The Computational 2D Materials Database: high-throughput modeling and discovery of atomically thin crystals

Haastrup, Sten; Strange, Mikkel; Pandey, Mohnish; Deilmann, Thorsten; Schmidt, Per Simmendefeldt; Hinsche, N. F.; Gjerding, Morten Niklas; Torelli, Daniele; Larsen, Poul Scheel; Riis-Jensen, Anders C.; Gath, Jakob; Jacobsen, Karsten; Mortensen, Jens Jørgen; Olsen, Thomas; Thygesen, Kristian Sommer

doi:10.1088/2053-1583/aacfc1

Cited by 911 publications

(1,045 citation statements)

References 174 publications

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“…Haastrup released one of the largest 2D databases [161] with more than 3000 materials. The adopted strategy is different from the previous databases.…”

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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“…Haastrup released one of the largest 2D databases [161] with more than 3000 materials. The adopted strategy is different from the previous databases.…”

Section: D Materialsmentioning

confidence: 99%

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

et al. 2019

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“…We begin by considering propagating plasmon polaritons supported by an extended, continuous graphene sheet, and then move on to the theoretical description of localized plasmons in graphene nanostructures. Finally, it should be noted that although here particular emphasis is given to plasmons in graphene, the theoretical methodology introduced in this section can be straightforwardly applied to other polaritonic excitations in the ever‐increasing number of 2D and quasi‐2D materials …”

Section: Graphene Plasmon Polaritonsmentioning

confidence: 99%

Strong Light–Matter Interactions Enabled by Polaritons in Atomically Thin Materials

Gonçalves

Stenger

Cox

et al. 2020

Advanced Optical Materials

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Polaritons, resulting from the hybridization of light with polarization charges formed at the boundaries between media with positive and negative dielectric response functions, can focus light into regions much smaller than its associated free‐space wavelength. This property is paramount for a plethora of applications in nanophotonics, ranging from biological sensing to photocatalysis to nonlinear and quantum optics. In the two‐dimensional (2D) limit, represented by atomically thin and van der Waals (vdW) materials of single‐layers bound by weak vdW attraction, polaritons are characterized by extremely small wavelengths associated with extreme optical confinement, and furthermore can exhibit long lifetimes, electrical tunability, and extreme sensitivity to their dielectric environment, among many other desirable qualities in nano‐optical device applications. Here, the fundamentals of polaritons in atomically thin materials are summarized, emphasizing plasmon and exciton polaritons, their strong light–matter interactions, and nonlinear plasmonics. More specifically, this review opens with a pedagogical discussion of plasmons in extended and nanostructured graphene, providing a classical electrodynamical model in a nonretarded theoretical framework, and the ultraconfined acoustic plasmons supported by hybrid graphene–dielectric–metal structures. In addition, the basic principles are introduced and the recent developments on nonlinear graphene plasmonics and on strong coupling physics with atomically thin transition metal dichalcogenides are reviewed. Finally, potentially new, promising research directions in the burgeoning field of 2D nanophotonics are identified.

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“…Prominent examples are Materials Project, MaterialsCloud/AIIDA, Novel Materials Discovery (NoMaD), the Open Quantum Materials Database (OQMD), the AFLOWlib repository and the Computational Materials Repository (CMR), which combined have many hundred thousands of entries. Also, specialized databases featuring phonon calculations and 2D materials have recently been presented. Important databases for experimentally observed crystal structures includes The Cambridge Database, The Inorganic Crystal Structure Database, the Crystallographic Open Database (COD) and the Inorganic Material Database (AtomWork) .…”

Section: Machine Learning Conceptsmentioning

confidence: 99%

Machine Learning for Computational Heterogeneous Catalysis

et al. 2019

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Big data and artificial intelligence has revolutionized science in almost every field – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this revolution has led to the development of scientific data repositories, as well as data mining and machine learning tools to investigate the vast materials space. The goal of using these tools is to establish a deeper understanding of the relations between materials properties and activity, selectivity and stability – the important figures of merit in catalysis. Based on these insights, catalyst design principles can be established, which hopefully lead us to discover highly efficient catalysts to solve pressing issues for a sustainable future and the synthesis of highly functional materials, chemicals and pharmaceuticals. The inherent complexity of catalytic reactions quests for machine learning methods to efficiently navigate through the high‐dimensional hyper‐surfaces in structure optimization problems to determine relevant chemical structures and transition states. In this review, we show how cutting edge data infrastructures and machine learning methods are being used to address problems in computational heterogeneous catalysis.

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The Computational 2D Materials Database: high-throughput modeling and discovery of atomically thin crystals

Cited by 911 publications

References 174 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

Strong Light–Matter Interactions Enabled by Polaritons in Atomically Thin Materials

Machine Learning for Computational Heterogeneous Catalysis

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