Large and realistic models of amorphous silicon

Igram, Dale J.; Bhattarai, Bishal; Biswas, Parthapratim; Drabold, D. A.

doi:10.1016/j.jnoncrysol.2018.04.011

Cited by 23 publications

(16 citation statements)

References 50 publications

(62 reference statements)

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“…This similarity is independent of the size of the a-Si simulation cell employed. We attribute it to the similarity of the local structure in a-Si and c-Si in bond distances and angles [24].…”

Section: Amorphous Simentioning

confidence: 99%

Band unfolding made simple

Mayo

Ynduráin

Soler

2020

J. Phys.: Condens. Matter

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We present a simple view on band unfolding of the energy bands obtained from supercell calculations. It relies on the relationship between the local density of states in reciprocal space (qLDOS) and the fully unfolded band structure. This provides an intuitive and valid approach not only for periodic, but also for systems with no translational symmetry. By refolding into the primitive Brillouin zone of the pristine crystal we recover the conventional unfolded bands. We implement our algorithm in the Siesta package and apply it to defects on Si and graphene.

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Section: Amorphous Simentioning

confidence: 99%

Band unfolding made simple

Mayo

Ynduráin

Soler

2020

J. Phys.: Condens. Matter

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“…In this study, we perform an ab initio simulation of Cu 46 Zr 46 Al 8 to determine the structural and electronic properties using the FEAR [20][21][22][23] technique and a traditional MQ method. FEAR has shown promise in modeling a wide range of amorphous systems, ranging from a-Si, [24] a-SiO 2 , silver-doped GeSe 3 , sodium silicate glass, [25] and a-C [23,26] to complex BMGs. [27] FEAR aims to use experimental information to aid and accelerate ab initio simulation of disordered materials.…”

Section: Methodology and Modelsmentioning

confidence: 99%

Forced Enhanced Atomic Refinement Modeling of the Metallic Glass Cu₄₆Zr₄₆Al₈

Thapa

Subedi

Bhattarai

et al. 2021

Physica Status Solidi (b)

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Herein, the structure of Cu46Zr46Al8 is inverted from X‐ray structure factor data and energy minimizations as implemented with forced enhanced atomic refinement (FEAR). The models generated are in good agreement with structural data obtained from diffraction experiment. Voronoi tessellation analysis shows reasonable agreement with previous results, and the models include structural units believed to have slow dynamics near glass transition and be responsible for the excellent glass forming ability of this metallic glass. It is shown, with constant temperature molecular dynamics (MD), that there is a significant increase in the fraction of these particular clusters near the glass transition. Space‐projected conductivity (SPC) calculations show that conduction through Zr dominates over Cu. Vibrational modes are strongly localized on a few Al atoms at high frequencies and distributed almost uniformly on Cu and Zr atoms at low frequencies.

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“…93,94 For inorganic substances, amorphous silicon and its hydrogenated counterparts have been extensively studied, given their important roles in thin-film transistors, multi-junction solar cells, image-sensor arrays, etc. 95,96 In addition, probing reaction pathways by atomistic modeling can also help to calibrate reaction rules that are the foundation of molecular-level kinetic models for petroleum refining. 97,98 In these kinetic models, pre-defined pathways are used to generate reaction networks to predict product yields from mixed reactants.…”

Section: Understanding the Tunability Of Hcms With Computation Molecular Dynamics Simulations Of The High-temperature Reactions Of Hcmsmentioning

confidence: 99%

Upgrading carbonaceous materials: Coal, tar, pitch, and beyond

Zang

Dong

Jian

et al. 2022

Matter

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Heavy carbonaceous materials (HCMs) such as coal are mostly used for nonrenewable power generation, while derivatives such as tar and pitch are often discarded by-products. Upgrading HCMs for a broad array of potential applications including their use in batteries, membranes, and catalysts could play an important role in the global demand for carbon neutralization. The diversity of HCMs is a technological asset that allows for the direct synthesis of highly customizable materials suited for specific applications. Herein, we will discuss state-of-the-art engineering techniques that can be employed to upscale HCMs and how the nature of the carbon source affects the final product. Further, we illustrate how machine learning (ML) methods can empower the screening of carbonaceous sources from this large family of materials with extremely diverse chemistry. We will also discuss data-driven methods to identify and prioritize the effects of individual processing parameters that could lead to a consistent as well as flexible manufacturing process.

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Large and realistic models of amorphous silicon

Cited by 23 publications

References 50 publications

Band unfolding made simple

Band unfolding made simple

Forced Enhanced Atomic Refinement Modeling of the Metallic Glass Cu₄₆Zr₄₆Al₈

Upgrading carbonaceous materials: Coal, tar, pitch, and beyond

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Large and realistic models of amorphous silicon

Cited by 23 publications

References 50 publications

Band unfolding made simple

Band unfolding made simple

Forced Enhanced Atomic Refinement Modeling of the Metallic Glass Cu46Zr46Al8

Upgrading carbonaceous materials: Coal, tar, pitch, and beyond

Contact Info

Product

Resources

About

Forced Enhanced Atomic Refinement Modeling of the Metallic Glass Cu₄₆Zr₄₆Al₈