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