Machine learning and molecular dynamics based models to predict the temperature dependent elastic properties of silver nanowires

References

• X. L. Feng, R. He, P. Yang and M. L. Roukes, “Very high frequency silicon nanowire electromechanical resonators,” Nano Lett., vol. 7, no. 7, pp. 1953–1959, 2007. DOI: 10.1021/nl0706695.
   Web of Science ®Google Scholar
• Y. Kondo and K. Takayanagi, “Gold nanobridge stabilized by surface structure,” Phys. Rev. Lett., vol. 79, no. 18, pp. 3455–3458, 1997. DOI: 10.1103/PhysRevLett.79.3455.
   Web of Science ®Google Scholar
• D. P. Burt, N. R. Wilson, J. M. Weaver, P. S. Dobson and J. V. Macpherson, “Nanowire probes for high resolution combined scanning electrochemical microscopy – atomic force microscopy,” Nano Lett., vol. 5, no. 4, pp. 639–643, 2005. DOI: 10.1021/nl050018d.
   PubMed Web of Science ®Google Scholar
• Y. Cui, Q. Q. Wei, H. K. Park and C. M. Lieber, “Nanowire nanosensors for highly sensitive and selective detection of biological and chemical species,” Science, vol. 293, no. 5533, pp. 1289–1292, 2001. DOI: 10.1126/science.1062711.
   PubMed Web of Science ®Google Scholar
• F. Patolsky, B. P. Timko, G. Zheng and M. Lieber, “Nanowire-based nanoelectronic devices in the life sciences,” MRS Bull., vol. 32, no. 2, pp. 142–149, 2007. DOI: 10.1557/mrs2007.47.
   Web of Science ®Google Scholar
• S. Wang, Z. Shan and H. Huang, “The mechanical properties of nanowires,” Adv. Sci. (Weinh)., vol. 4, no. 4, p. 1600332, 2017. DOI: 10.1002/advs.201600332.
   PubMedGoogle Scholar
• G. Brambilla and D. N. Payne, “The ultimate strength of glass silica nanowires,” Nano Lett., vol. 9, no. 2, pp. 831–835, 2009. DOI: 10.1021/nl803581r.
   PubMed Web of Science ®Google Scholar
• H. Zeng, T. Li, M. Bartenwerfer, S. Fatikow and Y. Wang, “In situ SEM electromechanical characterization of nanowire using an electrostatic tensile device,” J. Phys. D: Appl. Phys., vol. 46, no. 30, p. 305501, 2013. DOI: 10.1088/0022-3727/46/30/305501.
   Web of Science ®Google Scholar
• Z. Liu, et al., “TEM studies of platinum nanowires fabricated in mesoporous silica MCM-41,” Angew. Chem. Int. Ed., vol. 39, no. 17, pp. 3107–3110, 2000. DOI: 10.1002/1521-3773.
   PubMed Web of Science ®Google Scholar
• Y. Lu, C. Peng, Y. Ganesan, J. Y. Huang and J. Lou, “Quantitative in situ TEM tensile testing of an individual nickel nanowire,” Nanotechnology, vol. 22, no. 35, p. 355702, 2011. DOI: 10.1088/0957-4484/22/35/355702.
   PubMed Web of Science ®Google Scholar
• M. Bordag, et al., “Shear stress measurements on InAs nanowires by AFM manipulation,” Small, vol. 3, no. 8, pp. 1398–1401, 2007. DOI: 10.1002/smll.200700052.
   PubMed Web of Science ®Google Scholar
• Z. Wang, Y.-K. Su and H.-L. Li, “AFM study of gold nanowire array electrodeposited within anodic aluminum oxide template,” Appl. Phys. A, vol. 74, no. 4, pp. 563–565, 2002. DOI: 10.1007/s003390100909.
   Web of Science ®Google Scholar
• T. Baldauf, A. Heinzig, J. Trommer, et al., “Tuning the tunneling probability by mechanical stress in Schottky barrier based reconfigurable nanowire transistors,” Solid-State Electron., vol. 128, pp. 148–154, 2017. DOI: 10.1016/j.sse.2016.10.009.
   Web of Science ®Google Scholar
• T. Baldauf, A. Heinzig, J. Trommer, et al., “Stress-dependent performance optimization of reconfigurable silicon nanowire transistors,” IEEE Electron. Device Lett., vol. 36, no. 10, pp. 991–993, 2015. DOI: 10.1109/LED.2015.2471103.
   Web of Science ®Google Scholar
• U. Landman, W. D. Luedtke, N. A. Burnham and R. Colton, “Atomistic mechanisms and dynamics of adhesion, nanoindentation, and fracture,” Science, vol. 248, no. 4954, pp. 454–461, 1990. DOI: 10.1126/science.248.4954.454.
   PubMed Web of Science ®Google Scholar
• M. Lucas, et al., “Plastic deformation of pentagonal silver nanowires: comparison between AFM nanoindentation and atomistic simulations,” Phys. Rev. B, vol. 77, no. 24, p. 245420, 2008. DOI: 10.1103/PhysRevB.77.245420.
   Web of Science ®Google Scholar
• T. Sannicolo, M. Lagrange, A. Cabos, C. Celle, J.-P. Simonato and D. Bellet, “Metallic nanowire‐based transparent electrodes for next generation flexible devices: a review,” Small, vol. 12, no. 44, pp. 6052–6075, 2016. DOI: 10.1002/smll.201602581.
   PubMed Web of Science ®Google Scholar
• S. Kim, et al., “Employment of gold-coated silver nanowires as transparent conductive electrode for organic light emitting diodes,” Nanotechnology, vol. 28, no. 34, p. 345201, 2017. DOI: 10.1088/1361-6528/aa7ba0.
   PubMed Web of Science ®Google Scholar
• H. A. Wu, A. K. Soh, X. X. Wang and Z. H. Sun, “Strength and fracture of single crystal metal nanowire,” KEM, vol. 261–263, pp. 33–38, 2004. DOI: 10.4028/www.scientific.net/KEM.261-263.33.
   Google Scholar
• W. Liang and M. Zhou, “Response of copper nanowires in dynamic tensile deformation,” Proc. Inst. Mech. Eng., Part C: J. Mech. Eng. Sci., vol. 218, no. 6, pp. 599–606, 2004. DOI: 10.1243/095440604774202231.
   Web of Science ®Google Scholar
• Y. Jing, Q. Meng and W. Zhao, “Molecular dynamics simulations of the tensile and melting behaviours of silicon nanowires,” Physica E, vol. 41, no. 4, pp. 685–689, 2009. DOI: 10.1016/j.physe.2008.11.006.
   Google Scholar
• P. S. Branício and J. P. Rino, “Large deformation and amorphization of Ni nanowires under uniaxial strain: a molecular dynamics study,” Phys. Rev. B, vol. 62, no. 24, pp. 16950–16955, 2000. DOI: 10.1103/PhysRevB.62.16950.
   Web of Science ®Google Scholar
• Y. H. Wen, Z. Zhu, G. F. Shao and R. Z. Zhu, “The uniaxial tensile deformation of Ni nanowire: atomic-scale computer simulations,” Physica E, vol. 27, no. 1–2, pp. 113–120, 2005. DOI: 10.1016/j.physe.2004.10.009.
   Google Scholar
• S. J. A. Koh, H. P. Lee, C. Lu and Q. H. Cheng, “Molecular dynamics simulation of a solid platinum nanowire under uniaxial tensile strain: temperature and strain-rate effects,” Phys. Rev. B, vol. 72, no. 8, p. 085414, 2005. DOI: 10.1103/PhysRevB.72.085414.
   Web of Science ®Google Scholar
• K. Gall, J. Diao and M. L. Dunn, “The strength of gold nanowires,” Nano Lett, vol. 4, no. 12, pp. 2431–2436, 2004. DOI: 10.1021/nl048456s.
   Web of Science ®Google Scholar
• S. K. Joshi, K. Pandey, S. K. Singh and S. Dubey, “Molecular dynamics simulations of deformation behaviour of gold nanowires,” J. Nanotechnol., vol. 2019, p. 5710749, 2019. DOI: 10.1155/2019/5710749.
   Web of Science ®Google Scholar
• K. M. Ridings, T. S. Aldershof and S. C. Hendy, “Surface melting and breakup of metal nanowires: theory and molecular dynamics simulation,” J. Chem. Phys., vol. 150, no. 9, p. 094705, 2019. DOI: 10.1063/1.5086435.
   PubMed Web of Science ®Google Scholar
• M. Dalgic, S. Senturk and U. Domekeli, “Molecular dynamics simulation studies on melting of Sn nanowires,” J. Optoelectron. Adv. Mater., vol. 13, no. 11–12, pp. 1570–1576, 2011.
   Web of Science ®Google Scholar
• B. Wang, G. Wang, X. Chen and J. Zhao, “Melting behavior of ultrathin titanium nanowires,” Phys. Rev. B, vol. 67, no. 19, pp. 4–7, 2003. DOI: 10.1103/PhysRevB.67.193403.
   Web of Science ®Google Scholar
• G. Bilalbegović, “Structures and melting in infinite gold nanowires,” Solid State Commun., vol. 115, no. 2, pp. 73–76, 2000. DOI: 10.1016/S0038-1098(00)00149-6.
   Web of Science ®Google Scholar
• J. Kang and H. Hwang, “Molecular dynamics simulation study melting ultrathin copper nanowires,” J. Kor. Phys. Soc., vol. 40, no. 5, pp. 946–948, 2002.
   Web of Science ®Google Scholar
• Y. H. Wen, Z. Z. Zhu, R. Zhu and G. F. Shao, “Size effects on the melting of nickel nanowires: a molecular dynamics study,” Physica E, vol. 25, no. 1, pp. 47–54, 2004. DOI: 10.1016/j.physe.2004.06.048.
   Google Scholar
• M. F. Alam and M. S. Rahman, “Fracture mechanism of single and polycrystal silver nanowire: computational study,” IJMTT, vol. 54, no. 6, pp. 471–476, 2018. DOI: 10.14445/22315373/IJMTT-V54P557.
   Google Scholar
• D. W. Shi, L. M. He, L. G. Kong, H. Lin and L. Hong, “Superheating of Ag nanowires studied by molecular dynamics simulations,” Modell. Simul. Mater. Sci. Eng., vol. 16, no. 2, p. 025009, 2008. DOI: 10.1088/0965-0393/16/2/025009.
   Web of Science ®Google Scholar
• J. F. Rodrigues, L. Florea, M. C. F. de Oliveira, D. Diamond and O. N. Olieveira, “Big data and machine learning for materials science,” Discov. Mater., vol. 1, no. 1, p. 12, 2021. DOI: 10.1007/s43939-021-00012-0.
   PubMedGoogle Scholar
• H. Fujiyoshi, T. Hirakawa and T. Yamashita, “Deep learning-based image recognition for autonomous driving,” IATSS Res., vol. 43, no. 4, pp. 244–252, 2019. DOI: 10.1016/j.iatssr.2019.11.008.
   Web of Science ®Google Scholar
• O. Henaff, 2020. “Data-Efficient Image Recognition with Contrastive Predictive Coding,” Proc. 37th Int. Conf. Mach. Learn., pp. 4182–4192. https://proceedings.mlr.press/v119/henaff20a.html.
   Google Scholar
• S. Agarwalla and K. K. Sarma, “Machine learning based sample extraction for automatic speech recognition using dialectal Assamese speech,” Neural Netw., vol. 78, pp. 97–111, 2016. DOI: 10.1016/j.neunet.2015.12.010.
   PubMed Web of Science ®Google Scholar
• M. D. Skowronski and J. G. Harris, “Acoustic detection and classification of microchiroptera using machine learning: lessons learned from automatic speech recognition,” J. Acoust. Soc. Am., vol. 119, no. 3, pp. 1817–1833, 2006. DOI: 10.1121/1.2166948.
   PubMed Web of Science ®Google Scholar
• T. Bibi, P. Dixit, R. Ghule and R. Jadhav, “Web search personalization using machine learning techniques,” 2014 IEEE Int. Adv. Comput. Conf. (IACC), 2014. pp. 1296–1299. DOI: 10.1109/IAdCC.2014.6779514.
   Google Scholar
• IEEE Xplore. “Credit card fraud detection using machine learning techniques: a comparative analysis,” IEEE Conference Publication. Available: https://ieeexplore.ieee.org/abstract/document/8123782?casa_token=SG82gdJ8jbcAAAAA:0fxnzbdDH4yXLdfJHAYoc_5ULAOTMpwHA-W4g8QtZvsJ0wUCc_-1VnW_T0xGhaXiOYwvMKaJ0aGy. Accessed Aug. 27, 2022.
   Google Scholar
• SpringerLink. “Applicability of machine learning in spam and phishing email filtering: review and approaches,” Available: https://link.springer.com/article/10.1007/s10462-020-09814-9. Accessed Aug. 27, 2022.
   Google Scholar
• H.-C. Wu, Y.-H. Hu and Y.-H. Huang, “Two-stage credit rating prediction using machine learning techniques,” Kybernetes, vol. 43, no. 7, pp. 1098–1113, 2014. DOI: 10.1108/K-10-2013-0218.
   Web of Science ®Google Scholar
• S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” J. Comp. Phys., vol. 117, no. 1, pp. 1–19, 1995. DOI: 10.1006/jcph.1995.1039.
   Web of Science ®Google Scholar
• M. S. Daw and M. I. Baskes, “Embedded-atom method: derivation and application to impurities, surfaces, and other defects in metals,” Phys. Rev. B, vol. 29, no. 12, pp. 6443–6453, 1984. DOI: 10.1103/PhysRevB.29.6443.
   Web of Science ®Google Scholar
• K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev and A. Walsh, “Machine learning for molecular and materials science,” Nature, vol. 559, no. 7715, pp. 547–555, 2018. DOI: 10.1038/s41586-018-0337-2.
   PubMed Web of Science ®Google Scholar
• V. L. Deringer, M. A. Caro and G. Csányi, “Machine learning interatomic potentials as emerging tools for materials science,” Adv. Mater., vol. 31, no. 46, p. 1902765, 2019. DOI: 10.1002/adma.201902765.
   Web of Science ®Google Scholar
• C. Gao, et al., “Innovative materials science via machine learning,” Adv. Funct. Mater., vol. 32, no. 1, p. 2108044, 2022. DOI: 10.1002/adfm.202108044.
   Web of Science ®Google Scholar
• S. Li, Y. Liu, D. Chen, Y. Jiang, Z. Nie and F. Pan, “Encoding the atomic structure for machine learning in materials science,” WIREs Comput. Mol. Sci., vol. 12, no. 1, p. e1558, 2022. DOI: 10.1002/wcms.1558.
   Google Scholar
• S. P. Ong, “Accelerating materials science with high-throughput computations and machine learning,” Comput. Mater. Sci., vol. 161, pp. 143–150, 2019. DOI: 10.1016/j.commatsci.2019.01.013.
   Web of Science ®Google Scholar
• N. Wagner and J. M. Rondinelli, “Theory-guided machine learning in materials science,” Front. Mater., vol. 3, p. 28, 2016. https://www.frontiersin.org/articles/10.3389/fmats.2016.00028
   Web of Science ®Google Scholar
• Y. Zhang and C. Ling, “A strategy to apply machine learning to small datasets in materials science,” npj Comput. Mater., vol. 4, no. 1, pp. 1–8, 2018. DOI: 10.1038/s41524-018-0081-z.
   Google Scholar
• A. Stukowski, “Visualization and analysis of atomistic simulation data with OVITO – the Open Visualization Tool, IOP Publishing,” Modell. Simul. Mater. Sci. Eng., vol. 18, no. 1, p. 015012, 2010. DOI: 10.1088/0965-0393/18/1/015012.
   Web of Science ®Google Scholar
• The MathWorks, Inc., MATLAB and Machine Learning Toolbox Release. Natick, MA, USA: The MathWorks, Inc.; 2022.
   Google Scholar
• J. Liu, Y. Zhang, Y. Zhang, S. Kitipornchai and J. Yang, “Machine learning assisted prediction of mechanical properties of graphene/aluminium nanocomposite based on molecular dynamics simulation,” Mater. Des., vol. 213, p. 110334, 2022. DOI: 10.1016/j.matdes.2021.110334.
   Web of Science ®Google Scholar