Abstract
The characterization of nano-scale materials in the lab setting requires a huge cost, precision and time. The molecular simulations rule out the cost and precision but still these simulations are computationally expensive and intensive. In this regard, we present a support vector machine (SVM)-based molecular dynamics simulation of monolayer graphene to predict its temperature and strain rate-dependent fracture strength. The design of experiments algorithm for full factorial design with six levels of variation in both input settings (temperature and strain rate) is used to create the sample space for training the machine learning model. The prediction capability of machine learning model is further tested by utilizing separate samples generated with SOBOL sequence sampling technique. The accuracy of prediction is assessed by observing correlation coefficient (R2) and error analysis (probability density function (PDF) plots). To construct the model, temperature and strain rate are used as the input features and the desired response quantity is fracture strength of graphene.
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References
Lee C, Wei X, Kysar JW, Hone J (2008) “Measurement of the elastic properties and intrinsic strength of monolayer graphene”, science, vol 321(5887), pp 385–388
Thomas S, Ajith KM (2014) Molecular dynamics simulation of the thermo-mechanical properties of monolayer graphene sheet. Procedia Materials Science. 5:489–498
Ansari R, Ajori S, Motevalli B (2012) Mechanical properties of defective single-layered graphene sheets via molecular dynamics simulation. Superlattices Microstruct 51(2):274–289
Agius Anastasi A, Ritos K, Cassar G, Borg MK (2016) “Mechanical properties of pristine and nanoporous graphene”, Molecular Simulation, vol 42(18), pp 1502–1511
Gupta KK, Roy A, Dey S (2020) “Comparative Study of Various Defects in Monolayer Graphene Using Molecular Dynamics Simulation”, in Advances in Applied Mechanical Engineering, Springer, Singapore, pp 539–546
Tsai JL, Tu JF (2010) Characterizing mechanical properties of graphite using molecular dynamics simulation. Mater Des 31(1):194–199
Gupta KK, Dey S (2019) “Effect of Temperature on the Fracture Strength of Perfect and Defective MonoLayered Graphene”, in Advances in Computational Methods in Manufacturing, Springer, Singapore, pp 793–804
Dewapriya MAN, Rajapakse RKND (2014) “Molecular dynamics simulations and continuum modeling of temperature and strain rate dependent fracture strength of graphene with vacancy defects”, J Appl Mech, vol 81(8)
Zhang YY, Gu Y (2013) Mechanical properties of graphene: Effects of layer number, temperature and isotope. Comput Mater Sci 71:197–200
Gupta KK, Mukhopadhyay T, Roy A, Dey S (2020) “Probing the compound effect of spatially varying intrinsic defects and doping on mechanical properties of hybrid graphene monolayers”, J Mater Sci Technol
Rajasekaran G, Parashar A (2018) Effect of topological defects on mechanical properties of graphene sheets: a molecular dynamics study. Materials Today: Proceedings. 5(2):6780–6788
Mukhopadhyay T, Mahata A, Dey S, Adhikari S (2016) Probabilistic analysis and design of HCP nanowires: an efficient surrogate based molecular dynamics simulation approach. J Mater Sci Technol 32(12):1345–1351
Dewapriya MAN, Rajapakse RKND, Dias WPS (2020) “Characterizing fracture stress of defective graphene samples using shallow and deep artificial neural networks”, Carbon
Zhang Z, Hong Y, Hou B, Zhang Z, Negahban M, Zhang J (2019) Accelerated discoveries of mechanical properties of graphene using machine learning and high-throughput computation. Carbon 148:115–123
Wang X, Han D, Hong Y, Sun H, Zhang J, Zhang J (2019) Machine learning enabled prediction of mechanical properties of tungsten disulfide monolayer. ACS Omega 4(6):10121–10128
Yang H, Zhang Z, Zhang J, Zeng XC (2018) Machine learning and artificial neural network prediction of interfacial thermal resistance between graphene and hexagonal boron nitride. Nanoscale 10(40):19092–19099
Garg A, Vijayaraghavan V, Wong CH, Tai K, Gao L (2014) An embedded simulation approach for modeling the thermal conductivity of 2D nanoscale material. Simul Model Pract Theory 44:1–13
Vijayaraghavan V, Garg A, Wong CH, Tai K, Singru PM (2015) An integrated computational approach for determining the elastic properties of boron nitride nanotubes. Int J Mech Mater Des 11(1):1–14
Garg A, Tai K (2014) Stepwise approach for the evolution of generalized genetic programming model in prediction of surface finish of the turning process. Adv Eng Softw 78:16–27
Garg A, Vijayaraghavan V, Lam JSL, Singru PM, Gao L (2015) A molecular simulation based computational intelligence study of a nano-machining process with implications on its environmental performance. Swarm Evol Comput 21:54–63
MATLAB 2018a, The MathWorks, Inc., Natick, Massachusetts, United States.
Trafalis TB, Ince H (2000) “Support vector machine for regression and applications to financial forecasting”. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, IEEE, vol. 6, pp 348–353
Saunders LJ, Russell RA, Crabb DP (2012) The coefficient of determination: what determines a useful R2 statistic? Invest Ophthalmol Vis Sci 53(11):6830–6832
Plimpton S (1993) “Fast parallel algorithms for short-range molecular dynamics”, Sandia National Labs., Albuquerque, NM (United States), No. SAND-91–1144
Tersoff J (1988) New empirical approach for the structure and energy of covalent systems. Phys Rev B 37(12):6991
Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(1):33–38
Lindsay L, Broido DA (2010) “Optimized Tersoff and Brenner empirical potential parameters for lattice dynamics and phonon thermal transport in carbon nanotubes and graphene”, Physical Review B,vol 81(20), pp 205441
Mortazavi B, Fan Z, Pereira LFC, Harju A, Rabczuk T (2016) Amorphized graphene: a stiff material with low thermal conductivity. Carbon 103:318–326
Rajasekaran G, Kumar R, Parashar A (2016) “Tersoff potential with improved accuracy for simulating graphene in molecular dynamics environment”, Materials Research Express, vol 3(3), pp 035011
Wang MC, Yan C, Ma L, Hu N, Chen MW (2012) Effect of defects on fracture strength of graphene sheets. Comput Mater Sci 54:236–239
Acknowledgements
KKG is glad to acknowledge the financial support provided by Ministry of Education (MoE), Govt. of India during conducting the present study.
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Gupta, K.K., Roy, L., Dey, S. (2022). Machine Learning-Based Molecular Dynamics Simulations of Monolayered Graphene. In: Maiti, D.K., et al. Recent Advances in Computational and Experimental Mechanics, Vol II. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-6490-8_21
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DOI: https://doi.org/10.1007/978-981-16-6490-8_21
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