Deep learning and the Schrödinger equation

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Preprint

8 June 2017

preprint
Published by arXiv in arXiv

DOI:https://doi.org/10.48550/arXiv.1702.01361

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.

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All Related Versions

Version 1, 2017-02-05, ArXiv
Version 2, 2017-06-08, ArXiv
Version 3, 2017-11-03, ArXiv
Published version: Physical Review A, 96 (4), 042113.

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