Deep learning and the Schrödinger equation

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DOI	Resolve DOI: https://doi.org/10.1103/PhysRevA.96.042113
Author	Search for: Mills, Kyle¹; Search for: Spanner, Michael¹; Search for: Tamblyn, Isaac¹
Name affiliation	National Research Council Canada. Security and Disruptive Technologies
Format	Text
Type	Article
Journal title	Physical Review A
ISSN	2469-9926 2469-9934
Volume	96
Issue	4
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 model was able to predict the ground-state energy to within chemical accuracy, with a median absolute error of 1.49 mHa. We also investigated the performance of the model in predicting other quantities such as the kinetic energy and the first excited-state energy.
Publication date	2017-10-18
Publisher	American Physical Society
Related data	Quantum simulations of an electron in a two dimensional potential well [structured dataset]
Language	English
Peer reviewed	Yes
NPARC number	23002689
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Record identifier	672e419f-a979-4a7a-8ed5-bcfa4586d306
Record created	2017-12-20
Record modified	2020-03-16