Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Full Configuration Interaction Electron Correlations with Deep
  Learning   In this report, we present a deep learning framework termed the Electron
Correlation Potential Neural Network (eCPNN) that can learn succinct and
compact potential functions. These functions can effectively describe the
complex instantaneous spatial correlations among electrons in many--electron
atoms. The eCPNN was trained in an unsupervised manner with limited information
from Full Configuration Interaction (FCI) one--electron density functions
within predefined limits of accuracy. Using the effective correlation potential
functions generated by eCPNN, we can predict the total energies of each of the
studied atomic systems with a remarkable accuracy when compared to FCI
energies.

18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math