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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. [ABSTRACT FROM AUTHOR]

In this work, we investigated the tensile and compression forces effect on the thermal conductivity of silicon. We used the equilibrium molecular dynamics approach for the evaluation of thermal conductivity considering different interatomic potentials. More specifically, we tested Stillinger-Weber, Tersoff, Environment-Dependent Interatomic Potential, and Modified Embedded Atom Method potentials for the description of silicon atom motion under different strain and temperature conditions. It was shown that the Tersoff potential gives a correct trend of the thermal conductivity with the hydrostatic strain, while other potentials fail, especially when the compression strain is applied. Additionally, we extracted phonon density of states and dispersion curves from molecular dynamics simulations. These data were used for direct calculations of the thermal conductivity considering the kinetic theory approach. Comparison of molecular dynamics and kinetic theory simulations results as a function of strain and temperature allowed us to investigate the different factors affecting the thermal conductivity of the strained silicon. [ABSTRACT FROM AUTHOR]