| Download | - View final version: Deep learning and the Schrödinger equation (PDF, 2.7 MiB)
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| DOI | Resolve DOI: https://doi.org/10.1103/PhysRevA.96.042113 |
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| Author | Search for: Mills, Kyle1; Search for: Spanner, Michael1; Search for: Tamblyn, Isaac1 |
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| Name affiliation | - National Research Council of Canada. Security and Disruptive Technologies
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| Format | Text, Article |
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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 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. |
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| Publication date | 2017-10-18 |
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| Publisher | American Physical Society |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 23002689 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 672e419f-a979-4a7a-8ed5-bcfa4586d306 |
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| Record created | 2017-12-20 |
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| Record modified | 2020-05-30 |
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