Deep learning and the Schr\"odinger equation
Materials Science
2017-11-06 v3 Machine Learning
Chemical Physics
Abstract
We have trained a deep (convolutional) neural network to predict the ground-state energy of an electron in four classes of confining two-dimensional electrostatic potentials. On randomly generated potentials, for which there is no analytic form for either the potential or the ground-state energy, the neural network model was able to predict the ground-state energy to within chemical accuracy, with a median absolute error of 1.49 mHa. We also investigate the performance of the model in predicting other quantities such as the kinetic energy and the first excited-state energy of random potentials.
Cite
@article{arxiv.1702.01361,
title = {Deep learning and the Schr\"odinger equation},
author = {Kyle Mills and Michael Spanner and Isaac Tamblyn},
journal= {arXiv preprint arXiv:1702.01361},
year = {2017}
}