Grand-canonical evolutionary algorithm for the prediction of two-dimensional materials

Revard, Benjamin C.; Tipton, William W.; Yesypenko, Anna; Hennig, Richard G.

doi:10.1103/physrevb.93.054117

Cited by 67 publications

(72 citation statements)

References 70 publications

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“…As an upper bound, 2D SnSe has been synthesized [33,34] and has an exfoliation energy close to 150 meV/atom. The most promising 2D material candidates will have much lower exfoliation energies, but monolayers with exfoliation energies below 150 meV/atom may be feasible for synthesis [16,35]. Fig.…”

Section: Density-functional Theory (Dft)mentioning

confidence: 99%

Topology-Scaling Identification of Layered Solids and Stable Exfoliated 2D Materials

et al. 2017

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The Materials Project crystal structure database has been searched for materials possessing layered motifs in their crystal structures using a topology-scaling algorithm. The algorithm identifies and measures the sizes of bonded atomic clusters in a structure's unit cell, and determines their scaling with cell size. The search yielded 826 stable layered materials that are considered as candidates for the formation of two-dimensional monolayers via exfoliation. Density-functional theory was used to calculate the exfoliation energy of each material and 680 monolayers emerge with exfoliation energies below those of already-existent two-dimensional materials. The crystal structures of these two-dimensional materials provide templates for future theoretical searches of stable twodimensional materials. The optimized structures and other calculated data for all 826 monolayers are provided at https://materialsweb.org.The combination of modern computational tools and the growing number of available crystal structure databases with high-throughput interfaces have accelerated recent efforts to map the materials genome. One of the most recently discovered branches of the materials genome is the class of two-dimensional (2D) materials, which generally have properties that are markedly different from their three-dimensional counterparts. The canonical example is the graphite/graphene system, but monolayers have been exfoliated from many other layered compounds as well [1][2][3][4]. Stable 2D materials can also be obtained by deposition [5][6][7][8] or chemical exfoliation [9,10]. Because the contribution of interlayer interactions to these materials free energies is typically quite small, the existence of a mechanically exfoliable bulk precursor generally indicates the relative stability of a freestanding single layer, regardless of how it is synthesized.In an effort to discover novel 2D materials, two recent studies searched the inorganic crystal structure database (ICSD) for compounds with large interlayer spacings, which are characteristic of weak interlayer bonding that could be overcome by mechanical exfoliation [11,12]. They used the intuitive criteria of a low packing fraction based on the covalent radii of the atoms and an interlayer gap larger than the sum of the covalent radii of atoms at the layers' surfaces along the c-axis to identify layered compounds in the ICSD. They discovered almost 100 layered phases, nearly half of which had monolayers that had not been the subject of any prior publications.Here, we extend their method to identify a large number of layered compounds that were missed using the packing factor and c-axis interlayer gap criteria. We further add the constraint that a bulk material must be thermodynamically stable to be of interest during our search. Therefore, we use the Materials Project (MP) database [13], an online repository of crystallographic and thermodynamic data for over 65,000 compounds calculated with density-functional theory (DFT). Our algorithm is designed to correctly identify ad...

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Section: Density-functional Theory (Dft)mentioning

confidence: 99%

Topology-Scaling Identification of Layered Solids and Stable Exfoliated 2D Materials

et al. 2017

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“…Tipton et al directly compare atomic positions to identify duplicate structures in the population . During the search, two separate lists of previously observed structures are maintained.…”

Section: Evolutionary Algorithms For 2d Crystal Structure Predictionmentioning

confidence: 99%

“…For clarity, 1 × 2 supercells are shown. (Adopted from ‘Grand‐canonical evolutionary algorithm for the prediction of two‐dimensional materials’)…”

Section: Evolutionary Algorithms For 2d Crystal Structure Predictionmentioning

confidence: 99%

“…Multiobjective optimization approaches based on EA were also proposed. 72,82 In these approaches, the objective of structure is not only the total energy but also other properties of the material. Revard et al employ the layer thicknesses of 2D materials as the second objective function.…”

Section: Evaluation Of Fitnessmentioning

confidence: 99%

“…Tipton et al directly compare atomic positions to identify duplicate structures in the population. 81,82 During the search, two separate lists of previously observed structures are maintained. The first list, holds the relaxed, developed structures which are members of the current generation.…”

Section: Evaluation Of Fitnessmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of two‐dimensional materials by the global optimization approach

Luo

Xiang

2016

WIREs Comput Mol Sci

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Crystal structure prediction is one of the most fundamental challenges in the physics and chemistry sciences. In recent years, this problem has gained much practical success by the global optimization approach. Here, we survey some recent progress in finding the global minimum of two‐dimensional (2D) materials, and introduce some details of these global optimization approach. Then, we will give some typical examples to demonstrate their advantages for 2D crystal structure prediction. Finally, the future directions and challenges are briefly discussed. WIREs Comput Mol Sci 2017, 7:e1295. doi: 10.1002/wcms.1295 This article is categorized under: Structure and Mechanism > Computational Materials Science Electronic Structure Theory > Density Functional Theory

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2D Materials Based on Main Group Element Compounds: Phases, Synthesis, Characterization, and Applications

Neupane

Jia

et al. 2020

Adv Funct Materials

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2D materials based on main group element compounds have recently attracted significant attention because of their rich stoichiometric ratios and structure motifs. This review focuses on the phases in various 2D binary materials including III-VI, IV-VI, V-VI, III-V, IV-V, and V-V materials. Reducing 3D materials to 2D introduces confinement and surface effects as well as stabilizes unstable 3D phases in their 2D form. Their crystal structures, stability, preparation, and applications are summarized based on theoretical predictions and experimental explorations. Moreover, various properties of 2D materials, such as ferroelectric effect, anisotropic optical and electrical properties, ultralow thermal conductivity, and topological state are discussed. Finally, a few perspectives and an outlook are given to inspire readers toward exploring 2D materials with new phases and properties.

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Grand-canonical evolutionary algorithm for the prediction of two-dimensional materials

Cited by 67 publications

References 70 publications

Topology-Scaling Identification of Layered Solids and Stable Exfoliated 2D Materials

Topology-Scaling Identification of Layered Solids and Stable Exfoliated 2D Materials

Prediction of two‐dimensional materials by the global optimization approach

2D Materials Based on Main Group Element Compounds: Phases, Synthesis, Characterization, and Applications

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