Calculate electronic excited states using neural networks with effective core potential

TL;DR

This paper introduces a neural network approach combined with effective core potentials to accurately compute electronic excited states and ionization energies, surpassing traditional methods in precision for elements beyond the fourth period.

Contribution

The integration of effective core potentials with neural network quantum methods significantly improves the accuracy of excited state and ionization energy calculations for heavier elements.

Findings

Achieved high-precision energy calculations for elements Lithium to Gallium.

Successfully computed multiple excited states with accuracy comparable to experimental data.

Enhanced the applicability of neural network quantum methods to heavier elements.

Abstract

The essence of atomic structure theory, quantum chemistry, and computational materials science is solving the multi-electron stationary Schr\"odinger equation. The Quantum Monte Carlo-based neural network wave function method has surpassed traditional post-Hartree-Fock methods in precision across various systems. However, its energy uncertainty is limited to 0.01%, posing challenges in accurately determining excited states and ionization energies, especially for elements beyond the fourth period. Using effective core potentials to account for inner electrons enhances the precision of vertical excitation and ionization energies. This approach has proved effective in computing ground state energies for elements like Lithium to Gallium and in calculating energy levels and wave functions for atoms and molecules with second and fourth period elements. Additionally, by integrating effective core potentials with Ferminet, we've achieved multiple excited state calculations with a precision comparable to experimental results, marking a significant advancement in practical applications and setting a new standard for theoretical excited state calculations.