Computational methods for 2D materials: discovery, property characterization, and application design

Paul, Joshua T.; Singh, Arunima K.; Dong, Zhipeng; Zhuang, Houlong; Revard, Ben C.; Rijal, Biswas; Ashton, Michael; Linscheid, A.; Blonsky, Michael; Gluhovic, Dorde; Guo, Jing; Hennig, Richard G.

doi:10.1088/1361-648x/aa9305

Cited by 76 publications

(63 citation statements)

References 380 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Much has been written on DFT applications, and articles and reviews of general [305] and specific scope are constantly seen. DFT has been used for almost every kind of system ranging from atomic [306], molecular [307,308], and chemical systems, extended solids, surfaces [309], defects [310], 0D [311][312][313], 1D [314][315][316][317][318][319][320], and 2D [321] systems. In terms of properties, structural [322], electronic/transport [323,324], thermal [325][326][327], electron-phonon [328], optical [329], catalytic [330], magnetic [331][332][333], topological [334][335][336], and many others have been studied.…”

Section: Applications In Materials Sciencementioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

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...

show abstract

Section: Applications In Materials Sciencementioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Typically,b andgaps can be opened in 2D TMDs derived from bulk metals and can be increased in 2D TMDs derived from bulk semiconductors or insulators. [29] The band structures of bulk MoS 2 in 1T and 2H phasecan be described by density functional theory (DFT) calculations based on the known crystal structures. [29] Bulk 2H MoS 2 owns an indirect bandgap of 1.29 eV,w hose conduction band (CB) and valanceb and (VB) mainly consist of Mo 3d orbitala nd S 2p orbital, respectively.…”

Section: Electronic Band Structurementioning

confidence: 99%

“…[29] The band structures of bulk MoS 2 in 1T and 2H phasecan be described by density functional theory (DFT) calculations based on the known crystal structures. [29] Bulk 2H MoS 2 owns an indirect bandgap of 1.29 eV,w hose conduction band (CB) and valanceb and (VB) mainly consist of Mo 3d orbitala nd S 2p orbital, respectively. [8] The valence band maximum (VBM) is located att he Gp oint, while the conduction band minimum (CBM)i sl ocateda lmost halfway alongt he G-K direction,w hich constitutes the indirect bandgapt ransition (Figure 3b).…”

Section: Electronic Band Structurementioning

confidence: 99%

Metastable MoS₂: Crystal Structure, Electronic Band Structure, Synthetic Approach and Intriguing Physical Properties

Zhao

Pan

Fang

et al. 2018

Chemistry A European J

163

172

View full text Add to dashboard Cite

The 2H molybdenum disulfide (MoS ), as a stable hexagonal phase, has been one of the most studied transition metal dichalcogenides over the past decades. In the last five years, the metastable phases of MoS (1T, 1T', 1T'', and 1T''') have seen a revival of interests. Different from the edge-sharing [MoS ] trigonal prisms in the 2H MoS phase, these metastable phases are composed of the edge-sharing [MoS ] octahedra, in which the neighboring Mo-Mo distances differ. Due to the various crystal structures and different electronic configurations of the building [MoS ] motifs, these metastable polytypes are endowed with intriguing physical properties and potential applications in diverse fields. In this Review, the recent research progress on metastable MoS is summarized, especially with an emphasis on the diverse synthetic approaches and the newly uncovered physical properties. The phase structures and electronic band structures are also outlined. In the end, a perspective of the future investigation on metastable MoS is discussed.

show abstract

“…Until now, around a hundred kinds of 2D materials have been experimentally realized and hundreds of novel 2D materials are predicted to be stable by computations. 23,42,44 However, there are more 2D materials to be discovered. Searching for known 3D layered bulks to predict experimentally possible 2D materials is an effective method to further expand the family of 2D materials.…”

Section: Materials Databasesmentioning

confidence: 99%

“…40,41 Several early reviews have summarized the progress, discovery, design, characterization and computational methods for 2D materials. 23,[42][43][44][45] However, the reviews on high-throughput searching for layered materials based on materials database to further discover novel kinds of 2D materials are absent. Therefore, in this review, we summarize the latest reports on high-throughput search for layered materials and related applications in order to promote the further development of 2D materials.…”

mentioning

confidence: 99%

High‐throughput computational screening of layered and two‐dimensional materials

Zhang

Chen

Zhou

2018

WIREs Comput Mol Sci

View full text Add to dashboard Cite

The successful exfoliation of graphene and other kinds of two‐dimensional (2D) materials from their corresponding three‐dimensional (3D) bulk counterparts has inspired researchers to screen layered bulk compounds as parent materials for potential 2D materials. With the rapid development of supercomputers and high‐performance computations, high‐throughput materials screening is a growing new power in materials science for the discovery of novel kinds of materials with desired functionality. Recently, many parent 3D bulks have been identified by high‐throughput screening from materials databases for potential 2D materials, and several 2D materials databases are established through numerous efforts. In addition, on the basis of high‐throughput computations for electronic properties and data‐mining algorithms, several functional layered and 2D materials, such as electrode materials, photohydrolytic catalysts, half metals, piezoelectric monolayers and heterostructures, have been achieved. In this review, we summarize the recent progress in the high‐throughput screening of parent candidates for 2D materials and their further applications, and the challenges and perspectives are also briefly discussed. We highly expect that this review could lead the way forward in the discovery of novel 2D materials and provide a guide for the further development of 2D materials. This article is categorized under: Structure and Mechanism > Computational Materials Science Electronic Structure Theory > Density Functional Theory

show abstract

Computational methods for 2D materials: discovery, property characterization, and application design

Cited by 76 publications

References 380 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

Metastable MoS₂: Crystal Structure, Electronic Band Structure, Synthetic Approach and Intriguing Physical Properties

High‐throughput computational screening of layered and two‐dimensional materials

Contact Info

Product

Resources

About

Computational methods for 2D materials: discovery, property characterization, and application design

Cited by 76 publications

References 380 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

Metastable MoS2: Crystal Structure, Electronic Band Structure, Synthetic Approach and Intriguing Physical Properties

High‐throughput computational screening of layered and two‐dimensional materials

Contact Info

Product

Resources

About

Metastable MoS₂: Crystal Structure, Electronic Band Structure, Synthetic Approach and Intriguing Physical Properties