Atomistic modelling of the hypervelocity dynamics of shock-compressed graphite and impacted graphene armours

Stefano Signetti, Keonwook Kang, Nicola M. Pugno, Seunghwa Ryu

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5 Citations (Scopus)


We present a molecular dynamics (MD) simulation study on the hypervelocity dynamics of shock compressed graphite -up to hundreds of gigapascals- and impacted multilayer graphene armours by employing the AIREBO-M potential. The Morse-type non-singular intermolecular interaction allows the usage of relatively large integration timesteps for simulating materials’ response at such high strain-rate. The MD simulation results are in good agreement with the shock Hugoniot curves and with graphite-to-diamond transition obtained from both density functional theory (DFT) and experiments available in literature. We then show that thermodynamic properties of graphite from MD calculations can be used as input for a reliable equation of state to be employed in continuum simulations. Finally, we find that the size-scaling of the hypervelocity impact properties of graphene armours matches well with the DFT results and theoretical predictions of earlier studies. Our results open a concrete possibility towards accurate and fast multiscale simulation from atomistic to continuum level of shock propagation, shock-induced phase transformation, and dynamic fracture in large or hierarchical carbon systems, such as graphene-based foams and nanocomposites.

Original languageEnglish
Article number109152
JournalComputational Materials Science
Publication statusPublished - 2019 Dec

Bibliographical note

Funding Information:
This study was supported by the National Research Foundation of Korea (NRF, 2016M3D1A1900038 and 2019R1A2C4070690 ). SS acknowledges financial support from BrainKorea21 Plus Postdoc Scholarship (NRF). NMP is supported by the European Commission under the Graphene Flagship Core 2 (WP14 “Composites”, No. 785219) and the FET Proactive (“Neurofibers”, No. 732344) and by the Italian Ministry of Education, University and Research (MIUR) under the “Departments of Excellence” grant L. 232/2016, the ARS01-01384-PROSCAN Grant and the PRIN-20177TTP3S Grant.

Publisher Copyright:
© 2019 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Chemistry(all)
  • Materials Science(all)
  • Mechanics of Materials
  • Physics and Astronomy(all)
  • Computational Mathematics


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