Analysis of Kohn-Sham Eigenfunctions Using a Convolutional Neural Network in Simulations of the Metal-insulator Transition in Doped Semiconductors

TL;DR

This paper demonstrates that a convolutional neural network trained on simplified density functional theory data can accurately predict the critical concentration for the metal-insulator transition in doped semiconductors, even when applied to more complex spin-inclusive simulations.

Contribution

It introduces a CNN-based approach to assess Kohn-Sham eigenfunctions in DFT simulations of the metal-insulator transition, showing transferability across different simulation complexities.

Findings

CNN trained on simplified data predicts critical concentration accurately.

Model generalizes to simulations including electron spin.

Machine learning reduces computational effort in transition analysis.

Abstract

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.