Influence of the ab-initio calculation parameters on prediction of energy of point defects in silicon

Ganchenkova, Mariya G.; Abgaryan, K. K.; Бажанов, Д. И.; Mutigullin, I. V.; Бородин, В. А.

doi:10.1016/j.moem.2016.01.002

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…On the other hand, the energy of the T configuration is noticeably larger [44,46,47]. In the 64-atoms supercell employed in this work, we also found that the H and X configurations have very similar energies.…”

Section: B Performance: the Si I In Si With An Analytic Potentialsupporting

confidence: 64%

“…These computational parameters are adequate enough for demonstrating the capabilities on our proposed EA: they allow for fast calculations and yet are able to reproduce the defect configurations observed in other DFT studies [44][45][46][47]. In particular, among the various defect configurations that have been observed in these studies, we consider those three with the lowest energy: the Si in an hexagonal interstitial (H), in a tetragonal interstitial (T )…”

Section: B Performance: the Si I In Si With An Analytic Potentialmentioning

confidence: 98%

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Evolutionary computing and machine learning for the discovering of low-energy defect configurations

Arrigoni¹

2020

Preprint

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Section: B Performance: the Si I In Si With An Analytic Potentialsupporting

confidence: 64%

Section: B Performance: the Si I In Si With An Analytic Potentialmentioning

confidence: 98%

Evolutionary computing and machine learning for the discovering of low-energy defect configurations

Arrigoni¹

2020

Preprint

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“…It is also observed that the screened-exchange hybrid functional enables us to predict more accurate defect properties as we progress along the rungs of Perdew’s “Jacob’s ladder” to quantum chemistry’s promised land of chemical accuracy . Although calculated point defects energies with various approximations have been examined for metals − and oxides, , to the best of our knowledge, no work has been reported on the defect properties of hematite based on the above-mentioned hybrid methodology. Specifically, a computational study based on the Hyde–Scuseria–Ernzerhof (HSE) XC functional is needed to obtain the thermodynamic defect properties of hematite, which are difficult to get directly from corrosion experiments on oxide-scale formation.…”

Section: Introductionmentioning

confidence: 99%

Critical Assessment of the Thermodynamics of Vacancy Formation in Fe₂O₃ Using Hybrid Density Functional Theory

et al. 2020

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Fe2O3 hematite is a technologically important material with applications in energy storage as well as being the key phase formed in the rust of iron-based materials. Despite this central importance, there is much that is still unknown regarding the properties of defects in Fe2O3 and consequently oxide growth. Here, using screened hybrid density functional theory (HSE06), we consider the thermodynamics of vacancies in Fe2O3, considering the effects of ionic and electronic chemical potentials on both iron and oxygen vacancy formation. We find that, in the oxygen-rich limit, iron vacancies are easier to form, though the difference in formation energy between the two vacancies is only about 1 eV. In contrast, in the Fe-rich limit, oxygen vacancies have an extremely low formation energy, only 0.07 eV at mid-gap, and would spontaneously form as the Fermi level is reduced, while Fe vacancies require over 5 eV to form. Consistent with experiment, this indicates that Fe2O3 is relatively easily reduced but not oxidized. However, the theoretical picture is very different when considering other exchange–correlation functionals (GGA + U or SCAN), emphasizing the critical role of the exchange–correlation functional in describing this system accurately.

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“…In addition, the computed coordination numbers for Si atoms on Si (001), Si (111) and SiO2 (001) surfaces are 2, 3, and 2, respectively, consistent with values computed in prior first-principles calculations. [15][16][17] 11,12 and experimental 13,14 literature reports.…”

mentioning

confidence: 99%

Mechanisms of GaN quantum dot formation during nitridation of Ga droplets

Reese

Jeon

et al. 2020

Applied Physics Letters

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We have examined the formation mechanisms of GaN quantum dots (QDs) via annealing of Ga droplets in a nitrogen flux. We consider the temperature and substrate dependence of the size distributions of droplets and QDs, as well as the relative roles of Ga/N diffusivity and GaN nucleation rates on QD formation. We report on two competing mechanisms mediated by Ga surface diffusion, namely QD formation at or away from pre-existing Ga droplets. We discuss the relative roles of nucleation and coarsening dominant growth, as well as the polytype selection, on various substrates. The new insights provide an opportunity for tailoring QD size and polytype distributions for a wide range of III-N semiconductor QDs.__________________________ 1) H. Lu and C. Reese contributed equally to this work. 2) Corresponding Author: rsgold@umich.eduIn recent years, quantum dots (QDs) based on gallium nitride (GaN) and its alloys have been demonstrated for a wide variety of device applications, such as solar cells, 1 ,2 lightemitting diodes, 3,4,5 lasers, 6,7 and single-photon emitters. 8,9 QDs are typically grown epitaxially via a strain-induced Stranski-Krastanov (S-K) growth mode transition, which leads to a misfitstrain induced polarization in the QDs. 10 On the other hand, the nucleation and conversion of QDs via nitridation of metallic droplets, known as droplet epitaxy (DE), has attracted much attention as misfit-strain-induced-polarization is expected to be minimized. To date, DE of GaN QDs has been demonstrated on a variety of substrates, including 6H-SiC(0001), 11Si (111), 12,13,14 AlGaN/6H-SiC(0001), 15 and c-Al2O3, 16 with single electron transistor achieved on AlN/3C-SiC(001). 17 Furthermore, understanding of DE is critical for elucidation of the mechanisms for GaN growth under Ga-rich conditions, which are often argued to be a version of liquid phase epitaxy. 18 Indeed, during Ga-rich GaN growth, the low solubility of carbon in Ga and the reactivity of oxygen in Ga, with subsequent desorption of GaO at growth temperatures, leads to low carbon 19 and oxygen 20 co-incorporation. Therefore, DE GaN QDs are expected to be superior to those grown by the SK method. 21 However, conflicting results have been reported regarding the formation mechanisms of DE GaN QDs. For example, Wang et al. 17 and Gherisimova et al. 22 reported on the formation of QDs via a liquid phase epitaxy (LPE)-like process, where GaN crystallizes along the substrate/droplet interface when N supersaturates the liquid Ga. On the other hand, Debnath et al. 15 proposed a surface diffusion-driven mechanism, where Ga diffuses away from the droplets and reacts with N on the surface to form small QDs. Finally, Kawamura et al. 23 and Otsubo et al. 24 propose a formation mechanism where N diffuses along the surface to the droplet edges, and small QDs nucleate at the periphery. Here, we investigate the formation mechanisms for DE GaN QDs using a combined computational-experimental approach. Our first-principles calculations of activation barriers suggest that N is immobi...

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Influence of the ab-initio calculation parameters on prediction of energy of point defects in silicon

Cited by 7 publications

References 26 publications

Evolutionary computing and machine learning for the discovering of low-energy defect configurations

Evolutionary computing and machine learning for the discovering of low-energy defect configurations

Critical Assessment of the Thermodynamics of Vacancy Formation in Fe₂O₃ Using Hybrid Density Functional Theory

Mechanisms of GaN quantum dot formation during nitridation of Ga droplets

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Influence of the ab-initio calculation parameters on prediction of energy of point defects in silicon

Cited by 7 publications

References 26 publications

Evolutionary computing and machine learning for the discovering of low-energy defect configurations

Evolutionary computing and machine learning for the discovering of low-energy defect configurations

Critical Assessment of the Thermodynamics of Vacancy Formation in Fe2O3 Using Hybrid Density Functional Theory

Mechanisms of GaN quantum dot formation during nitridation of Ga droplets

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Product

Resources

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Critical Assessment of the Thermodynamics of Vacancy Formation in Fe₂O₃ Using Hybrid Density Functional Theory