Atomistic simulations of thermal conductivity in GeTe nanowires

Abstract

The thermal conductivity of GeTe crystalline nanowires has been computed by means on non-equilibrium molecular dynamics simulations employing a machine learning interatomic potential. This material is of interest for application in phase change nonvolatile memories. The resulting lattice thermal conductivity of an ultrathin nanowire (7.3 nm diameter) of 1.57 W/mK is sizably lower than the corresponding bulk value of 3.15 W/mK obtained within the same framework. The analysis of the phonon dispersion relations and lifetimes reveals that the lower thermal conductivity in the nanowire is mostly due to a reduction in the phonon group velocities. We further predict the presence of a minimum in the lattice thermal conductivity for thicker nanowires.

Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport

“…MD simulations with ML potentials have been applied to study heat transport properties of a number of materials, including, e.g., GeTe and MnGe compounds [61][62][63][64], diamond and amorphous silicon [65][66][67], multilayer graphene [68], monolayer silicene [69], CoSb 3 [70], monolayer MoS 2 and MoSe 2 and their alloys [71], C 3 N [72], α-Ag 2 Se [73,74], β-Ga 2 O 3 [75], Tl 3 VSe 4 [59], PbTe [59], and SnSe [76]. There are also works that exclusively used the Boaltzmann transport equation (BTE) approach to calculate thermal conductivity based on force constants determined from ML potentials [77][78][79][80][81][82].…”

Transferability of neural network potentials for varying stoichiometry: Phonons and thermal conductivity of MnxGey compounds

“…A similar approach was employed to fit a NNP for GeTe, 51 showing excellent performance in describing the structure of its liquid and amorphous phases and the crystallization mechanism, 15,52 as well as the structure and vibrational properties of nanowires. 53 Whereas in principle, it would be possible to use larger NN, with more layers or more nodes per layer, that would mean adding even more parameters to fit, thus making it very difficult to achieve accuracy with a limited training set. Fitting a reliable NNP requires a comprehensive training database.…”

Mechanism of amorphous phase stabilization in ultrathin films of monoatomic phase change material

“…The same framework was exploited previously to generate an interatomic potential for the phase change compound GeTe 19 which allowed us to address several properties ranging from dynamical heterogeneity 20 and fast crystallization [21][22][23] in the supercooled liquid to structural relaxations and thermal transport in the amorphous phase in the bulk [24][25][26] and in nanowires. 27,28 2. Computational details Molecular dynamics simulations have been performed with an interatomic potential generated with NN scheme of ref.…”