The complementary graphene growth and etching revealed by large-scale kinetic Monte Carlo simulation

AbstractTo fully understand the kinetics of graphene growth, large-scale atomic simulations of graphene islands evolution up to macro sizes (i.e., graphene islands of a few micrometers or with billions of carbon atoms) during growth and etching is essential, but remains a great challenge. In this paper, we developed a low computational cost large-scale kinetic Monte Carlo (KMC) algorithm, which includes all possible events of carbon attachments and detachments on various edge sites of graphene islands. Such a method allows us to simulate the evolution of graphene islands with sizes up to tens of micrometers during either growth or etching with a single CPU core. With this approach and the carefully fitted parameters, we have reproduced the experimentally observed evolution of graphene islands during both growth or etching on Pt(111) surface, and revealed more atomic details of graphene growth and etching. Based on the atomic simulations, we discovered a complementary relationship of graphene growth and etching—the route of graphene island shape evolution during growth is exactly the same as that of the etching of a hole in graphene and that of graphene island etching is exactly same as that of hole growth. The complementary relation brings us a basic principle to understand the growth and etching of graphene, and other 2D materials from atomic scale to macro size and the KMC algorithm is expected to be further developed into a standard simulation package for investigating the growth mechanism of 2D materials on various substrates.

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Effect of atomic scale plasticity on hydrogen diffusion in iron: Quantum mechanically informed and on-the-fly kinetic Monte Carlo simulations

Journal of Materials Research ◽

10.1557/jmr.2008.0340 ◽

2008 ◽

Vol 23 (10) ◽

pp. 2757-2773 ◽

Cited By ~ 39

Author(s):

A. Ramasubramaniam ◽

M. Itakura ◽

M. Ortiz ◽

E.A. Carter

Keyword(s):

Monte Carlo ◽

Density Functional ◽

Hydrogen Diffusion ◽

Kinetic Monte Carlo ◽

Computational Cost ◽

Bulk Diffusion ◽

Density Functional Theory Calculations ◽

Atomic Scale ◽

Energy Barriers ◽

Eam Potential

We present an off-lattice, on-the-fly kinetic Monte Carlo (KMC) model for simulating stress-assisted diffusion and trapping of hydrogen by crystalline defects in iron. Given an embedded atom (EAM) potential as input, energy barriers for diffusion are ascertained on the fly from the local environments of H atoms. To reduce computational cost, on-the-fly calculations are supplemented with precomputed strain-dependent energy barriers in defect-free parts of the crystal. These precomputed barriers, obtained with high-accuracy density functional theory calculations, are used to ascertain the veracity of the EAM barriers and correct them when necessary. Examples of bulk diffusion in crystals containing a screw dipole and vacancies are presented. Effective diffusivities obtained from KMC simulations are found to be in good agreement with theory. Our model provides an avenue for simulating the interaction of hydrogen with cracks, dislocations, grain boundaries, and other lattice defects, over extended time scales, albeit at atomistic length scales.

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Atomic Scale Modeling of the Silicon (100) Thermal Oxidation, A Kinetic Monte Carlo Approach

MRS Proceedings ◽

10.1557/proc-567-213 ◽

1999 ◽

Vol 567 ◽

Cited By ~ 1

Author(s):

A. Esteve ◽

M. Djafari Rouhani ◽

Ph. Faurous ◽

D. Esteve

Keyword(s):

Monte Carlo ◽

Thermal Oxidation ◽

Large Scale ◽

Kinetic Monte Carlo ◽

Atomic Scale ◽

Scale Modeling ◽

Temporal Aspect ◽

Oxidation Modeling ◽

Original Approach ◽

Oxidation Mechanisms

ABSTRACTIn this paper, we propose an original approach of the silicon (100) dry thermal oxidation modeling that is capable to reproduce the oxidation dynamics at the atomic level and at a large scale comparing with ab initio methods. This approach is based on the use of a Monte Carlo procedure to manage the temporal aspect. In conjunction with experimental literature data, first principle calculations have been carried out with the objective of isolating some elementary oxidation mechanisms, i.e. basic atomic movements and their corresponding energies. This preliminary list of mechanisms is discussed in detail. A growth mechanism allowing the oxide defect generation is proposed. Finally, we present Monte Carlo calculations with the implemented mechanisms where at least three silicon layers are oxidized.

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Formation of embedded Co nanostructures in Cu(001) surface under electromigration

Modern Physics Letters B ◽

10.1142/s0217984921500901 ◽

2020 ◽

pp. 2150090

Author(s):

S. V. Kolesnikov ◽

A. L. Klavsyuk ◽

A. M. Saletsky

Keyword(s):

Monte Carlo ◽

Current Density ◽

Kinetic Monte Carlo ◽

The Self ◽

Atomic Scale ◽

Self Organization ◽

Simulation Results ◽

Self Learning

Formation of embedded Co nanostructures in Cu(001) surface under electromigration is investigated on the atomic scale by performing self-learning kinetic Monte Carlo (kMC) simulations. The analysis of simulation results reveals the following important result. The electromigration of vacancies does not influence on the self-organization of Co nanostructures in the first layer of Cu(001) surface at all values of current density, which can be achieved in experiments.

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KIMERA: A Kinetic Montecarlo Code for Mineral Dissolution

Minerals ◽

10.3390/min10090825 ◽

2020 ◽

Vol 10 (9) ◽

pp. 825

Author(s):

Pablo Martin ◽

Juan J. Gaitero ◽

Jorge S. Dolado ◽

Hegoi Manzano

Keyword(s):

Monte Carlo ◽

Dissolution Rate ◽

Point Defects ◽

Open Source Software ◽

Kinetic Monte Carlo ◽

Object Oriented ◽

Mineral Dissolution ◽

Atomic Scale ◽

Mineral Structure ◽

Scientific Tool

KIMERA is a scientific tool for the study of mineral dissolution. It implements a reversible Kinetic Monte Carlo (KMC) method to study the time evolution of a dissolving system, obtaining the dissolution rate and information about the atomic scale dissolution mechanisms. KIMERA allows to define the dissolution process in multiple ways, using a wide diversity of event types to mimic the dissolution reactions, and define the mineral structure in great detail, including topographic defects, dislocations, and point defects. Therefore, KIMERA ensures to perform numerous studies with great versatility. In addition, it offers a good performance thanks to its parallelization and efficient algorithms within the KMC method. In this manuscript, we present the code features and show some examples of its capabilities. KIMERA is controllable via user commands, it is written in object-oriented C++, and it is distributed as open-source software.

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Kinetics of Precipitation: Comparison between Monte Carlo Simulations, Cluster Dynamics and the Classical Laws

Defect and Diffusion Forum ◽

10.4028/www.scientific.net/ddf.237-240.671 ◽

2005 ◽

Vol 237-240 ◽

pp. 671-676 ◽

Cited By ~ 4

Author(s):

Philippe Maugis ◽

Frédéric Soisson ◽

Ludovic Lae

Keyword(s):

Monte Carlo ◽

Kinetic Monte Carlo ◽

Diffusion Mechanism ◽

Interface Energy ◽

Atomic Scale ◽

Atomistic Simulations ◽

Model Based ◽

Kinetics Of Precipitation ◽

Kinetics Of ◽

Nucleation Growth

We test the main approximations of the classical laws for nucleation, growth and coarsening by comparison with atomistic simulations of the kinetics of precipitation. We investigate the kinetics of phase separation in dilute A-B solid solutions by precipitation of B atoms in the Arich matrix. Classically, the kinetics is represented by the time evolution of the total number of particles and their mean radius. In this work, the kinetics is predicted by three types of models: (a) an Atomic-scale Kinetic Monte Carlo (AKMC) model based on a vacancy diffusion mechanism, (b) a Cluster Dynamics model, and (c) the MultiPreci model, based on the coupling of the classical laws of nucleation, growth and coarsening. Cluster Dynamics and the Multipreci model have been parameterized such that the thermodynamic and kinetic parameters (solubility, diffusion coefficient, interface energy) be identical to that of the AKMC. Under these conditions we find that the classical laws are in good agreement with the atomistic simulations as long as the thermodynamics of the solid solution remains strictly regular. As expected, Cluster Dynamics compares better with the atomistic simulations, especially if a precise description of the energetics of the smallest clusters is applied.

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Evaluating the Benefits of Kinetic Monte Carlo and Microkinetic Modeling for Catalyst Design Studies in the Presence of Lateral Interactions

10.26434/chemrxiv.13012868.v1 ◽

2020 ◽

Author(s):

Xiao Li ◽

Lars Grabow

Keyword(s):

Monte Carlo ◽

Kinetic Monte Carlo ◽

Computational Cost ◽

Mean Field ◽

Catalyst Design ◽

Mean Field Approximation ◽

Lateral Interactions ◽

Trend Prediction ◽

Co Oxidation Reaction ◽

Coverage Effects

<div>Popular computational catalyst design strategies rely on the identification of reactivity descriptors, which can be used along with Brønsted−Evans−Polanyi (BEP) and scaling relations as input to a microkinetic model (MKM) to make predictions for activity or selectivity trends. The main benefit of this approach is related to the inherent dimensionality reduction of the large material space to just a few catalyst descriptors. Conversely, it is well documented that a small set of descriptors is insufficient to capture the intricacies and complexities of a real catalytic system. The inclusion of coverage effects through lateral adsorbate-adsorbate interactions can narrow the gap between simplified descriptor predictions and real systems, but mean-field MKMs cannot properly account for local coverage effects. This shortcoming of the mean-field approximation can be rectified by switching to a lattice-based kinetic Monte Carlo (kMC) method using cluster expansion representation of adsorbate−adsorbate lateral interactions. </div><div><br></div><div>Using the prototypical CO oxidation reaction as an example, we critically evaluate the benefits of kMC over MKM in terms of trend prediction accuracy and computational cost. After confirming that in the absence of lateral interactions the kMC and MKM approaches yield identical trends and mechanistic information, we observed substantial differences between the two kinetic models when lateral interactions were introduced. The difference, however, is mainly manifested in the absolute rates, surface coverages and the optimal descriptor values, whereas relative activity trends remain largely intact. Moreover, the nature of the rate-determining step as identified using Campbell’s degree of rate control is also consistent between both approaches. Considering that the computational cost of MKM is ca. three orders of magnitude lower than for a kMC simulation, the MKM approach does provide the best balance between accuracy and efficiency when used in the context of computational catalyst screening.</div><div><br></div>

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