Prediction of mechanical properties in graphene-reinforced aluminum nanocomposites using machine learning and molecular dynamics simulations
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Machine Learning in Materials Science · Boron and Carbon Nanomaterials Research · Graphene research and applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (34)
Show source-provided references
1. Jin J, Pei J, Xie P, Chen J, Zhao H (2023) Machine learning-based prediction of mechanical properties and performance of nickel–graphenenanocomposites using molecular dynamics simulation data. ACS Appl Nano Mater 6(13):12190–12199. https://doi.org/10.1021/acsanm.3c0284010.1021/acsanm.3c02840 ↗
2. Ma Z, Wang H, Zhao Y, Li Z, Liu H, Yang Y, Zhao Z (2024) Enhancing mechanical properties of graphene/aluminum nanocomposites via microstructure design using molecular dynamics simulations. Materials 17(18):4552. https://doi.org/10.3390/ma1718455210.3390/ma17184552 ↗
3. Zhou J, Shen J, Yue W, Liu Y, Chen Z (2023) Molecular dynamics simulation of reinforcement mechanism of graphene/aluminum composites and microstructure evolution. J Mater Res Technol 23:2147–2159. https://doi.org/10.1016/j.jmrt.2023.01.10810.1016/j.jmrt.2023.01.108 ↗
4. Kumari N, Sarangi SK (2024) Efficient prediction of mechanical properties of boron nitride nanosheets using artificial neural networks. Phys Scr 99(11):116008. https://doi.org/10.1088/1402-4896/ad6b3c10.1088/1402-4896/ad6b3c ↗
5. Mashhadzadeh AH, Dehaghani MZ, Mashhadzadeh AH, Kadyr A, Golman B, Spitas C, Kostas KV (2024) Machine learning-assisted design of boron and nitrogen doped graphenenanosheets with tailored thermomechanical properties. Comput Mater Sci 240:112998. https://doi.org/10.1016/j.commatsci.2024.11299810.1016/j.commatsci.2024.112998 ↗
6. Pan J, Liu H, Zhu W, Wang S, Gao X, Zhao P (2023) Predicting mechanical properties of boron nitride nanosheets obtained from molecular dynamics simulation: a machine learning method. Crystals 14(1):52. https://doi.org/10.3390/cryst1401005210.3390/cryst14010052 ↗
7. Champa-Bujaico E, Díez-Pascual AM, García-Díaz P (2023) Synthesis and characterization of polyhydroxyalkanoate/graphene oxide/nanoclaybionanocomposites: experimental results and theoretical predictions via machine learning models. Biomolecules 13(8):1192. https://doi.org/10.3390/biom1308119210.3390/biom13081192 ↗
8. Nikzad MH, Heidari-Rarani M, Navarchian AH (2024) Multi-objective optimization and machine learning-based prediction of tensile properties of an armchair graphene sheet. Diamond Relat Mater 144:111014. https://doi.org/10.1016/j.diamond.2024.11101410.1016/j.diamond.2024.111014 ↗
9. Joshi SK, Singh SK, Dubey S (2023) Machine learning and molecular dynamics based models to predict the temperature dependent elastic properties of silver nanowires. Int J Comput Methods Eng Sci Mech 24(5):345–353. https://doi.org/10.1080/15502287.2023.223997410.1080/15502287.2023.2239974 ↗
10. Kibrete F, Trzepieciński T, Gebremedhen HS, Woldemichael DE (2023) Artificial intelligence in predicting mechanical properties of composite materials. J Compos Sci 7(9):364. https://doi.org/10.3390/jcs709036410.3390/jcs7090364 ↗
11. Li JW, Guo JG, Zhou LJ (2023) Molecular dynamics studies on mechanical properties of graphene/nanotwinned aluminum matrix composites. Phys E Low-Dimens Syst Nanostruct 147:115597. https://doi.org/10.1016/j.physe.2023.11559710.1016/j.physe.2023.115597 ↗
12. Srivastava AK, Pathak VK, Kumar M, Kumar R, Prakash S (2022) Mechanical properties of Boron nitride nanosheet reinforced aluminiumnanocomposite: a molecular dynamics study. Mol Simul 48(11):1003–1016. https://doi.org/10.1080/08927022.2022.209006710.1080/08927022.2022.2090067 ↗
13. Srivastava AK, Singh R, Pathak VK (2025) Stress–strain response of graphene-reinforced aluminium composite: a molecular dynamics study. Deformation and Fracture in Materials. CRC Press, pp 263–270. https://doi.org/10.1201/9781003406717-2510.1201/9781003406717-25 ↗
14. Li JW, Guo JG, Qu CY (2023) Molecular dynamics investigation on nanoindentation mechanical response of graphene/nanotwinned aluminum matrix composites. Appl Phys A 129(6):456. https://doi.org/10.1007/s00339-023-06687-310.1007/s00339-023-06687-3 ↗
15. Tang J, Ahmadi A, Alizadeh AA, Abedinzadeh R, Abed AM, Smaisim GF, Hadrawi SK, Nasajpour-Esfahani N, Toghraie D (2023) Investigation of the mechanical properties of different amorphous composites using the molecular dynamics simulation. J Mater Res Technol 24:1390–1400. https://doi.org/10.1016/j.jmrt.2023.04.08410.1016/j.jmrt.2023.04.084 ↗
16. Li GY, Lv YH, Zhang L, Jiang K, Zhang L, Liu Y, Tan XL, Bo T (2025) Machine learning-driven exploration of composition- and temperature-dependent transport and thermodynamic properties in LiF–NaF–KF molten salts for nuclear applications. J Phys Chem B 129(37):9418–942910.1021/acs.jpcb.5c03444 ↗
17. Ma L, Duan X, Chen K, Hou C, Li H, Ye J, Wu Y, Rong J, Yu X, Wei Y, Feng J (2025) Deep potential model-based study on the thermodynamic, kinetic, and mechanical properties of PtNi alloys. Chem Eng Sci 286:122793
18. Liu J, Zhang Y, Zhang Y, Kitipornchai S, Yang J (2022) Machine learning assisted prediction of mechanical properties of graphene/aluminiumnanocomposite based on molecular dynamics simulation. Mater Des 213:110334. https://doi.org/10.1016/j.matdes.2021.11033410.1016/j.matdes.2021.110334 ↗
19. Najjar IMR, Sadoun AM, AbdElaziz M, Ahmadian H, Fathy A, Kabeel AM (2023) Prediction of the tensile properties of ultrafine grained Al–SiCnanocomposites using machine learning. J Mater Res Technol 24:7666–7682. https://doi.org/10.1016/j.jmrt.2023.06.04110.1016/j.jmrt.2023.06.041 ↗
20. Shishir MIR, Elapolu MSR, Tabarraei A (2023) A deep learning model for predicting mechanical properties of polycrystalline graphene. Comput Mater Sci 218:111924. https://doi.org/10.1016/j.commatsci.2023.11192410.1016/j.commatsci.2023.111924 ↗
21. Vargo E, Dahl JC, Evans KM, Khan T, Alivisatos P, Xu T (2022) Using machine learning to predict and understand complex self-assembly behaviors of a multicomponent nanocomposite. Adv Mater 34(32):2203168. https://doi.org/10.1002/adma.20220316810.1002/adma.202203168 ↗
22. Dahiya M, Khanna V, Bansal SA (2023) Aluminium-graphene metal matrix nanocomposites: modelling, analysis, and simulation approach to estimate mechanical properties. Mater Today Proc 78:414–419. https://doi.org/10.1016/j.matpr.2023.05.25910.1016/j.matpr.2023.05.259 ↗
23. Pashmforoush F (2023) Mechanical properties prediction of various graphene reinforced nanocomposites using transfer learning-based deep neural network. ProcInstMechEng E J Process MechEng 237(4):1214–1223. https://doi.org/10.1177/0954408922114859110.1177/09544089221148591 ↗
24. Hasan MS, Wong T, Rohatgi PK, Nosonovsky M (2022) Analysis of the friction and wear of graphene reinforced aluminum metal matrix composites using machine learning models. Tribol Int 170:107527. https://doi.org/10.1016/j.triboint.2022.10752710.1016/j.triboint.2022.107527 ↗
25. Luo K, Luo W, Zuo J, Xiao J, Jiang T, Huang L, Zhang W (2025) Machine learning for predicting tensile properties in graphene-reinforced polyethylene composites. Mater Today Commun. https://doi.org/10.1016/j.mtcomm.2025.11305310.1016/j.mtcomm.2025.113053 ↗
26. Wu YC, Chang X, Yu ZG, Zhang YW, Shao JL (2025) Revealing the role of Al₄C₃ in the mechanical behavior of aluminum/graphene composites through machine learning potential-driven atomistic simulations. Mech Mater 209:10542810.1016/j.mechmat.2025.105428 ↗
27. Jin S, Zhao J, Tan X, Jiang B (2026) Machine learning prediction of tribological properties of graphene-reinforced perfluoroelastomer composites based on molecular dynamics simulations and experimental research. Polym Compos10.1002/pc.70854 ↗
28. Amith SC, Lakshmanan P, David A, Arunkumar T (2025) Simulation and prediction of tribological behavior in AA7075/hBN/graphene aluminium nanocomposites using hybrid ultrasonication and machine learning techniques. Int J Interact Des Manuf 19:7559–757810.1007/s12008-025-02388-2 ↗
29. Qi Y, Liu J, Dong Y, Feng XQ, Li Q (2024) Impact of interatomic potentials on atomic-scale wear of graphene: a molecular dynamics study. Lubricants 12(7):245. https://doi.org/10.3390/lubricants1207024510.3390/lubricants12070245 ↗
30. Wang X, Ramírez-Hinestrosa S, Dobnikar J, Frenkel D (2024) Correction: the Lennard-Jones potential: when (not) to use it. Phys Chem Chem Phys 26(37):24714–24715. https://doi.org/10.1039/D4CP90254D10.1039/D4CP90254D ↗
31. Lou TS, Chen N, Zhao L (2024) Adaptive fast desensitized Kalman filter. Circuits Syst Signal Process 43(11):7364–7386. https://doi.org/10.1007/s00034-024-02438-y10.1007/s00034-024-02438-y ↗
32. Liang G, Tiwari P, Nowaczyk S, Byttner S, Alonso-Fernandez F (2024) Dynamic causal explanation based diffusion-variational graph neural network for spatiotemporal forecasting. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2024.337692110.1109/TNNLS.2024.3376921 ↗
33. Chou JS, Molla A (2024) Arctic tern-optimized weighted feature regression system for predicting bridge scour depth. Eng Appl Comput Fluid Mech 18(1):2364745. https://doi.org/10.1080/19942060.2024.236474510.1080/19942060.2024.2364745 ↗
34. Haldar, B., Joardar, H., Mondal, A.K., Alrasheedi, N.H., Khan, R. and Papathi, M.P., 2025. Machine-learning-driven analysis of wear loss and frictional behavior in magnesium hybrid composites.Crystals,15(5), p.452.10.3390/cryst15050452 ↗