Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Atomic structure optimization with machine-learning enabled
  interpolation between chemical elements   We introduce a computational method for global optimization of structure and
ordering in atomic systems. The method relies on interpolation between chemical
elements, which is incorporated in a machine learning structural fingerprint.
The method is based on Bayesian optimization with Gaussian processes and is
applied to the global optimization of Au-Cu bulk systems, Cu-Ni surfaces with
CO adsorption, and Cu-Ni clusters. The method consistently identifies
low-energy structures, which are likely to be the global minima of the energy.
For the investigated systems with 23-66 atoms, the number of required energy
and force calculations is in the range 3-75.

29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
1
The categories of this paper are: cs
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom