Molecular Dynamics Simulation of Iron — A Review

Chui, C. P.; Liu, Wenqing; Xu, Yongbing; Zhou, Yan

doi:10.1142/s201032471540007x

Cited by 5 publications

(2 citation statements)

References 182 publications

(169 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Illustration of (A) cells distribution in a two-dimensional irregular cellular automata based on the Voronoi cells, and (B) a shapeless cell on a curved interface and its spherical neighborhood [121]. facilitated scientific discovery of interactions among particles or atoms since molecular dynamics method allows us to examine the varying atomic configurations at finite temperatures by incorporating statistical mechanics [149]. The molecular dynamics methods simulate microstructure evolutions from the atomistic point of view by solving the particle trajectories derived from the interatomic forces.…”

Section: Molecular Dynamics Methodsmentioning

confidence: 99%

A review on the modeling and simulations of solid-state diffusional phase transformations in metals and alloys

Liu

Zhan

2018

Manufacturing Rev.

View full text Add to dashboard Cite

Solid-state diffusional phase transformations are vital approaches for controlling of the material microstructure and thus tailoring the properties of metals and alloys. To exploit this mean to a full extent, much effort is paid on the reliable and efficient modeling and simulation of the phase transformations. This work gives an overview of the developments in theoretical research of solid-state diffusional phase transformations and the current status of various numerical simulation techniques such as empirical and analytical models, phase field, cellular automaton methods, Monte Carlo models and molecular dynamics methods. In terms of underlying assumptions, physical relevance, implementation and computational efficiency for the simulation of phase transformations, the advantages and disadvantages of each numerical technique are discussed. Finally, trends or future directions of the quantitative simulation of solid-state diffusional phase transformation are provided.Keywords: Solid-state diffusional phase transformation / numerical simulation / empirical and analytical models / phase-field models / cellular automata / Monte Carlo models / molecular dynamics methods *

show abstract

Section: Molecular Dynamics Methodsmentioning

confidence: 99%

A review on the modeling and simulations of solid-state diffusional phase transformations in metals and alloys

Liu

Zhan

2018

Manufacturing Rev.

View full text Add to dashboard Cite

show abstract

“…The required information on the atomic structure of iron-based nanoparticles with several nanometer size can be provided using molecular-dynamic (MD) simulations [17], which usually performed to simulate the nucleation and growth of nanoparticles [18,19], and to study of the catalytic processes on the iron surfaces [20,21]. The structures predicted by MD simulations can be further verified by comparsion of theroretical and experimental radial distribution functions (RDFs).…”

Section: Introductionmentioning

confidence: 99%

Reactive force-field molecular dynamic models of iron/oxide/carbon nanocomposites designed for magnetic hyperthermia

Avakyan¹,

Manukyan²,

Paramonova³

et al. 2018

Proceedings of the International Conference "Mathematical Biology and Bioinformatics"

View full text Add to dashboard Cite

The study is devoted to nanoparticles with an iron core and an oxide shell structure, which are enclosed by an additional carbon layer. Such particles are promising for magnetic hyperthermia due to their stability in an AC magnetic field and the tunability of the magnetic properties by varying of the thickness of the oxide shell. To determine the correspondence between the atomic structure of synthesized nanoparticles to expectations, the core-shell structure with an additional carbon coating is modeled on the basis of classical molecular dynamics with a reactive force field that allows chemical bonds to form and break. The results of the molecular dynamics simulation are compared with the pair radial functions obtained from the analysis of the extended fine structure of X-ray absorption spectra near the Fe K edge. The resulting model of the cluster Fe 1564 O 409 demonstrates the stability of its atomic structure (at least for 4 ps) at room temperature. The results can be used to study the efficiency for magnetic hyperthermia and the biological effect of the obtained nanocomposites.

show abstract

Interaction energy prediction of organic molecules using deep tensor neural network

Yuan

Ren

Li³

et al. 2021

Chinese Journal of Chemical Physics

View full text Add to dashboard Cite

The interaction energy of two molecules system plays a critical role in analyzing the interacting effect in molecular dynamic simulation. Since the limitation of quantum mechanics calculating resources, the interaction energy based on quantum mechanics can not be merged into molecular dynamic simulation for a long time scale. A deep learning framework, deep tensor neural network, is applied to predict the interaction energy of three organic related systems within the quantum mechanics level of accuracy. The geometric structure and atomic types of molecular conformation, as the data descriptors, are applied as the network inputs to predict the interaction energy in the system. The neural network is trained with the hierarchically generated conformations data set. The complex tensor hidden layers are simplified and trained in the optimization process. The predicted results of different molecular systems indicate that deep tensor neural network is capable to predict the interaction energy with 1 kcal/mol of the mean absolute error in a relatively short time. The prediction highly improves the efficiency of interaction energy calculation. The whole proposed framework provides new insights to introducing deep learning technology into the interaction energy calculation.

show abstract

Molecular Dynamics Simulation of Iron — A Review

Cited by 5 publications

References 182 publications

A review on the modeling and simulations of solid-state diffusional phase transformations in metals and alloys

A review on the modeling and simulations of solid-state diffusional phase transformations in metals and alloys

Reactive force-field molecular dynamic models of iron/oxide/carbon nanocomposites designed for magnetic hyperthermia

Interaction energy prediction of organic molecules using deep tensor neural network

Contact Info

Product

Resources

About