Fast evaluation of interaction integrals for confined systems with machine learning

Abstract

The calculation of interaction integrals is a bottleneck for the treatment of many-body quantum systems due to its high numerical cost. We conduct configuration interaction calculations of the few-electron states confined in III-V semiconductor 2D structures using a shallow neural network to calculate the two-electron integrals, that can be used for general isotropic interaction potentials. This approach allows for a speed up of the evaluation of the energy levels and a controllable accuracy.

9 pages, 10 figures

References in corpus (4)

• Self-Learning Monte Carlo Method
• Efficient solution of Poisson's equation with free boundary conditions
• Full configuration interaction approach to the few-electron problem in artificial atoms
• Calculation of two-centre two-electron integrals over Slater-type orbitals revisited. I. Coulomb and hybrid integrals