Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepDFT: Neural Message Passing Network for Accurate Charge Density
  Prediction   We introduce DeepDFT, a deep learning model for predicting the electronic
charge density around atoms, the fundamental variable in electronic structure
simulations from which all ground state properties can be calculated. The model
is formulated as neural message passing on a graph, consisting of interacting
atom vertices and special query point vertices for which the charge density is
predicted. The accuracy and scalability of the model are demonstrated for
molecules, solids and liquids. The trained model achieves lower average
prediction errors than the observed variations in charge density obtained from
density functional theory simulations using different exchange correlation
functionals.

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The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
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The categories of this paper are: eess
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph