Ferromagnetism and half-metallicity in two-dimensional 
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 monolayers induced by hole doping

Meng, Ruishen; Houssa, Michel; Iordanidou, Konstantina; Pourtois, Geoffrey; Afanasiev, Valeri; Stesmans, André

doi:10.1103/physrevmaterials.4.074001

Cited by 23 publications

(13 citation statements)

References 44 publications

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“…Espin has the tendency of being relatively larger for the binary compounds with lighter anions in the same crystal structure. This is in agreement with values reported in the literature 26,27,29,30 for the 2D gallium or indium oxides and chalcogenides, with Espin following the order of GaO > GaS > GaSe, and InO>InS>InSe.…”

Section: Resultssupporting

confidence: 92%

“…Similar to the bulk "d 0 " ferromagnetic materials, researches have predicted that the 2D III-VI (gallium oxides/chalcogenides and indium oxides/chalcogenides) 26,27,28,29,30 and IV-VI (tin oxides/chalcogenides and lead oxides) 31,32,33 semiconductors, and some other 2D materials such as InP3, etc. 34,35,36,37 would exhibit non-magnetic to ferromagnetic, and semiconducting to half-metallic transition upon hole doping.…”

Section: Introductionmentioning

confidence: 92%

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Hole-doping induced ferromagnetism in 2D materials

Meng

Pereira

Locquet

et al. 2022

Preprint

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Two-dimensional (2D) ferromagnetic materials are considered to be promising candidates for future generations of spintronic devices. Yet, 2D materials with intrinsic ferromagnetism are scarce. High-throughput first-principles simulations are performed in order to screen 2D materials that present a non-magnetic to a ferromagnetic transition upon hole doping. A global evolutionary search is subsequently performed, in order to identify alternative possible atomic structures of the eligible candidates, and 122 materials exhibiting hole-doping induced ferromagnetism are identified. Their energetic and dynamic stability, as well as their magnetic properties under hole doping, are investigated systematically. Half of these 2D materials are metal halides, followed by chalcogenides, oxides, and nitrides, some of them having predicted Curie temperatures above 300 K. The exchange interactions responsible for the ferromagnetic order in these 2D materials are also discussed. This work not only provides theoretical insights into hole-doped 2D ferromagnetic materials but also enriches the family of 2D magnetic materials for possible spintronic applications.

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Section: Resultssupporting

confidence: 92%

Section: Introductionmentioning

confidence: 92%

Hole-doping induced ferromagnetism in 2D materials

Meng

Pereira

Locquet

et al. 2022

Preprint

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“…This peculiar VB dispersion leads to a sharp high peak of the DOS near the VB edge. By shifting the Fermi level close to this peak, the Stoner criterion is fulfilled and the system becomes ferromagnetic. − Hence, hole-doped CuAlO 2 could be a ferromagnetic material.…”

Section: Resultsmentioning

confidence: 99%

Stoner Ferromagnetism in Hole-Doped CuM^IIIAO₂ with M^IIIA = Al, Ga, and In

Iordanidou

Persson

2021

ACS Appl. Mater. Interfaces

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Using density functional theory calculations, we examine the effect of hole doping on the magnetic and electronic properties of CuM IIIA O 2 , with M IIIA = Al, Ga, and In. CuM IIIA O 2 nonmagnetic semiconductors switch to ferromagnetic half-metals upon hole doping. For CuAlO 2 , the nonmagnetic-to-ferromagnetic transition occurs for hole densities of ∼7 × 10 19 /cm 3 . Ferromagnetism arises from an exchange splitting of the electronic states at the valence band edge, and it can be attributed to the high-lying Cu-d states. Hole doping induced by cation vacancies and substitutional divalent dopants is also investigated. Interestingly, both vacancies and nonmagnetic divalent dopants result in the emergence of ferromagnetism.

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“…These properties are also related to crystal structure and stoichiometry, the local environment and symmetry around key magnetic atoms, structural disorder effects, quantum effects, and thermodynamic phase stability. [13][14][15] The complex interplay between all the intrinsic and microstructural parameters is responsible for the difficulty in predicting the magnetic properties of an alloy. [16][17][18] J. M. D. Coey has pointed an interesting fact about the development of magnetic materials in the 20th century, where most of the evolution in performance has been achieved.…”

Section: Introductionmentioning

confidence: 99%

“…These properties are also related to crystal structure and stoichiometry, the local environment and symmetry around key magnetic atoms, structural disorder effects, quantum effects, and thermodynamic phase stability. [ 13–15 ] The complex interplay between all the intrinsic and microstructural parameters is responsible for the difficulty in predicting the magnetic properties of an alloy. [ 16–18 ]…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Magnetic Materials

Katsikas

Sarafidis

Kioseoglou

2021

Physica Status Solidi (b)

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The technological advancements of every era of human civilization owe themselves to the materials available at the time. Despite the substantial interest in the discovery of novel materials, materials research remains a very delicate and time‐exhaustive procedure. Over the last 30 years, ab initio computational methods based on density functional theory (DFT) have allowed researchers to explore materials with ease and expect above‐experiment measurement precision. However, DFT‐based detailed investigation of novel materials is generally computationally intensive and greatly time‐consuming. This review presents machine learning methods applied to DFT simulation data to uncover connections between material structure, chemical composition, and magnetization, a target property chosen for its great industrial demand. Models are developed that can partially circumvent the need for simulation, guiding researchers in the design of magnetic materials. Specifically, the Materials Project database is examined and it is concluded that Eu, Gd, Pu, Fe, Np, Mn, U, Cr, Co, and Ce are amongst the most common elements found in magnetic materials, and that materials of the same composition may have different magnetization depending on their space group. A neural network capable of predicting magnetization with a standard error of 8.3 × 10−3 μ B Å−3 is created.

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Ferromagnetism and half-metallicity in two-dimensional MO (M=Ga,In) monolayers induced by hole doping

Cited by 23 publications

References 44 publications

Hole-doping induced ferromagnetism in 2D materials

Hole-doping induced ferromagnetism in 2D materials

Stoner Ferromagnetism in Hole-Doped CuM^IIIAO₂ with M^IIIA = Al, Ga, and In

Machine Learning in Magnetic Materials

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Ferromagnetism and half-metallicity in two-dimensional MO (M=Ga,In) monolayers induced by hole doping

Cited by 23 publications

References 44 publications

Hole-doping induced ferromagnetism in 2D materials

Hole-doping induced ferromagnetism in 2D materials

Stoner Ferromagnetism in Hole-Doped CuMIIIAO2 with MIIIA = Al, Ga, and In

Machine Learning in Magnetic Materials

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Stoner Ferromagnetism in Hole-Doped CuM^IIIAO₂ with M^IIIA = Al, Ga, and In