科研之友
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摘要
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.
- 出版日期2017-10-18
