Transferability of neural network potentials for varying stoichiometry: phonons and thermal conductivity of Mn$_x$Ge$_y$ compounds

TL;DR

This paper develops a neural network potential that accurately models the structural, vibrational, and thermal properties of Mn_xGe_y compounds across various compositions, enabling efficient study of their phononic and thermal behaviors.

Contribution

It introduces a transferable neural network potential trained on minimal data to predict properties of Mn_xGe_y materials with different stoichiometries.

Findings

Successfully reproduces structural and vibrational properties.

Accurately predicts thermal conductivity across compositions.

Enables phononic analysis in nanoscale heterostructures.

Abstract

Germanium manganese compounds exhibit a variety of stable and metastable phases with different stoichiometry. 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.