Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Transferability of neural network potentials for varying stoichiometry:
  phonons and thermal conductivity of Mn$_x$Ge$_y$ compounds   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.

35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex