AFLOW: An automatic framework for high-throughput materials discovery

Curtarolo, Stefano; Setyawan, Wahyu; Hart, Gus L. W.; Jahnátek, Michal; Chepulskii, Roman V.; Taylor, Richard; Wang, Shidong; Xue, Junkai; Yang, Kesong; Levy, Ohad

doi:10.1016/j.commatsci.2012.02.005

Cited by 1,125 publications

(919 citation statements)

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“…Calculations on pristine solid picene were performed using the reported experimental structure 13 as a starting point for geometry relaxation, and a 6×9×4 k-point grid. k-point strings used to plot band structures were generated using the AFLOW framework 49 . Projections of the density of states onto the molecular orbitals of individual picene molecules was performed using a modified version of VASP and related external routines 50 .…”

Section: Methodsmentioning

confidence: 99%

Redox-controlled potassium intercalation into two polyaromatic hydrocarbon solids

et al. 2017

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Alkali metal intercalation into polyaromatic hydrocarbons (PAHs) has been studied intensely following reports of superconductivity in a number of potassium-and rubidium-intercalated materials. There are however no reported crystal structures to inform understanding of the chemistry and physics because of the complex reactivity of PAHs with strong reducing agents at high temperature. Here we present the synthesis of crystalline K2Pentacene and K2Picene by a solid-solid insertion protocol that uses potassium hydride as a redox-controlled reducing agent to access the PAH dianions, enabling determination of their crystal structures. In both cases, the inserted cations expand the parent herringbone packings by reorienting the molecular anions to create multiple potassium sites within initially dense molecular layers, and thus interact with the PAH anion π-systems. The synthetic and crystal chemistry of alkali metal intercalation into PAHs differs from that into fullerenes and graphite, where the cation sites are pre-defined by the host structure.Reaction of alkali and alkaline earth metals with carbon-based molecular solids has been extensively studied in the search for novel magnetic and electronic properties, with a particular focus on superconductivity. [1][2][3][4][5][6] For example, alkali metal intercalation into solid C60 produces A3C60 superconductors, with Tc as high as 38 K for Cs3C60 at 7 kbar. 6 In these materials, cations occupy the interstitial voids that already exist in the host e.g., in the fcc lattice of C60 (Figure 1). 7 Reaction of potassium with picene (C22H14), a phenacene composed of five fused benzene rings, has been reported to afford superconductivity at 18 K. 8 Superconductivity in other alkali-metal polyaromatic hydrocarbons (PAHs) was subsequently reported in phenanthrene-, dibenzopentacene-and coronene-based materials, with the highest reported Tc of 33 K claimed 2 in potassium-doped 1,2:8,9-dibenzopentacene. [9][10][11] Despite the significant interest in these materials, to date no crystal structure has been determined for any of the alkali-metal PAH systems. This structural information is a prerequisite for materials design and for understanding of both physical properties and reaction chemistry.Picene, like many other PAHs (including molecules such as phenanthrene 12 which is reported to afford superconductivity on cation insertion), crystallises in the herringbone structure, 13 with layers consisting of two parallel one-dimensional chains of molecules with opposing inclinations defined by an intermolecular angle, ω, of 57.89(7)° (Supplementary Figure 1). 14 The largest voids in the structure are located between the picene layers, adjacent to the saturated C-H bonds and far from the electron density of the PAH π-systems ( Figure 1a). This contrasts with C60, where the octahedral and tetrahedral voids in the fcc lattice are adjacent to the conjugated π-electron system (Figure 1b Pentacene is the linear isomer of picene, and also adopts the herringbone structure, 16 with ω = 52...

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Section: Methodsmentioning

confidence: 99%

Redox-controlled potassium intercalation into two polyaromatic hydrocarbon solids

et al. 2017

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“…The Oganov fingerprint also gives rise to clear gaps whereas the BCM fingerprint only weakly indicates some gap. The Behler fingerprint gives a well pronounced gap for Zn 2 SnO 4 but only a blurred gap for CsPbI 3 .…”

Section: Numerical Testsmentioning

confidence: 98%

“…On one hand, databases of materials have been created containing structural information of both experimental and theoretical compounds from highthroughput calculations, which are the basis for data-mining techniques in materials discovery projects. [1][2][3][4][5][6][7] On the other hand, ab initio structure predictions [8][9][10][11][12][13][14][15] can produce a huge number of new structures that have either not yet been found experimentally or are metastable. [16][17][18][19][20][21] In both cases, it is essential to quantify similarities and dissimilarities between structures in the data sets, requiring a configurational distance that satisfies the properties of a metric.…”

Section: Introductionmentioning

confidence: 99%

A fingerprint based metric for measuring similarities of crystalline structures

Zhu

Amsler

Fuhrer

et al. 2016

The Journal of Chemical Physics

128

124

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Measuring similarities/dissimilarities between atomic structures is important for the exploration of potential energy landscapes. However, the cell vectors together with the coordinates of the atoms, which are generally used to describe periodic systems, are quantities not directly suitable as fingerprints to distinguish structures. Based on a characterization of the local environment of all atoms in a cell, we introduce crystal fingerprints that can be calculated easily and define configurational distances between crystalline structures that satisfy the mathematical properties of a metric. This distance between two configurations is a measure of their similarity/dissimilarity and it allows in particular to distinguish structures. The new method can be a useful tool within various energy landscape exploration schemes, such as minima hopping, random search, swarm intelligence algorithms, and high-throughput screenings. C 2016 AIP Publishing LLC. [http://dx

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“…The slab unit cells were constructed from optimized bulk geometries with the use of the ACONVASP software package and are provided in the Supplemental Material [47,48]. For the elemental materials with cubic crystal structures, the {100}, {110}, and {111} surfaces were taken into account.…”

Section: B Dft Calculations For Surfacesmentioning

confidence: 99%

Error estimates for density-functional theory predictions of surface energy and work function

et al. 2016

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Density-functional theory (DFT) predictions of materials properties are becoming ever more widespread. With increased use comes the demand for estimates of the accuracy of DFT results. In view of the importance of reliable surface properties, this work calculates surface energies and work functions for a large and diverse test set of crystalline solids. They are compared to experimental values by performing a linear regression, which results in a measure of the predictable and material-specific error of the theoretical result. Two of the most prevalent functionals, the local density approximation (LDA) and the Perdew-Burke-Ernzerhof parametrization of the generalized gradient approximation (PBE-GGA), are evaluated and compared. Both LDA and GGA-PBE are found to yield accurate work functions with error bars below 0.3 eV, rivaling the experimental precision. LDA also provides satisfactory estimates for the surface energy with error bars smaller than 10%, but GGA-PBE significantly underestimates the surface energy for materials with a large correlation energy.

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AFLOW: An automatic framework for high-throughput materials discovery

Cited by 1,125 publications

References 89 publications

Redox-controlled potassium intercalation into two polyaromatic hydrocarbon solids

Redox-controlled potassium intercalation into two polyaromatic hydrocarbon solids

A fingerprint based metric for measuring similarities of crystalline structures

Error estimates for density-functional theory predictions of surface energy and work function

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