Lattice thermal conductivity and Young's modulus of XN4 (X = Be, Mg and Pt) 2D materials using machine learning interatomic potentials.

The newly synthesized BeN4 monolayer has introduced a novel group of 2D materials called nitrogen-rich 2D materials. In the present study, the anisotropic mechanical and thermal properties of three members of this group, BeN4, MgN4, and PtN4, are investigated. To this end, a machine learning-based interatomic potential (MLIP) is developed and utilized in classical molecular dynamics (MD) simulations. Mechanical properties are calculated by extracting the stress-strain curve and thermal properties by the non-equilibrium molecular dynamics (NEMD) method. The acquired results show the anisotropic Young's modulus and lattice thermal conductivity of these materials. Generally, the Young's modulus and thermal conductivity in the armchair direction are higher than in the zigzag direction. Also, the anisotropy of Young's modulus is almost constant at every temperature for BeN4 and MgN4, while for PtN4, this parameter is decreased by increasing the temperature. The findings of this research are not only evidence of the application of machine learning in MD simulations, but also provide information on the basic anisotropic mechanical and thermal properties of these newly discovered 2D nanomaterials.

Nonreciprocal forces enable cold-to-hot heat transfer between nanoparticles.

We study the heat transfer between two nanoparticles held at different temperatures that interact through nonreciprocal forces, by combining molecular dynamics simulations with stochastic thermodynamics. Our simulations reveal that it is possible to construct nano refrigerators that generate a net heat transfer from a cold to a hot reservoir at the expense of power exerted by the nonreciprocal forces. Applying concepts from stochastic thermodynamics to a minimal underdamped Langevin model, we derive exact analytical expressions predictions for the fluctuations of work, heat, and efficiency, which reproduce thermodynamic quantities extracted from the molecular dynamics simulations. The theory only involves a single unknown parameter, namely an effective friction coefficient, which we estimate fitting the results of the molecular dynamics simulation to our theoretical predictions. Using this framework, we also establish design principles which identify the minimal amount of entropy production that is needed to achieve a certain amount of uncertainty in the power fluctuations of our nano refrigerator. Taken together, our results shed light on how the direction and fluctuations of heat flows in natural and artificial nano machines can be accurately quantified and controlled by using nonreciprocal forces.

Thermal transport at a nanoparticle-water interface: A molecular dynamics and continuum modeling study.

Heat transfer between a silver nanoparticle and surrounding water has been studied using molecular dynamics (MD) simulations. The thermal conductance (Kapitza conductance) at the interface between a nanoparticle and surrounding water has been calculated using four different approaches: transient with/without temperature gradient (internal thermal resistance) in the nanoparticle, steady-state non-equilibrium, and finally equilibrium simulations. The results of steady-state non-equilibrium and equilibrium are in agreement but differ from the transient approach results. MD simulation results also reveal that in the quenching process of a hot silver nanoparticle, heat dissipates into the solvent over a length-scale of ∼2 nm and over a time scale of less than 5 ps. By introducing a continuum solid-like model and considering a heat conduction mechanism in water, it is observed that the results of the temperature distribution for water shells around the nanoparticle agree well with the MD results. It is also found that the local water thermal conductivity around the nanoparticle is greater by about 50% than that of bulk water. These results have important implications for understanding heat transfer mechanisms in nanofluid systems and also for cancer photothermal therapy, wherein an accurate local description of heat transfer in an aqueous environment is crucial.