Article (Scientific journals)
Molecular dynamics simulation and machine learning to predict mechanical behavior of Cu/Zr multilayer nanofilms under tension-compression
Nguyen, Hoang-Giang; Young, Sheng-Joue; LE, Thanh-Dung et al.
2025In Journal of Non-Crystalline Solids, 666, p. 123682
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Keywords :
Machine learning; Mechanical properties; Molecular dynamics; Multilayer nanofilms; Dynamics simulation; Lattice disorders; Machine-learning; Mechanical; Mechanical behavior; Molecular dynamics machines; Multilayer nano-films; Nano films; Property; Strain-rates; Electronic, Optical and Magnetic Materials; Ceramics and Composites; Condensed Matter Physics; Materials Chemistry
Abstract :
[en] This study utilizes molecular dynamics simulations to examine the mechanical response of Cu/Zr multilayer nanofilms under tension and compression deformation with the assistance of machine learning. The results demonstrate slip behavior during the tensile process, occurring exclusively in the Cu film, and phase transformation during the compression process, occurring solely in the Zr film. Additionally, this study investigates the effects of temperature, layer thickness, and strain rate on dislocation evolution within nanofilms. This study reveals that lattice disorder in Cu/Zr nanofilms mitigates the impact of external conditions by inhibiting the reverse movement of dislocations. Temperature and strain rate significantly affect the mechanical behavior, while the number of layers is negligible. Therefore, temperature and strain rate primarily influence plastic deformation in Cu/Zr nanofilms. Additionally, the research elucidates how temperature, strain rates, and layer configuration contribute to lattice disorder. These findings offer novel insights into the mechanical characteristics and deformation mechanisms of Cu/Zr at the atomic scale.
Disciplines :
Materials science & engineering
Author, co-author :
Nguyen, Hoang-Giang;  Department of Mechanical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan ; Department of Electronic Engineering, National United University, Miaoli, Taiwan ; Faculty of Engineering, Kien Giang University, Kien Giang Province, Viet Nam
Young, Sheng-Joue;  Department of Electronic Engineering, National United University, Miaoli, Taiwan
LE, Thanh-Dung  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom ; Department of Electrical Engineering, Écolede Technologie Supérieure, University of Québec, Montréal, Canada
Nguyen, Chi-Ngon;  College of Engineering, Can Tho University, Ninh Kieu District, Can Tho, Viet Nam
Do, Le-Binh;  Faculty of Engineering, Kien Giang University, Kien Giang Province, Viet Nam
Nguyen, Thai-Nam;  Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
Fang, Te-Hua ;  Department of Mechanical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan ; Department of Fragrance and Cosmetic Science, Kaohsiung Medical University, Kaohsiung, Taiwan
External co-authors :
yes
Language :
English
Title :
Molecular dynamics simulation and machine learning to predict mechanical behavior of Cu/Zr multilayer nanofilms under tension-compression
Publication date :
15 October 2025
Journal title :
Journal of Non-Crystalline Solids
ISSN :
0022-3093
Publisher :
Elsevier B.V.
Volume :
666
Pages :
123682
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
National Science and Technology Council
Funding text :
The authors acknowledge the support from the National Science and Technology Council, Taiwan , under grant numbers NSTC 113\u20132221-E-992\u2013067-MY3 , NSTC 113\u20132811-E239\u2013002 , and Industry Cooperation Project no. 113A00262 .
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