A deep neural network with energy as the loss function solves the two-body deuteron and a three-channel triton model, matching analytic and Gaussian-expansion benchmarks.
Multi-body wave function of ground and low-lying excited states using unornamented deep neural networks
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abstract
We propose a method to calculate wave functions and energies not only of the ground state but also of low-lying excited states using a deep neural network and the unsupervised machine learning technique. For systems composed of identical particles, a simple method to perform symmetrization for bosonic systems and antisymmetrization for fermionic systems is also proposed.
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Solving two and three-body systems with deep neural networks
A deep neural network with energy as the loss function solves the two-body deuteron and a three-channel triton model, matching analytic and Gaussian-expansion benchmarks.