Hybrid method for modeling epitaxial growth: Kinetic Monte Carlo plus molecular dynamics

2007 ◽  
Vol 76 (24) ◽  
Author(s):  
P. Zoontjens ◽  
T. P. Schulze ◽  
S. C. Hendy
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
Epitaxial Growth ◽  
Hybrid Method ◽  
Growth Kinetic ◽  
Kinetic Monte Carlo

Coarse kMC-based replica exchange algorithms for the accelerated simulation of protein folding in explicit solvent

2016 ◽  
Vol 18 (18) ◽  
pp. 13052-13065 ◽  
Author(s):  
Emanuel K. Peter ◽  
Joan-Emma Shea ◽  
Igor V. Pivkin
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
Protein Folding ◽  
Kinetic Monte Carlo ◽  
Replica Exchange ◽  
Replica Exchange Molecular Dynamics ◽  
Accelerated Simulation ◽  
Explicit Solvent ◽  
Exchange Algorithms

In this paper, we present a coarse replica exchange molecular dynamics (REMD) approach, based on kinetic Monte Carlo (kMC).

Kinetic Monte Carlo simulations of nanocolumn formation in two-component epitaxial growth

2010 ◽  
Vol 96 (7) ◽  
pp. 071913 ◽  
Author(s):  
Shu Zheng ◽  
Wenguang Zhu ◽  
Y. F. Gao ◽  
G. M. Stocks ◽  
Zhenyu Zhang
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Epitaxial Growth ◽  
Kinetic Monte Carlo ◽  
Two Component ◽  
Kinetic Monte Carlo Simulations

L1 Regularization-Based Model Reduction of Complex Chemistry Molecular Dynamics for Statistical Learning of Kinetic Monte Carlo Models

MRS Advances ◽  
2016 ◽  
Vol 1 (24) ◽  
pp. 1767-1772 ◽  
Author(s):  
Qian Yang ◽  
Carlos A. Sing-Long ◽  
Evan J. Reed
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
Kinetic Monte Carlo ◽  
A Priori ◽  
Reaction Network ◽  
Md Simulations ◽  
Rate Coefficients ◽  
Elementary Reactions ◽  
L1 Regularization ◽  
Complex Chemistry

ABSTRACTKinetic Monte Carlo (KMC) methods have been a successful technique for accelerating time scales and increasing system sizes beyond those achievable with fully atomistic simulations. However, a requirement for its success is a priori knowledge of all relevant reaction pathways and their rate coefficients. This can be difficult for systems with complex chemistry, such as shock-compressed materials at high temperatures and pressures or phenolic spacecraft heat shields undergoing pyrolysis, which can consist of hundreds of molecular species and thousands of distinct reactions. In this work, we develop a method for first estimating a KMC model composed of elementary reactions and rate coefficients by using large datasets derived from a few molecular dynamics (MD) simulations of shock compressed liquid methane, and then using L1 regularization to reduce the estimated chemical reaction network. We find that the full network of 2613 reactions can be reduced by 89% while incurring approximately 9% error in the dominant species (CH4) population. We find that the degree of sparsity achievable decreases when similar accuracy is required for additional populations of species.

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