High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory

Choudhary, Kamal; Kalish, Irina; Beams, Ryan; Tavazza, Francesca

doi:10.1038/s41598-017-05402-0

Cited by 219 publications

(254 citation statements)

References 69 publications

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“…We first screen out the 1189 magnetic materials entries from the databases [30][31][32][33] with convergent total magnetic moment larger than 0.01 μB, and then apply the SI algorithm [37,38] to the remaining 2282 non-magnetic 2D entries and extract topological ones in a single sweep by the following steps: 1. First, we generate the atomic insulator (AI) basis vectors: there are 80 layer groups (LGs) in total.…”

Section: Methodsmentioning

confidence: 99%

Two-dimensional topological materials discovery by symmetry-indicator method

Wang

Tang

et al. 2019

Phys. Rev. B

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Two-dimensional (2D) topological materials (TMs) have attracted tremendous attention due to the promise of revolutionary devices with non-dissipative electric or spin currents. Unfortunately, the scarcity of 2D TMs holds back the experimental realization of such devices. In this work, based on our recently developed, highly efficient TM discovery algorithm using symmetry indicators, we explore the possible 2D TMs in all non-magnetic compounds in four recently proposed materials databases for possible 2D materials. We identify hundreds of 2D TM candidates, including 205 topological (crystalline) insulators and 299 topological semimetals. In particular, we highlight MoS, with a mirror Chern number of -4, as a possible experimental platform for studying the interaction-induced modification to the topological classification of materials. Our results winnow out the topologically interesting 2D materials from these databases and provide a TM gene pool which for further experimental studies.

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Section: Methodsmentioning

confidence: 99%

Two-dimensional topological materials discovery by symmetry-indicator method

Wang

Tang

et al. 2019

Phys. Rev. B

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“…Choudhary et al made publicly available a 2D database with hundreds of single-layered materials [157]. They used the simple idea of comparing the PBE lattice parameters from the Materials Project database [149], against the experimental values of ICSD.…”

Section: D Materialsmentioning

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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“…With the advent of materials informatics toolkits and software enabling the development of high‐throughput (HT) screening workflows, HT periodic density functional theory (DFT) has been used to construct databases of electronic, energetic, and structural properties for hundreds of thousands of inorganic materials from first‐principles calculations . Metal–organic frameworks (MOFs), a novel class of highly porous crystalline materials, are well‐suited for computational screening studies due to the modular nature of their inorganic nodes and organic linkers .…”

Section: Introductionmentioning

confidence: 99%

Identifying promising metal–organic frameworks for heterogeneous catalysis via high‐throughput periodic density functional theory

2019

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Metal–organic frameworks (MOFs) are a class of nanoporous materials with highly tunable structures in terms of both chemical composition and topology. Due to their tunable nature, high‐throughput computational screening is a particularly appealing method to reduce the time‐to‐discovery of MOFs with desirable physical and chemical properties. In this work, a fully automated, high‐throughput periodic density functional theory (DFT) workflow for screening promising MOF candidates was developed and benchmarked, with a specific focus on applications in catalysis. As a proof‐of‐concept, we use the high‐throughput workflow to screen MOFs containing open metal sites (OMSs) from the Computation‐Ready, Experimental MOF database for the oxidative C—H bond activation of methane. The results from the screening process suggest that, despite the strong C—H bond strength of methane, the main challenge from a screening standpoint is the identification of MOFs with OMSs that can be readily oxidized at moderate reaction conditions. © 2019 Wiley Periodicals, Inc.

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High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory

Cited by 219 publications

References 69 publications

Two-dimensional topological materials discovery by symmetry-indicator method

Two-dimensional topological materials discovery by symmetry-indicator method

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

Identifying promising metal–organic frameworks for heterogeneous catalysis via high‐throughput periodic density functional theory

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