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The atom sampling method for mesoscale molecular dynamics and its application to graphene assemblies.

Xiong Pan1, Hanhui Jin1,2, Xiaoke Ku1,2

  • 1School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China. enejhh@emb.zju.edu.cn.

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This study introduces an atom sampling method to improve coarse-grained (CG) models for graphene simulations. The new Tersoff sampling model enhances accuracy in predicting mechanical behaviors of graphene structures.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Nanotechnology

Background:

  • Coarse-grained (CG) molecular dynamics models are crucial for simulating mesoscale graphene structures due to experimental and atomistic limitations.
  • Existing CG models face challenges with bead mass, leading to energy mismatches and inaccurate mechanical predictions.
  • Graphene's potential in nanostructures necessitates efficient and accurate simulation methods.

Purpose of the Study:

  • To propose and validate an atom sampling method for mesoscale molecular dynamics simulations.
  • To develop a Tersoff sampling model for coarsening graphene in planar directions.
  • To mitigate energy mismatches in CG models and improve the prediction of graphene's mechanical properties.

Main Methods:

  • Development of an atom sampling method to address increased bead mass in CG models.
  • Creation of a Tersoff sampling model for planar graphene coarsening.
  • Simulation of mechanical responses in monolayer and multilayer graphene using the developed model, comparing results with all-atom (AA) and existing CG models.

Main Results:

  • The Tersoff sampling model effectively reduces kinetic and potential energy misfit compared to traditional CG models.
  • Temperature changes in simulations more accurately reflect graphene's mechanical behavior in elastic and fracture zones.
  • The atom sampling method demonstrates broad applicability to fluids and crystal structures.

Conclusions:

  • The proposed atom sampling method enhances the accuracy of mesoscale simulations for graphene.
  • The Tersoff sampling model provides a more reliable approach for predicting graphene's mechanical properties.
  • This method offers a computationally efficient yet accurate alternative for simulating various materials.