scholarly journals Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using Complementary DFT and Machine Learning Approaches

2018 ◽  
Vol 2 (1) ◽  
pp. 1800128 ◽  
Author(s):  
Sherif Abdulkader Tawfik ◽  
Olexandr Isayev ◽  
Catherine Stampfl ◽  
Joe Shapter ◽  
David A. Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Properties ◽  
2D Materials ◽  
Learning Approaches ◽  
Structural And Electronic Properties ◽  
Efficient Prediction

scholarly journals HfS2 and TiS2 Monolayers with Adsorbed C, N, P Atoms: A First Principles Study

Catalysts ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 94
Author(s):  
Mailing Berwanger ◽  
Rajeev Ahuja ◽  
Paulo Cesar Piquini
Keyword(s):  
Density Functional Theory ◽  
Electronic Properties ◽  
Molecular Species ◽  
First Principles ◽  
Density Functional ◽  
2D Materials ◽  
Metallic Behavior ◽  
Functional Theory ◽  
Structural And Electronic Properties ◽  
Localized Levels

First principles density functional theory was used to study the energetic, structural, and electronic properties of HfS 2 and TiS 2 materials in their bulk, pristine monolayer, as well as in the monolayer structure with the adsorbed C, N, and P atoms. It is shown that the HfS 2 monolayer remains a semiconductor while TiS 2 changes from semiconductor to metallic behavior after the atomic adsorption. The interaction with the external atoms introduces localized levels inside the band gap of the pristine monolayers, significantly altering their electronic properties, with important consequences on the practical use of these materials in real devices. These results emphasize the importance of considering the interaction of these 2D materials with common external atomic or molecular species.

Tailoring the structural and electronic properties of an SnSe2/MoS2 van der Waals heterostructure with an electric field and the insertion of a graphene sheet

2019 ◽  
Vol 21 (39) ◽  
pp. 22140-22148 ◽  
Author(s):  
Tuan V. Vu ◽  
Nguyen V. Hieu ◽  
Le T. P. Thao ◽  
Nguyen N. Hieu ◽  
Huynh V. Phuc ◽  
...  
Keyword(s):  
Electric Field ◽  
Electronic Properties ◽  
Graphene Sheet ◽  
2D Materials ◽  
Van Der Waals ◽  
Optoelectronic Devices ◽  
Two Dimensional ◽  
Van Der Waals Heterostructure ◽  
Two Dimensional Materials ◽  
Structural And Electronic Properties

van der Waals heterostructures by stacking different two-dimensional materials are being considered as potential materials for nanoelectronic and optoelectronic devices because they can show the most potential advantages of individual 2D materials.

DFT study of magnetic interfaces based on half-metallic Co2FeGe1/2Ga1/2 with h-BN and MoSe2 monolayers

2021 ◽  
Author(s):  
Konstantin Larionov ◽  
Jose Pais Pereda ◽  
Pavel Borisovich Sorokin
Keyword(s):  
Electronic Properties ◽  
2D Materials ◽  
Dft Study ◽  
Half Metallic ◽  
Structural And Electronic Properties

A large variety of recently predicted and synthesized 2D materials significantly broaden capabilities of magnetic interfaces design for spintronics applications. Their diverse structural and electronic properties allow finely adjust interfacial...

scholarly journals Efficient prediction of structural and electronic properties of hybrid 2D materials using complementary DFT and machine learning approaches

2018 ◽  
Author(s):  
Sherif Tawfik ◽  
Olexandr Isayev ◽  
Catherine Stampfl ◽  
Joseph Shapter ◽  
David Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parameter Space ◽  
Density Functional ◽  
2D Materials ◽  
Van Der Waals ◽  
Practical Method ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Van Der Waals Heterostructures ◽  
Computational Screening

<p>There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work however, even the most efficient electronic structure methods such as density functional theory, are too time consuming to explore more than a tiny fraction of all possible hybrid 2D materials. Here we demonstrate that a combination of DFT and machine learning techniques provide a practical method for exploring this parameter space much more efficiently than by DFT or experiment. As a proof of concept we applied this methodology to predict the interlayer distance and band gap of bilayer heterostructures. Our methods quickly and accurately predicted these important properties for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.</p>

scholarly journals In silico design of 2D polymers containing truxene-based platforms: insights into their structural and electronic properties

2020 ◽  
Vol 8 (43) ◽  
pp. 15416-15425
Author(s):  
Sergio Gámez-Valenzuela ◽  
Marcelo Echeverri ◽  
Berta Gómez-Lor ◽  
José I. Martínez ◽  
M. Carmen Ruiz Delgado
Keyword(s):  
Electronic Properties ◽  
In Silico ◽  
2D Materials ◽  
In Silico Design ◽  
Structural And Electronic Properties ◽  
2D Polymers

A total of 27 different 2D truxene-based polymers were theoretically investigated. Our results provide interesting guidelines to design novel 2D materials with applications ranging from sensing to photocatalysis or electronics.

scholarly journals Efficient prediction of structural and electronic properties of hybrid 2D materials using complementary DFT and machine learning approaches

2018 ◽  
Author(s):  
Sherif Tawfik ◽  
Olexandr Isayev ◽  
Catherine Stampfl ◽  
Joseph Shapter ◽  
David Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parameter Space ◽  
Density Functional ◽  
2D Materials ◽  
Van Der Waals ◽  
Practical Method ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Van Der Waals Heterostructures ◽  
Computational Screening

<p>There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work however, even the most efficient electronic structure methods such as density functional theory, are too time consuming to explore more than a tiny fraction of all possible hybrid 2D materials. Here we demonstrate that a combination of DFT and machine learning techniques provide a practical method for exploring this parameter space much more efficiently than by DFT or experiment. As a proof of concept we applied this methodology to predict the interlayer distance and band gap of bilayer heterostructures. Our methods quickly and accurately predicted these important properties for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.</p>

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