Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Taming nuclear complexity with a committee of multilayer neural networks   We demonstrate that a committee of deep neural networks is capable of
predicting the ground-state and excited energies of more than 1800 atomic
nuclei with an accuracy akin to the one achieved by state-of-the-art nuclear
energy density functionals (EDFs) and a major speed-up. An active learning
strategy is proposed to train this algorithm with a minimal set of 210 nuclei.
This approach enables future fast studies of the influence of EDFs
parametrizations on structure properties over the whole nuclear chart and
suggests that for the first time an artificial intelligence successfully
encoded the laws of nuclear deformation.

7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th