## 1 Introduction  
The development of new composites with improved mechanical properties composed of carbon structures and metal nanoparticles is of great importance. The combination of graphene with the most widespread metals, such as aluminum, nickel, copper, titanium, and silver, has been intensively studied over recent decades [1; 2; 3; 4], is crumpled graphene (CG), which has recently been actively investigated for energy conservation applications [5; 6]. The design of this lightweight three-dimensional graphene assembly and its mechanical properties were described in [7]. Copper (Cu) is widely used due to its inherent thermal conductivity and low cost [8]. Despite the widespread use of Cu, there is still a need to improve its mechanical, tribological, and thermal performance in various industries. Previously, it was established that graphene reinforcement of Cu increased its mechanical properties, while simultaneously maintaining high electrical conductivity [9]. It was shown that graphene decreased the thermal expansion coefficient compared to pure Cu, which is better for electronic applications [10; 11; 12]. Due to the improved tribological properties, copper-graphene composites have been proposed for use as coatings [13; 14; 15]. High strength and ductility were demonstrated for copper matrix composites within a graphene network [16; 17; 18]. The large-scale atomic/molecular massively parallel simulator (LAMMPS) can be used for any type of molecular dynamic (MD) simulation [2; 5; 14; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. The MD simulation method is an effective tool for understanding the interconnection between the structure and properties of a material and the interactions between different structural elements [36; 37; 38; 19; 38]. For the MD simulation of graphene/metal interaction, a suitable interatomic potential must be chosen to represent the particular issue under investigation. Interatomic potentials store basic information about the physical and chemical properties of the system [39; 40]. On the one hand, it is very important to choose a potential that will describe the interaction between different materials as accurately as possible. On the other hand, computational costs need to be minimized. In large-scale atomistic modeling of complex systems, potentials, such as the analytical bond-order potential (BOP), the embedded atom method (EAM), and the Morse potential have been used successfully [20; 21; 22]. The simulation of Cu/graphene composites has also been conducted with more complicated potentials, such as the charge-optimized many-body potential (COMB) [41; 42], for example, to study hydrocarbons on Cu surfaces [41] or the surface oxidation of Cu [43]. This potential has also been used to study graphene on a Cu surface with H and O atoms [23]. However, simulating interactions in graphene/metal systems remains challenging. The obtained adhesion energy, solubility, and catalytic properties of different metals in contact with carbon are quite different [44]. For example, the adhesion energy of graphene on a Cu substrate has been calculated experimentally as 0.72 J/m\({}^{2}\)[45] and 12.75 J/m\({}^{2}\)[46], and 0.40 J/m\({}^{2}\)[47] from first principles. In [48], the adhesion energy of graphene membranes on different substrates was measured experimentally at nano-scale using a nano-scratch method. The difference in system parameters is understandable since interatomic potentials have been developed to solve a range of problems, including graphene growth on a metal substrate, strengthening of a metal matrix by graphene coverage or graphene inclusions, and the study of thermal conductivity or mechanical properties. Even different atomic positioning of Me (shown for Ni) atoms on graphene surfaces results in different interactions [24; 49].