학술논문

Transferability of neural network potentials for varying stoichiometry: Phonons and thermal conductivity of Mnx Gey compounds
Document Type
article
Source
Journal of Applied Physics. 127(24)
Subject
cond-mat.mtrl-sci
Applied Physics
Mathematical Sciences
Physical Sciences
Engineering
Language
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 xGe 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.