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2 TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

2 2020

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Molecular Dynamics Simulations of Thermal Conductivity of Penta-Graphene

Muna Isyna, Winczewski Szymon

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

2020

Vol. 24, No 3

191--220

The thermal conductivity of penta-graphene (PG), a new two dimensional carbon allotrope and its dependence on temperature, strain, and direction are studied in this paper. The thermal conductivity of PG is investigated using a non-equilibrium molecular dynamics simulation (NEMD) with the Two Region Method by applying the optimized Tersoff interatomic potential. Our study shows that the thermal conductivity of PG (determined for the [100] direction) at the room temperature of 300 K is about 18.7 W/(m K), which is much lower than the thermal conductivity of graphene. As the temperature increases, the thermal conductivity of PG is decreasing because, unlike graphene, PG has lower phonon group velocities and few collective phonon excitations. The obtained dependence of the thermal conductivity on the temperature can be described as κ ∼ T −0.32. For the [110] direction the thermal conductivity at the room temperature of 300 K is very similar: about 17.8 W/(m K). In this case, the temperature dependence follows the κ ∼ T −0.3 relation. Our investigations reveal that the thermal conductivity of PG is isotropic, meaning that heat transport behavior is independent of the heat flow direction. Our results indicate that the thermal conductivity of PG depends in an interesting way on the applied strain: nonmonotonic up-and-down behavior is observed. The thermal conductivity increases between strains from 0% up to 12.5%, and it decreases above a strain of 12.5%. Our investigation highlights the fascinating thermal transport properties of penta-graphene. The ultra-low thermal conductivity, the decreasing thermal conductivity with the increasing temperature, and the ultra-high mechanical strength of PG show that PG possesses a great potential in thermoelectric and nanomechanics applications. We hope that these findings, made by means of simulations, will become a bridge to inspire and encourage the experimental works, especially in the synthesis of PG.

Application of the neural networks for developing new parametrization of the Tersoff potential for carbon

Nwachukwu Anthony Chukwuemeka, Winczewski Szymon

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

2020

Vol. 24, No 4

299--333

Penta-graphene (PG) is a 2D carbon allotrope composed of a layer of pentagons having sp2- and sp3- bonded carbon atoms. A study carried out in 2018 has shown that the parameterization of the Tersoff potential proposed in 2005 by Ehrhart and Able (T05 potential) performs better than other potentials available for carbon, being able to reproduce structural and mechanical properties of the PG. In this work, we tried to improve the T05 potential by searching for its parameters giving a better reproduction of the structural and mechanical properties of the PG known from the ab initio calculations. We did this using Molecular Statics (MS) simulations and Neural Network (NN). Our test set consisted of the following structural properties: the lattice parameter a; the interlayer spacing h; two lengths of C-C bonds, d1 and d2 respectively; two valence angles, O1 and )2, respectively. We also examined the mechanical properties by calculating three elastic constants, C11, C12 and C66, and two elastic moduli, the Young’s modulus E and the Poisson’s ratio v. We used MS technique to compute the structural and mechanical properties of PG at T =0 K. The Neural Network used is composed of 2 hidden layers, with 20 and 10 nodes for the first and second layer, respectively. We used an Adams optimizer for the NN optimization and the Mean Squared Error as the loss function. We obtained inputs (about 80 000 different sets of potential parameters) for the Molecular Statics simulation by using randomly generated numbers. The outputs from these simulations became the inputs to our Neural Network. The Molecular Statics simulations were done with LAMMPS while the Neural Network and other computations were done with Python, Pytorch, Numpy, Pandas, GNUPLOT and Bash scripts. We obtained a parameterization which has a slightly better accuracy (lower relative errors of the calculated structural and mechanical properties) than the original parameterization.