A computational intelligence based dislocation recognition during molecular dynamics simulation

Jianfeng Huang; Yi  Chen; Yuaxin Luo; Don McGlinchey

doi:10.1109/IConAC.2016.7604923

A computational intelligence based dislocation recognition during molecular dynamics simulation

Jianfeng Huang, Yi Chen, Yuaxin Luo, Don McGlinchey

Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Dislocation evolution in metal is the majority response of external stress or strain during the deformation. In this paper, a computational intelligence aided dislocation recognition algorithm which is integrated in a molecular dynamics simulation is proposed. This algorithm is inspired by genetic algorithm(GA) and follows the main GA process in which the initial population is the disperse nodes set and is under the select, crossover to evolve the new generation of nodes set. Finally, two examples of the dislocation recognition during fatigue simulation by using this algorithm is provided with good result when comparing with the traditional MD post process method.

Original language	English
Title of host publication	22nd International Conference on Automation and Computing (ICAC)
Publisher	IEEE
ISBN (Electronic)	9781862181328
DOIs	https://doi.org/10.1109/IConAC.2016.7604923
Publication status	Published - 24 Oct 2016

Keywords

molecular dynamics simulation, dislocation evolution, computational intelligence aided dislocation recognition algorithm, genetic algorithm

Documents and Links

10.1109/IConAC.2016.7604923

Luo, Y et al (2016) A computational intelligence based dislocation recognition during molecular dynamics simulation
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Accepted author manuscript, 1.26 MB

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Cite this

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title = "A computational intelligence based dislocation recognition during molecular dynamics simulation",

abstract = "Dislocation evolution in metal is the majority response of external stress or strain during the deformation. In this paper, a computational intelligence aided dislocation recognition algorithm which is integrated in a molecular dynamics simulation is proposed. This algorithm is inspired by genetic algorithm(GA) and follows the main GA process in which the initial population is the disperse nodes set and is under the select, crossover to evolve the new generation of nodes set. Finally, two examples of the dislocation recognition during fatigue simulation by using this algorithm is provided with good result when comparing with the traditional MD post process method.",

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N2 - Dislocation evolution in metal is the majority response of external stress or strain during the deformation. In this paper, a computational intelligence aided dislocation recognition algorithm which is integrated in a molecular dynamics simulation is proposed. This algorithm is inspired by genetic algorithm(GA) and follows the main GA process in which the initial population is the disperse nodes set and is under the select, crossover to evolve the new generation of nodes set. Finally, two examples of the dislocation recognition during fatigue simulation by using this algorithm is provided with good result when comparing with the traditional MD post process method.

AB - Dislocation evolution in metal is the majority response of external stress or strain during the deformation. In this paper, a computational intelligence aided dislocation recognition algorithm which is integrated in a molecular dynamics simulation is proposed. This algorithm is inspired by genetic algorithm(GA) and follows the main GA process in which the initial population is the disperse nodes set and is under the select, crossover to evolve the new generation of nodes set. Finally, two examples of the dislocation recognition during fatigue simulation by using this algorithm is provided with good result when comparing with the traditional MD post process method.

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