First principles and machine learning based superior catalytic activities and selectivities for N2 reduction in MBenes, defective 2D materials and 2D π-conjugated polymer-supported single atom catalysts

2021 ◽  
Author(s):  
Mohammad Zafari ◽  
Arun S. Nissimagoudar ◽  
Muhammad Umer ◽  
Geunsik Lee ◽  
Kwang S. Kim
Keyword(s):  
Machine Learning ◽  
Nitrogen Fixation ◽  
First Principles ◽  
Conjugated Polymer ◽  
2D Materials ◽  
Single Atom ◽  
Vacancy Defects ◽  
Catalytic Activities ◽  
Polymer Supported ◽  
New Machine

The catalytic activity and selectivity can be improved for nitrogen fixation by using hollow sites and vacancy defects in 2D materials, while a new machine learning descriptor accelerates screening of efficient electrocatalysts.

First-principles screening of single transition metal atoms anchored on two-dimensional C9N4 for the nitrogen reduction reaction

2021 ◽  
Vol 23 (14) ◽  
pp. 8784-8791
Author(s):  
Qingling Meng ◽  
Ling Zhang ◽  
Jinge Wu ◽  
Shuwei Zhai ◽  
Xiamin Hao ◽  
...  
Keyword(s):  
Nitrogen Fixation ◽  
Transition Metal ◽  
First Principles ◽  
Reduction Reaction ◽  
Single Atom ◽  
Two Dimensional ◽  
Metal Atoms ◽  
Transition Metal Atoms ◽  
Catalytically Active ◽  
Single Transition

Theoretical screening of transition metal atoms anchored on monolayer C9N4 as highly stable, catalytically active and selective single-atom catalysts for nitrogen fixation.

First-principles investigation of copper diffusion barrier performance in defective 2D layered materials

2022 ◽  
Author(s):  
Manareldeen Ahmed ◽  
Yan Li ◽  
Wenchao Chen ◽  
Erping Li
Keyword(s):  
Barrier Layer ◽  
Diffusion Barrier ◽  
First Principles ◽  
Energy Barrier ◽  
2D Materials ◽  
Diffusion Path ◽  
Layered Materials ◽  
Vacancy Defects ◽  
2D Layered Materials ◽  
Barrier Performance

Abstract This paper investigates the diffusion barrier performance of 2D layered materials with pre-existing vacancy defects using first-principles density functional theory. Vacancy defects in 2D materials may give rise to a large amount of Cu accumulation, and consequently, the defect becomes a diffusion path for Cu. Five 2D layered structures are investigated as diffusion barriers for Cu, i.e., graphene with C vacancy, hBN with B/N vacancy, and MoS2 with Mo/2S vacancy. The calculated energy barriers using climbing image - nudged elastic band show that MoS2-V2S has the highest diffusion energy barrier among other 2D layers, followed by hBN-VN and graphene. The obtained energy barrier of Cu on defected layer is found to be proportional to the length of the diffusion path. Moreover, the diffusion of Cu through vacancy defects is found to modulate the electronic structures and magnetic properties of the 2D layer. The charge density difference shows that there exists a considerable charge transfer between Cu and barrier layer as quantified by Bader charge. Given the current need for an ultra-thin diffusion barrier layer, the obtained results contribute to the field of application of 2D materials as Cu diffusion barrier in the presence of mono-vacancy defects.

scholarly journals DFT calculations of the structure and stability of copper clusters on MoS2

2020 ◽  
Vol 11 ◽  
pp. 391-406
Author(s):  
Cara-Lena Nies ◽  
Michael Nolan
Keyword(s):  
First Principles ◽  
Density Functional ◽  
Noble Metals ◽  
Semiconductor Device ◽  
2D Materials ◽  
Single Atom ◽  
Copper Binding ◽  
Adsorption Sites ◽  
Potential Applications ◽  
Low Dimensional

Layered materials, such as MoS2, are being intensely studied due to their interesting properties and wide variety of potential applications. These materials are also interesting as supports for low-dimensional metals for catalysis, while recent work has shown increased interest in using 2D materials in the electronics industry as a Cu diffusion barrier in semiconductor device interconnects. The interaction between different metal structures and MoS2 monolayers is therefore of significant importance and first-principles simulations can probe aspects of this interaction not easily accessible to experiment. Previous theoretical studies have focused particularly on the adsorption of a range of metallic elements, including first-row transition metals, as well as Ag and Au. However, most studies have examined single-atom adsorption or adsorbed nanoparticles of noble metals. This means there is a knowledge gap in terms of thin film nucleation on 2D materials. To begin addressing this issue, we present in this paper a first-principles density functional theory (DFT) study of the adsorption of small Cu n (n = 1–4) structures on 2D MoS2 as a model system. We find on a perfect MoS2 monolayer that a single Cu atom prefers an adsorption site above the Mo atom. With increasing nanocluster size the nanocluster binds more strongly when Cu atoms adsorb atop the S atoms. Stability is driven by the number of Cu–Cu interactions and the distance between adsorption sites, with no obvious preference towards 2D or 3D structures. The introduction of a single S vacancy in the monolayer enhances the copper binding energy, although some Cu n nanoclusters are actually unstable. The effect of the vacancy is localised around the vacancy site. Finally, on both the pristine and the defective MoS2 monolayer, the density-of-states analysis shows that the adsorption of Cu introduces new electronic states as a result of partial Cu oxidation, but the metallic character of Cu nanoclusters is preserved.

Machine learning-based high throughput screening for nitrogen fixation on boron-doped single atom catalysts

2020 ◽  
Vol 8 (10) ◽  
pp. 5209-5216 ◽  
Author(s):  
Mohammad Zafari ◽  
Deepak Kumar ◽  
Muhammad Umer ◽  
Kwang S. Kim
Keyword(s):  
Machine Learning ◽  
Nitrogen Fixation ◽  
High Throughput ◽  
High Throughput Screening ◽  
Reduction Reaction ◽  
Single Atom ◽  
Nitrogen Reduction ◽  
Computational Burden ◽  
Boron Doped

Machine learning (ML) methods would significantly reduce the computational burden of catalysts screening for nitrogen reduction reaction (NRR).

Pt atoms stabilized on hexagonal boron nitride as efficient single-atom catalysts for CO oxidation: a first-principles investigation

RSC Advances ◽  
2015 ◽  
Vol 5 (14) ◽  
pp. 10452-10459 ◽  
Author(s):  
Xin Liu ◽  
Ting Duan ◽  
Changgong Meng ◽  
Yu Han
Keyword(s):  
Electronic Structure ◽  
Boron Nitride ◽  
Co Oxidation ◽  
First Principles ◽  
Hexagonal Boron Nitride ◽  
Single Atom ◽  
Vacancy Defects

Taking CO oxidation as a probe, we investigated the electronic structure and reactivity of Pt atoms stabilized by vacancy defects on hexagonal boron nitride (h-BN) by first-principles-based calculations.

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

2018 ◽  
Author(s):  
Sherif Tawfik ◽  
Olexandr Isayev ◽  
Catherine Stampfl ◽  
Joseph Shapter ◽  
David Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Band Gap ◽  
Density Functional ◽  
2D Materials ◽  
Van Der Waals ◽  
Building Blocks ◽  
Machine Learning Techniques ◽  
Interlayer Distance ◽  
Computational Screening ◽  
Wide Range

Materials constructed from different van der Waals two-dimensional (2D) heterostructures offer a wide range of benefits, but these systems have been little studied because of their experimental and computational complextiy, and because of the very large number of possible combinations of 2D building blocks. The simulation of the interface between two different 2D materials is computationally challenging due to the lattice mismatch problem, which sometimes necessitates the creation of very large simulation cells for performing density-functional theory (DFT) calculations. Here we use a combination of DFT, linear regression and machine learning techniques in order to rapidly determine the interlayer distance between two different 2D heterostructures that are stacked in a bilayer heterostructure, as well as the band gap of the bilayer. Our work provides an excellent proof of concept by quickly and accurately predicting a structural property (the interlayer distance) and an electronic property (the band gap) 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.

To Bend or Not to Bend, the Dilemma of Multiple Bonds

2019 ◽  
Author(s):  
Michele Pizzocchero ◽  
Matteo Bonfanti ◽  
Rocco Martinazzo
Keyword(s):  
First Principles ◽  
2D Materials ◽  
Main Group ◽  
First Principles Calculations ◽  
Group 14 ◽  
Multiple Bonds ◽  
Main Group Elements ◽  
Structural Distortions

The manuscript addresses the issue of the structural distortions occurring at multiple bonds between high main group elements, focusing on group 14. These distortions are known as trans-bending in silenes, disilenes and higher group analogues, and buckling in 2D materials likes silicene and germanene. A simple but correlated \sigma + \pi model is developed and validated with first-principles calculations, and used to explain the different behaviour of second- and higher- row elements.

Sign in / Sign up

Export Citation Format

Share Document