Zare Pakzad, Sina, Nasr Esfahani, Mohammad orcid.org/0000-0002-6973-2205, Canadinc, Demircan et al. (1 more author) (2025) Machine learning-driven atomistic analysis of mechanical behavior in silicon nanowires. Computational Materials Science. 113446. ISSN 0927-0256
Abstract
This study investigates the modulus of elasticity of silicon nanowires using a combination of molecular dynamics simulations and machine learning techniques. The research presents a substantial dataset with over 3000 data points obtained from molecular dynamics simulations, which reveals detailed insights into the mechanical properties of silicon nanowires and underscores the importance of accurate model calibration. Machine learning surrogate models are employed to predict the elasticity of silicon nanowires, focusing on the influence of surface state and crystal orientation. By analyzing partial dependencies and using inverse pole figures, the study demonstrates that the modulus of elasticity exhibits significant orientation dependence. This work bridges computational and experimental approaches, offering a refined understanding of the mechanical behavior of silicon nanowires. The findings highlight the potential of integrating machine learning with atomistic simulations to improve the predictive accuracy of material properties, building the framework for advancements in nanoelectromechanical applications.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy. |
Keywords: | Machine learning,Modulus of elasticity,Molecular dynamics,Silicon nanowire,Tensile behavior |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 03 Jan 2025 13:00 |
Last Modified: | 03 Jan 2025 13:00 |
Published Version: | https://doi.org/10.1016/j.commatsci.2024.113446 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.commatsci.2024.113446 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:221302 |