Solving Multi-Dimensional Schr\"{o}dinger Equations Based on EPINNs
Jinde Liu, Chen Yang, and Gang Jiang

TL;DR
This paper introduces a neural network-based numerical method for solving multi-dimensional Schr"{o}dinger equations, accurately computing multiple excited states and eigenvalues with improved precision and efficiency, applicable to complex quantum systems.
Contribution
It presents a novel neural network approach incorporating orthogonal normalization and energy-based loss to accurately solve multi-dimensional Schr"{o}dinger equations for multiple excited states.
Findings
Achieves at least an order of magnitude higher accuracy than previous methods.
Effectively computes excited states of hydrogen-like atoms.
Demonstrates potential for multi-electron atomic molecule solutions.
Abstract
Due to the good performance of neural networks in high-dimensional and nonlinear problems, machine learning is replacing traditional methods and becoming a better approach for eigenvalue and wave function solutions of multi-dimensional Schr\"{o}dinger equations. This paper proposes a numerical method based on neural networks to solve multiple excited states of multi-dimensional stationary Schr\"{o}dinger equation. We introduce the orthogonal normalization condition into the loss function, use the frequency principle of neural networks to automatically obtain multiple excited state eigenfunctions and eigenvalues of the equation from low to high energy levels, and propose a degenerate level processing method. The use of equation residuals and energy uncertainty makes the error of each energy level converge to 0, which effectively avoids the order of magnitude interference of error…
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Taxonomy
TopicsSpectroscopy and Laser Applications
