Mechanical Property Evaluation of Buckypaper/Epoxy Composites Using Molecular Dynamics Simulations Fully Implemented on Graphical Processing Units

2012 ◽  
Vol 9 (12) ◽  
pp. 2144-2154
Author(s):  
Nima Nouri ◽  
Saeed Ziaei-Rad
Keyword(s):  
Mechanical Property ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Epoxy Composites ◽  
Graphical Processing Units ◽  
Property Evaluation ◽  
Graphical Processing ◽  
Dynamics Simulations

Ecological Impact of Green Computing Using Graphical Processing Units in Molecular Dynamics Simulations

2018 ◽  
Vol 9 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Izabele Marquetti ◽  
Jhonatam Rodrigues ◽  
Salil S. Desai
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Ecological Impact ◽  
Green Computing ◽  
Processing Unit ◽  
Central Processing ◽  
Computational Performance ◽  
Graphical Processing Units ◽  
Graphical Processing ◽  
Dynamics Simulations

Molecular dynamics (MD) models require comprehensive computational power to simulate nanoscale phenomena. Traditionally, central processing unit (CPU) clusters have been the standard method of performing these numerically intensive computations. This article investigates the use of graphical processing units (GPUs) to implement large-scale MD models for exploring nanofluidic-substrate interactions. MD models of water nanodroplets over flat silicon substrate are tracked wherein the simulation attains a steady state computational performance. Different classes of GPU units from NVIDIA (C2050, K20, and K40) are evaluated for energy efficiency performance with respect to three green computing measures: simulation completion time, power consumption, and CO2 emissions. The CPU+K40 configuration displayed the lowest energy consumption profile for all the measures. This research demonstrates the use of energy efficient graphical computing versus traditional CPU computing for high-performance molecular dynamics simulations.

Neural network potential for Zr-Rh system by machine learning

2021 ◽  
Author(s):  
Kun Xie ◽  
Chong Qiao ◽  
Hong Shen ◽  
Riyi Yang ◽  
Ming Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Mechanical Property ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Structural Features ◽  
Ab Initio Molecular Dynamics ◽  
The Neural Network ◽  
Dynamics Simulations ◽  
Good Agreement

Abstract Zr-Rh metallic glass has enabled its many applications in vehicle parts, sports equipment and so on due to its outstanding performance in mechanical property, but the knowledge of the microstructure determining the superb mechanical property remains yet insufficient. Here, we develop a deep neural network potential of Zr-Rh system by using machine learning, which breaks the dilemma between the accuracy and efficiency in molecular dynamics simulations, and greatly improves the simulation scale in both space and time. The results show that the structural features obtained from the neural network method are in good agreement with the cases in ab initio molecular dynamics simulations. Furthermore, we build a large model of 5400 atoms to explore the influences of simulated size and cooling rate on the melt-quenching process of Zr77Rh23. Our study lays a foundation for exploring the complex structures in amorphous Zr77Rh23, which is of great significance for the design and practical application.

Nanoscale Modeling and Simulation of Interfacial Bonding of Single-Walled Nanotube Reinforced Composites

2003 ◽  
Author(s):  
Jihua Gou ◽  
Shunliang Jiang ◽  
Bob Minaie ◽  
Zhiyong Liang ◽  
Chuck Zhang ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Epoxy Resin ◽  
Molecular Mechanics ◽  
Stress Transfer ◽  
Molecular Structures ◽  
Interfacial Bonding ◽  
Epoxy Composites ◽  
Reinforced Composites ◽  
Dynamics Simulations

Owning to the extraordinary mechanical, electrical and thermal properties of single-walled nanotubes (SWNTs), SWNT reinforced composites can be used for various applications. In the development of SWNT reinforced composites, one of the fundamental issues that scientists and engineers are confronting is the SWNT-polymer interfacial bonding, which will determine the load transfer capability from the polymer matrix to the nanotube. In single-walled nanotube (SWNT) reinforced epoxy composites, the epoxy resin molecules and the nanotubes are at the nano scale. the interaction at the SWNT/epoxy resin interface is highly dependent on their local molecular structures and bonding. At this small length scale, the lattice structures of the nanotube and the epoxy resin cannot be considered continuous, and their interfacial properties cannot be determined through continuum mechanics. In this paper, the interfacial bonding of SWNT reinforced epoxy composites is investigated using molecular mechanics and molecular dynamics simulations based on a cured epoxy resin model, which is constructed by incorporating three-dimensional crosslinks formed with Shell EPON 862 epoxy resin and EPI CURE W curing agent during polymerization. The interfacial bonding energy between the SWNT and the cured epoxy resin is analyzed using molecular mechanics. Furthermore, the pullout of a SWNT from the cured epoxy resin is investigated using molecular dynamics simulations. Based on the pullout simulation, the interfacial shear strength between the SWNT and the cured epoxy resin is calculated to be up to 75MPa. These analysis results indicate that there could be an effective stress transfer from the epoxy resin to the nanotube.

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