Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multiscale machine-learning interatomic potentials for ferromagnetic and
  liquid iron   We develop and compare four interatomic potentials for iron: a simple
machine-learned embedded atom method (EAM) potential, a potential with
machine-learned two- and three-body-dependent terms, a potential with
machine-learned EAM and three-body terms, and a Gaussian approximation
potential with the SOAP descriptor. All potentials are trained to the same
diverse database of body-centered cubic and liquid structures computed with
density functional theory. The four presented potentials represent different
levels of complexity and span three orders of magnitude in computational cost.
The first three potentials are tabulated and evaluated efficiently using cubic
spline interpolations, while the fourth one is implemented without additional
optimization. We compare and discuss the advantages of each implementation,
transferability and applicability in terms of the balance between required
accuracy versus computational cost.

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The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math