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

Abstract

Germanium manganese compounds exhibit a variety of stable and metastable phases with different stoichiometries. These materials entail interesting electronic, magnetic, and thermal properties both in their bulk form and as heterostructures. Here, we develop and validate a transferable machine learning potential, based on the high-dimensional neural network formalism, to enable the study of Mn x Ge y materials over a wide range of compositions. We show that a neural network potential fitted on a minimal training set reproduces successfully the structural and vibrational properties and the thermal conductivity of systems with different local chemical environments, and it can be used to predict phononic effects in nanoscale heterostructures.

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].…”

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 [58][59][60][61], diamond and amorphous silicon [62][63][64], multilayer graphene [65], monolayer silicene [66], CoSb 3 [67], monolayer MoS 2 and MoSe 2 and their alloys [68], C 3 N [69], α-Ag 2 Se [70,71], β-Ga 2 O 3 [72], Tl 3 VSe 4 [56], PbTe [56], and SnSe [73]. 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 [74][75][76][77][78][79].…”

High-Throughput Computations of Cross-Plane Thermal Conductivity in Multilayer Stanene

“…This MLIPs have also been used to predict other 2D materials such as nanoporous C3N4, C3N5 and C3N6 nanosheets. [9][10][11] Other types of machine learning based potentials have also been developed to predict the thermal properties of MnxGey compounds, [12] silicon, [13] silicene, [14] WSe2, [15] and MoS2(1-x)Se2x [16].…”