  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.
