English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Germanium manganese compounds exhibit a variety of stable and metastablephases with different stoichiometry. These materials entail interestingelectronic, magnetic and thermal properties both in their bulk form and asheterostructures. Here we develop and validate a transferable machine learningpotential, based on the high-dimensional neural network formalism, to enablethe study of Mn$_x$Ge$_y$ materials over a wide range of compositions. We showthat a neural network potential fitted on a minimal training set reproducessuccessfully the structural and vibrational properties and the thermalconductivity of systems with different local chemical environments, and it canbe used to predict phononic effects in nanoscale heterostructures.

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