Elastic properties of bulk and low-dimensional materials using van der Waals density functional

Choudhary, Kamal; Cheon, Gowoon; Reed, Evan J.; Tavazza, Francesca

doi:10.1103/physrevb.98.014107

Cited by 109 publications

(118 citation statements)

References 95 publications

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“…Identification of suitable optoelectronic materials [337] as well as solar absorbers [345,346] have also been possible via HT calculations. Another important study based on HT methods is the obtention of elastic properties of inorganic materials [18,158] and the subsequent structuring of the data into publicly available databases. Additionally, Mera Acosta et al [347] performed a screening in the AFLOWLIB database, showing that three-dimensional materials can exhibit non-magnetic spin splittings similar to the splitting found in the Zeeman effect.…”

Section: Materials Discovery Design and Characterizationmentioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field. characterized by its diverse and huge volume, usually ranging from terabytes to petabytes of data, being created in or near real-time. Such data is found either structured and unstructured in nature, and is exhaustive, usually aiming to capture entire populations in a scalable manner [7]. Simple tasks represent challenges in this scale: capture, curation, storage, search, sharing, analysis, and visualization of the data cannot be accomplished without the proper tools. Thus, it can be effectively summarized by the popular 'five V's': volume, velocity, variety, veracity, and value, shown in figure 3(right). A related sixth V is visualization, although not exclusive to Big Data, which requires different techniques to handle data with various characteristics.Striving to tackle the challenges imposed by Big Data, the field of Data Science has arisen. It is largely interdisciplinary being a combination of mathematics and statistics, computer science and programming, and domain knowledge for problem definition and solving, as shown in figure 3(left). Its objective is, roughly speaking, to deal with the whole process of data production, cleaning, preparation, and finally, analysis. Data science encompasses areas such as Big Data, which deals with large volumes of data, and data mining, which relates to analysis processes to discover patterns and extract knowledge from data, part of the so-called Knowledge Discovery in Databases (KDD).The analysis process within Data Science is challenging, as the techniques are very different from traditional static and rigid datasets, generated and analyzed under a predetermined hypothesis. The distinction from traditional data is based on the larger abundance, exhaustivity, and variety of Big Data. It is also much more dynamic, messy and uncertain, being highly relational [7]. Recently, the possibility of overcoming such a challenge slowly sta...

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Section: Materials Discovery Design and Characterizationmentioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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“…Up to now hundreds of ab initio calculations have been performed to characterize existing and potential 2DMs [15][16][17][18]. However, the nature of the flexural modes is hard to pinpoint computationally, which led to some controversy about the universality of observing flexural modes for 2DMs (see reference [19] for a list of publications reporting linear or quadratic ZA dispersions for the same 2DM) despite their indisputable existence in the continuum limit.…”

mentioning

confidence: 99%

Bending rigidities and universality of flexural modes in 2D crystals

Croy

2020

J. Phys. Mater.

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The existence of flexural modes with a quadratic phonon-dispersion is a distinguishing property of two-dimensional materials and has important consequences for their properties. Here, we deduce theoretically within the harmonic approximation the conditions for which orthotropic two-dimensional materials display a flexural mode. Further, we derive formulae for the calculation of the corresponding bending rigidities using the equilibrium structure and the second-order force constants as input. This completes the description of the elasticity of 2D crystals. Our findings are exemplarily validated by ab initio calculations of the phonon dispersions of four representative materials.

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“…The m = n = 1 component is just the internal functional, as implied in equation (109). The exponential-re-summation term of E DF xc , that is, equation (110), is set up to track general screening, including vertex corrections, when used together with the leading E in…”

Section: Electrodynamics Interpretation Of Consistent Vdw-df Designsmentioning

confidence: 99%

“…provides a mapping of where we can expect cumulant effects (including nonlocal vertex corrections), in equation (109), to play a larger role in material binding. Being based on an exponential re-summation, it has a formal structure that allows a low-level approximation to be accurate on the quasi-particle dynamics, at least for core levels and near the Fermi surface [86,163,164].…”

Section: Electrodynamics Interpretation Of Consistent Vdw-df Designsmentioning

confidence: 99%

“…So far the vdW-DF-cx version has proven itself useful and accurate for descriptions of a range of dense and sparse-matter problems. Successful applications include the study of traditional bulk-matter cohesion, structure, thermo-physical properties [9,45,94,95], and vacancy formation [96], as well as of binding, structure, vibrations, polarization, elastic properties, phase transitions, defects, and dynamics in molecular, polymer, and layered materials [45,[97][98][99][100][101][102][103][104][105][106][107][108][109][110][111][112]. This is, for example, relevant for development of ionic liquids, batteries, thermo-electrics, and organic solar-cell materials [98,[113][114][115][116][117][118].…”

Section: Introductionmentioning

confidence: 99%

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Friends or strangers? Attempts at reactivating buyer–supplier relationships

Poblete

Bengtson

2020

JBIM

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Purpose The purpose of this paper is to explore an important management aspect of business relationship dynamics, namely, the reactivation process of previously ended buyer–supplier relationships. Design/methodology/approach A processual case study approach focusing on a single in-depth case has been used. The case is based on longitudinal data from a number of sources concerning one reactivation failure. Findings Grounded in previous research and based on this study’s case findings, the authors have designed a model of analysis for relationship reactivation processes. Using the model on this study’s particular case, the authors show how the structural properties of network embeddedness and resource ties worked in favor of the process, whereas the social bonds and the lack of them led to mistrust that disturbed the negotiation and, hence, worked against the reactivation process. Originality/value This study makes a contribution to the field of relationship dynamics by exploring relationship reactivation processes. The designed model shows how reactivation can be understood as an interplay between structural properties and (re)building activities and contributes new knowledge on factors that affect this process.

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Elastic properties of bulk and low-dimensional materials using van der Waals density functional

Cited by 109 publications

References 95 publications

From DFT to machine learning: recent approaches to materials science–a review

From DFT to machine learning: recent approaches to materials science–a review

Bending rigidities and universality of flexural modes in 2D crystals

Friends or strangers? Attempts at reactivating buyer–supplier relationships

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