the mixed occupation of crystallographic sites by multiple elements, is widespread in alloys, ceramics, and compositionally complex materials, where short- and long-range orderings can strongly influence…

Materials Science · Physics 2026-05-20 Jiayu Peng , Peichen Zhong

Machine Learning (ML) techniques are revolutionizing the way to perform efficient materials modeling. Nevertheless, not all the ML approaches allow for the understanding of microscopic mechanisms at play in different phenomena. To address…

Materials Science · Physics 2022-06-22 Udaykumar Gajera , Loriano Storchi , Danila Amoroso , Francesco Delodovici , Silvia Picozzi

What determines whether a molecular property prediction model organizes its representations so that geometric and compositional information can be cleanly separated? We introduce Compositional Probe Decomposition (CPD), which linearly…

Machine Learning · Computer Science 2026-03-10 Joshua Steier

An ideal atomistic model of a disordered material should contradict no experiments,and should also be consistent with accurate force fields (either {\it ab initio}or empirical). We make significant progress toward jointly satisfying {\it…

Materials Science · Physics 2016-08-31 Parthapratim Biswas , De Nyago Tafen , D. A. Drabold

We present a detailed study of the local atomic and magnetic structure of the type-I multiferroic perovskite system (Sr,Ba)(Mn,Ti)O$_3$ using x-ray and neutron pair distribution function (PDF) analysis, polarized neutron scattering, and…

Amorphous materials are coming within reach of realistic computer simulations, but new approaches are needed to fully understand their intricate atomic structures. Here, we show how machine-learning (ML)-based techniques can give new,…

Many materials contain extended defects of nanosize scale, such as dislocations, cracks, pores, polymorphic inclusions, and other embryos of competing phases. When one is interested not in the precise internal structure of a sample with…

Statistical Mechanics · Physics 2022-01-27 V. I. Yukalov , E. P. Yukalova

We report on a new algorithm for detection of crystallographic information in 3D, as retained in Atom Probe Tomography (APT), with improved robustness and signal detection performance. The algorithm is underpinned by 1D distribution…

Materials Science · Physics 2019-05-22 Daniel Haley , Paul A. J. Bagot , Michael P. Moody

As molecular dynamics is increasingly used to characterize non-crystalline materials, it is crucial to verify that the numerical model is accurate enough, consistent with experimental data and can be used to extract various characteristics…

Disordered Systems and Neural Networks · Physics 2008-11-17 F. Gaspari , I. M. Kupchak , A. I. Shkrebtii , J. M. Perz

Disordered molecular systems such as amorphous catalysts, organic thin films, electrolyte solutions, and water are at the cutting edge of computational exploration today. Traditional simulations of such systems at length-scales relevant to…

Mesoscale and Nanoscale Physics · Physics 2023-09-06 Ata Madanchi , Michael Kilgour , Frederik Zysk , Thomas D. Kühne , Lena Simine

The effective, fast transport of matter through porous media is often characterized by complex dispersion effects. To describe in mathematical terms such situations, instead of a simple macroscopic equation (as in the classical Darcy's…

Numerical Analysis · Mathematics 2025-05-06 Surendra Nepal , Vishnu Raveendran , Michael Eden , Rainey Lyons , Adrian Muntean

Nuclear Magnetic Resonance (NMR) spectroscopy is a central characterization method for molecular structure elucidation, yet interpreting NMR spectra to deduce molecular structures remains challenging due to the complexity of spectral data…

Chemical Physics · Physics 2025-07-15 Qingsong Yang , Binglan Wu , Xuwei Liu , Bo Chen , Wei Li , Gen Long , Xin Chen , Mingjun Xiao

Physical systems are frequently modeled as sets of points in space, each representing the position of an atom, molecule, or mesoscale particle. As many properties of such systems depend on the underlying ordering of their constituent…

Other Condensed Matter · Physics 2015-11-18 Emanuel A. Lazar , Jian Han , David J. Srolovitz

The atomic pair distribution functions (PDF) of four powder samples of YBa2Cu3O6+x (x=0.25, 0.45, 0.65, 0.94) at 15 K have been measured by means of pulsed neutron diffraction. The PDF is modelled using a full-profile fitting approach to…

Superconductivity · Physics 2009-10-31 M. Gutmann , S. J. L. Billinge , E. L. Brosha , G. H. Kwei
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