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arXiv · 1702.01361

Deep learning and the Schrödinger equation

Kyle Mills·Michael Spanner·Isaac Tamblyn
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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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BibTeXRIS

Kyle Mills, Michael Spanner, Isaac Tamblyn. 2017-11-03. Deep learning and the Schrödinger equation. https://doi.org/10.1103/physreva.96.042113

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