Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Analysis of Kohn-Sham Eigenfunctions Using a Convolutional Neural
  Network in Simulations of the Metal-insulator Transition in Doped
  Semiconductors   Machine learning has recently been applied to many problems in condensed
matter physics. A common point of many proposals is to save computational cost
by training the machine with data from a simple example and then using the
machine to make predictions for a more complicated example. Convolutional
neural networks (CNN), which are one of the tools of machine learning, have
proved to work well for assessing eigenfunctions in disordered systems. Here we
apply a CNN to assess Kohn-Sham eigenfunctions obtained in density functional
theory (DFT) simulations of the metal-insulator transition of a doped
semiconductor. We demonstrate that a CNN that has been trained using
eigenfunctions from a simulation of a doped semiconductor that neglects
electron spin successfully predicts the critical concentration when presented
with eigenfunctions from simulations that include spin.

11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
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The categories of this paper are: stat
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
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The categories of this paper are: math-ph
1
The categories of this paper are: cs