Spectral quadrature for the first principles study of crystal defects: Application to magnesium

Ghosh, Swarnava; Bhattacharya, Kaushik

doi:10.1016/j.jcp.2022.111035

Cited by 5 publications

(2 citation statements)

References 62 publications

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“…3 for a schematic describing this idea. Recent advances in computational methods for large-scale first-principles simulations of defects in materials [29] can be used to accurately calculate defect generation and evolution during processing.…”

Section: Incorporating Ab Initio Approaches In Multiscale Modelling O...mentioning

confidence: 99%

Exploiting Machine Learning in Multiscale Modelling of Materials

Anand

Ghosh

Zhang

et al. 2022

J. Inst. Eng. India Ser. D

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of machine learning of equivariant properties, machine learning-aided statistical mechanics, the incorporation of ab initio approaches in multiscale models of materials processing and application of machine learning in uncertainty quantification. In addition to the above, the applicability of Bayesian approach for multiscale modelling will be discussed. Critical issues related to the multiscale materials modelling are also discussed.This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The publisher acknowledges the US government license to provide public access under the DOE Public Access Plan (http://energy.gov/downloads/doe-publicaccess-plan).

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Section: Incorporating Ab Initio Approaches In Multiscale Modelling O...mentioning

confidence: 99%

Exploiting Machine Learning in Multiscale Modelling of Materials

Anand

Ghosh

Zhang

et al. 2022

J. Inst. Eng. India Ser. D

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“…Diluted magnetic semiconductor oxides (DMSOs) have attracted great attention due to their high Curie temperature ( T c ) and intrinsic magnetism, leading to some potential applications in the electronic and spintronic device industry. − To induce magnetic order in an insulator, it needs partially filled d or f orbitals, so the DMSOs like TiO 2 , HfO 2 , SnO 2 , and ZrO 2 are nonferromagnetic. In order to induce ferromagnetism in the above materials, the magnetic dopants and the oxygen vacancies (V o ) are among the two important factors in the domain of semiconductor materials. − …”

Section: Introductionmentioning

confidence: 99%

Decisive Role of the Defect-Clustering Effect in the Variation of Magnetic Order in Diluted Magnetic Binary Metal Oxides

Bakhtiar,

Ai,

Dong

et al. 2023

J. Phys. Chem. C

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Above room temperature T c, magnetism is becoming viable in diluted magnetic semiconductors, which show different magnetic orders, depending on the dopant type and their fabrication method. However, how the defects can influence the magnetic properties remains unclear at the atomic level. In this article, by taking the Co-doped tetragonal MO2 (t-MO2, M = Ti, Sn, Hf, and Zr) as an example, the density functional theory-based investigations show that the magnetic order is mainly decided by the defect clustering effect, following a “defect-distance-based” defect correlation principle. It is found that the distance between two defects (Co–Co) leads to an alteration of magnetic order, where SnO2 and ZrO2 show antiferromagnetic (AFM) behavior when the nearest Co–Co distance lies within one octahedron, while a larger distance gives rise to the ferromagnetic (FM) state. In contrast, TiO2 exhibits an AFM state at the neighboring Co–Co configuration with the nearest Co–Co distance larger than one octahedra, while HfO2 showed an opposite trend as that of TiO2. Furthermore, the position of oxygen vacancies mainly affects the magnitude of magnetization instead of magnetic order. We believe this work would significantly advances the understanding of defect chemistry and condensed matter physics related to diluted FM-AFM systems.

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Accurate Approximations of Density Functional Theory for Large Systems with Applications to Defects in Crystalline Solids

Bhattacharya

Gavini

Ortiz

et al. 2022

Density Functional Theory

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This chapter presents controlled approximations of Kohn-Sham density functional theory (DFT) that enable very large scale simulations. The work is motivated by the study of defects in crystalline solids, though the ideas can be used in other applications. The key idea is to formulate DFT as a minimization problem over the density operator, and to cast spatial and spectral discretization as systematically convergent approximations. This enables efficient and adaptive algorithms that solve the equations of DFT with no additional modeling, and up to desired accuracy, for very large systems, with linear and sublinear scaling. Various approaches based on such approximations are presented, and their numerical performance demonstrated through selected examples. These examples also provide important insight about the mechanics and physics of defects in crystalline solids.

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Spectral quadrature for the first principles study of crystal defects: Application to magnesium

Cited by 5 publications

References 62 publications

Exploiting Machine Learning in Multiscale Modelling of Materials

Exploiting Machine Learning in Multiscale Modelling of Materials

Decisive Role of the Defect-Clustering Effect in the Variation of Magnetic Order in Diluted Magnetic Binary Metal Oxides

Accurate Approximations of Density Functional Theory for Large Systems with Applications to Defects in Crystalline Solids

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