Non-equilibrium Molecular Dynamics for Calculating the Thermal Conductivity of Graphene-Coated Aluminum

Toprak, Kasim; Yılmaz, Ahmet Berk

doi:10.34088/kojose.663888

Cited by 3 publications

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References 28 publications

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“…Since graphene also has much greater current density and thermal conductivity values than copper, copper overcomes this issue and provides the characteristics and opportunities to deliver the required thermal management by incorporating graphene. On the other hand, the orientation of graphene in composites significantly alters the thermal properties of graphenemetal materials, bringing different enhancements in nanocomposites [16][17][18][19][20][21]. Further studies are required to investigate the contribution of graphene to the thermal conductivity of graphene-copper nanomaterials.…”

Section: Introductionmentioning

confidence: 99%

Understanding neural network tuned Langevin thermostat effect on predicting thermal conductivity of graphene-coated copper using nonequilibrium molecular dynamics simulations

Toprak

2024

Modelling Simul. Mater. Sci. Eng.

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Copper has always been used in thermoelectric applications due to its extensive properties among metals. However, it requires further improving its heat transport performance at the nanosized applications by supporting another high thermal conductivity material. Herein, copper was coated with graphene, and the neural network fitting was employed for the nonequilibrium molecular dynamics simulations of graphene-coated copper nanomaterials to predict thermal conductivity. The Langevin thermostat that was tuned with a neural network fitting, which makes up the backbone of deep learning, generated the temperature difference between the two ends of the models. The neural network fitting calibrated the Langevin thermostat damping constants that helped to control the temperatures precisely. The buffer and thermostat lengths were also analyzed, and they have considerable effects on the thermostat temperatures and a significant impact on the thermal conductivity of the graphene-coated copper. Regarding thermal conductivity, the four different shapes of vacancy defect concentrations and their locations in the graphene sheets were further investigated. The vacancy between the thermostats significantly decreases the thermal conductivity; however, the vacancy defect in thermostats does not have a similar effect. When the graphene is placed between two copper blocks, the thermal conductivity decreases drastically, and it continues to drop when the sine wave amplitude on the graphene sheet increases.

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Section: Introductionmentioning

confidence: 99%

Understanding neural network tuned Langevin thermostat effect on predicting thermal conductivity of graphene-coated copper using nonequilibrium molecular dynamics simulations

Toprak

2024

Modelling Simul. Mater. Sci. Eng.

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Tersoff and REBO Potentials

Kumar

Singh

2022

Forcefields for Atomistic-Scale Simulations: Materials and Applications

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Genetic algorithm optimization of langevin thermostat and thermal properties of graphene-aluminum nanocomposites: a molecular dynamics

Toprak

2024

Modelling Simul. Mater. Sci. Eng.

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The thermal properties of a laminated structure of graphene-coated aluminum composite nanomaterial were investigated through non-equilibrium molecular dynamics (NEMD) simulations to address the problem of temperature deviation in the thermostat volume applied. This paper presents a new insight into the best values of timestep and Langevin thermostat damping parameters for each atom in the nanomaterial with different size configurations using the genetic algorithm (GA) method by considering the timestep and thermostat damping parameters for each atom type, as well as the thickness of the nanomaterial, the thermostat, buffer, and heat flow lengths. The initial population results indicate that the thermostat temperature deviation increases with higher thermostat damping coefficients and timestep. However, the deviation decreases significantly with increased heat flow and thermostat lengths. Variations in buffer length and aluminum thickness do not have a significant effect on temperature. The application of a GA for optimization leads to a decrease in thermostat temperature deviation. The optimized parameters resulted in better thermostat temperature deviations when analyzing the temperature, aluminum thickness, and both buffer and thermostat lengths. Additionally, the thermal conductivity of aluminum-graphene nanomaterial decreases with increasing temperature, buffer length, and aluminum thickness, but increases by up to 9.85% with increasing thermostat length.

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Non-equilibrium Molecular Dynamics for Calculating the Thermal Conductivity of Graphene-Coated Aluminum

Cited by 3 publications

References 28 publications

Understanding neural network tuned Langevin thermostat effect on predicting thermal conductivity of graphene-coated copper using nonequilibrium molecular dynamics simulations

Understanding neural network tuned Langevin thermostat effect on predicting thermal conductivity of graphene-coated copper using nonequilibrium molecular dynamics simulations

Tersoff and REBO Potentials

Genetic algorithm optimization of langevin thermostat and thermal properties of graphene-aluminum nanocomposites: a molecular dynamics

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