Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gaussian approximation potential modeling of lithium intercalation in
  carbon nanostructures   We demonstrate how machine-learning based interatomic potentials can be used
to model guest atoms in host structures. Specifically, we generate Gaussian
approximation potential (GAP) models for the interaction of lithium atoms with
graphene, graphite, and disordered carbon nanostructures, based on reference
density-functional theory (DFT) data. Rather than treating the full Li--C
system, we demonstrate how the energy and force differences arising from Li
intercalation can be modeled and then added to a (prexisting and unmodified)
GAP model of pure elemental carbon. Furthermore, we show the benefit of using
an explicit pair potential fit to capture "effective" Li--Li interactions, to
improve the performance of the GAP model. This provides proof-of-concept for
modeling guest atoms in host frameworks with machine-learning based potentials,
and in the longer run is promising for carrying out detailed atomistic studies
of battery materials.

25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess