scholarly journals Molecular dynamics simulation of pull-out Halloysite nanotube from polyurethane matrix

2021 ◽  
Vol 13 (9) ◽  
pp. 168781402110446
Author(s):  
Mohammadreza Heidari Pebdani ◽  
Ronald E. Miller
Keyword(s):  
Molecular Dynamics ◽  
Force Field ◽  
Density Functional ◽  
Md Simulation ◽  
Halloysite Nanotubes ◽  
Dynamics Simulation ◽  
Experimental Sample ◽  
Functional Theory ◽  
Pull Out ◽  
Good Agreement

Molecular dynamics (MD) simulation has been applied to study of pull-out of Halloysite nanotubes (HNTs) from a polyurethane (PU) matrix. First, the Machine learning (ML) particle swarm optimization (PSO) method was used to obtain force field parameters for MD from data of density functional theory (DFT) calculations. The current study shows the possibility of using a PSO technique to modify the force field with DFT data with less than 5 kcal/mol discrepancy. Second, we considered the influence of atomic interface on pulling out of HNT from PU. Energy variation has been proposed as the cohesion strength between matrix and nanoparticle. In addition, the best Lennard Jones parameters in the MD simulation make good agreement with an experimental sample stress-strain response.

Bayesian active learning of interatomic force field for molecular dynamics simulation of Pt/Ag(111)

2021 ◽  
Author(s):  
Kai Xu ◽  
Lei Yan ◽  
Bingran You
Keyword(s):  
Molecular Dynamics ◽  
Active Learning ◽  
Force Field ◽  
Density Functional ◽  
Process Model ◽  
Large Scale ◽  
Computational Cost ◽  
Dynamics Simulation ◽  
Potential Energy Landscape ◽  
Three Body

Force field is a central requirement in molecular dynamics (MD) simulation for accurate description of the potential energy landscape and the time evolution of individual atomic motions. Most energy models are limited by a fundamental tradeoff between accuracy and speed. Although ab initio MD based on density functional theory (DFT) has high accuracy, its high computational cost prevents its use for large-scale and long-timescale simulations. Here, we use Bayesian active learning to construct a Gaussian process model of interatomic forces to describe Pt deposited on Ag(111). An accurate model is obtained within one day of wall time after selecting only 126 atomic environments based on two- and three-body interactions, providing mean absolute errors of 52 and 142 meV/Å for Ag and Pt, respectively. Our work highlights automated and minimalistic training of machine-learning force fields with high fidelity to DFT, which would enable large-scale and long-timescale simulations of alloy surfaces at first-principles accuracy.

Molecular Dynamics Simulation of Germanium Nanoparticles

2003 ◽  
Vol 769 ◽  
Author(s):  
Sang H. Yang ◽  
Rajiv J. Berry
Keyword(s):  
Molecular Dynamics ◽  
Density Functional Theory ◽  
Density Functional ◽  
Functional Method ◽  
Dynamics Simulation ◽  
Functional Theory ◽  
Bulk Properties ◽  
Lattice Constants ◽  
Semiconductor Thin Films ◽  
Method Of Density Functional

AbstractNanoparticles are known to melt at temperatures well below the bulk melting point. This behavior is being exploited for the recrystallization of Germanium to form large-grain semiconductor thin films on flexible and low temperature substrates. The melting of Ge nanoparticles as a function of size was investigated using the ab-initio Harris functional method of density functional theory (DFT).The DFT code was initially evaluated for its ability to predict the bulk properties of crystalline Ge. A conjugate gradient method was employed for minimizing the multiphase atomic positional parameters of the diamond, BC8 and ST12 structures. The computed lattice constants, bulk moduli, and internal atomic positional parameters were found to agree well with other calculations and with reported experimental results.A constant temperature Nose-Hoover thermostat was added to the DFT code in order to compute thermal properties via molecular dynamics. The simulations were tested on a 13-atom Ge cluster, which was found to melt at 820 K. Further heating resulted in the cluster breaking up into two smaller clusters, which remained stable up to 1300K.

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