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Amorphous silicon (a-Si) models are analyzed for structural, electronic and vibrational characteristics. Several models of various sizes have been computationally fabricated for this analysis. It is shown that a recently developed…

Disordered Systems and Neural Networks · Physics 2018-11-29 Dale Igram , Bishal Bhattarai , Parthapratim Biswas , D. A. Drabold

The general and practical inversion of diffraction data-producing a computer model correctly representing the material explored - is an important unsolved problem for disordered materials. Such modeling should proceed by using our full…

Materials Science · Physics 2016-07-05 Anup Pandey , Parthapratim Biswas , David A. Drabold

Amorphous silicon (a-Si) is a widely studied non-crystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural models of a-Si can be obtained by harnessing the power of…

In this paper we infer the structure of Pd40Ni40P20 from experimental diffraction data and ab initio interactions using Force Enhanced Atomic Refinement (FEAR). Our model accurately reproduces known experimental signatures of the system and…

Materials Science · Physics 2019-08-22 Bishal Bhattarai , Rajendra Thapa , David A. Drabold

This paper addresses a difficult inverse problem that involves the reconstruction of a three-dimensional model of tetrahedral amorphous semiconductors via inversion of diffraction data. By posing the material-structure determination as a…

Disordered Systems and Neural Networks · Physics 2019-12-06 Dil K. Limbu , Stephen R. Elliott , Raymond Atta-Fynn , Parthapratim Biswas

Although experiments can offer some fingerprints of the atomic structure of glasses (coordination numbers, pair distribution function, etc.), atomistic simulations are often required to directly access the structure itself (i.e., the…

Materials Science · Physics 2020-03-18 Qi Zhou , Tao Du , Lijie Guo , Morten M. Smedskjaer , Mathieu Bauchy

Localized basis ab initio molecular dynamics simulation within the density functional framework has been used to generate realistic configurations of amorphous silicon carbide (a-SiC). Our approach consists of constructing a set of smart…

Disordered Systems and Neural Networks · Physics 2015-05-13 Raymond Atta-Fynn , Parthapratim Biswas

Ideal models of complex materials must satisfy all available information about the system. Generally, this information consists of experimental data, information implicit to sophisticated interatomic interactions and potentially other {\it…

Disordered Systems and Neural Networks · Physics 2009-11-13 Parthapratim Biswas , Raymond Atta-Fynn , S. Chakraborty , D. A. Drabold

Hydrogenation of amorphous silicon (a-Si:H) is critical for reducing defect densities, passivating mid-gap states and surfaces, and improving photoconductivity in silicon-based electro-optical devices. Modelling the atomic scale structure…

Using molecular dynamics (MD) simulation, we investigate the mechanical response of silicon to high dose ion-irradiation. We employ a realistic and efficient model to directly simulate ion beam induced amorphization. Structural properties…

Materials Science · Physics 2009-10-31 Keith M. Beardmore , Niels Gronbech-Jensen

We have investigated the response of amorphous silicon (a-Si), in particular crystallization, to external mechanical shear deformations using classical molecular dynamics (MD) simulations and the empirical Environment Dependent Inter-atomic…

Materials Science · Physics 2015-05-28 Ali Kerrache , Normand Mousseau , Laurent J. Lewis

Understanding the structural origins of the properties of amorphous materials remains one of the most important challenges in structural science. In this study we demonstrate that local 'structural simplicity', embodied by the degree to…

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,…

The technique known as 4D-STEM has recently emerged as a powerful tool for the local characterization of crystalline structures in materials, such as cathode materials for Li-ion batteries or perovskite materials for photovoltaics. However,…

The structural and electronic properties of amorphous silicon ($a$-Si) are investigated by first-principles calculations based on the density-functional theory (DFT), focusing on the intrinsic structural defects. By simulated melting and…

Materials Science · Physics 2017-10-11 Yoritaka Furukawa , Yu-ichiro Matsushita

In this work We report on the extensive characterization of amorphous silicon carbide (a-SiC) coatings prepared by physical deposition methods. We compare the results obtained on two different sputtering systems (a standard RF magnetron…

In coherent X-ray diffraction microscopy the diffraction pattern generated by a sample illuminated with coherent x-rays is recorded, and a computer algorithm recovers the unmeasured phases to synthesize an image. By avoiding the use of a…

The rapid advancement of diffusion models has significantly improved high-quality image generation, making generated content increasingly challenging to distinguish from real images and raising concerns about potential misuse. In this…

Computer Vision and Pattern Recognition · Computer Science 2025-11-04 Beilin Chu , Xuan Xu , Xin Wang , Yufei Zhang , Weike You , Linna Zhou

Ab initio simulations are capable of providing detailed information of material behavior at the nanoscale. Simulating experimentally relevant situations is, however, often computationally intense. Using hybrid approaches between ab initio…

Computational Physics · Physics 2019-03-26 Michael Sluydts , Michiel Larmuseau , Johan Lauwaert , Stefaan Cottenier

Generative models show great promise for the inverse design of molecules and inorganic crystals, but remain largely ineffective within more complex structures such as amorphous materials. Here, we present a diffusion model that reliably…

Disordered Systems and Neural Networks · Physics 2026-01-21 Kai Yang , Daniel Schwalbe-Koda
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