Analyzing Fe-Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms

Date

2009-10-01

Author

Bhattacharya, Baidurya
Kumar, G. R. Dinesh
Agarwal, Akash
Erkoç, Şakir
Singh, Arunima
Chakraborti, Nirupam

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Failure behavior of Zn coated Fe is simulated through molecular dynamics (MD) and the energy absorbed at the onset of failure along with the corresponding strain of the Zn lattice are computed for different levels of applied shear rate. temperature and thickness. Data-driven models are constructed by feeding the MD results to an evolutionary neural network. The outputs of these neural networks are utilized to carry out a multi-objective optimization through genetic algorithms, where the best possible tradeoffs between two conflicting requirements, minimum deformation and maximum energy absorption at the onset of failure, are determined by constructing a Pareto frontier.

Subject Keywords

General Physics and Astronomy, General Materials Science, General Computer Science, Mechanics of Materials, General Chemistry, Computational Mathematics

URI

https://hdl.handle.net/11511/51683

Journal

COMPUTATIONAL MATERIALS SCIENCE

DOI

https://doi.org/10.1016/j.commatsci.2009.04.023

Collections

Department of Physics, Article

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Citation Formats

B. Bhattacharya, G. R. D. Kumar, A. Agarwal, Ş. Erkoç, A. Singh, and N. Chakraborti, “Analyzing Fe-Zn system using molecular dynamics, evolutionary neural nets and multi-objective genetic algorithms,” COMPUTATIONAL MATERIALS SCIENCE, pp. 821–827, 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/51683.