Fermionic Neural Network with Effective Core Potential

TL;DR

This paper enhances neural network-based electronic structure calculations by integrating FermiNet with effective core potentials, enabling accurate modeling of complex transition metal systems.

Contribution

It introduces a novel combination of FermiNet with effective core potentials to improve accuracy and efficiency for large, challenging atomic systems.

Findings

Accurate ground state energies for 3d transition metal atoms and monoxides.

Results are consistent with experimental data and state-of-the-art methods.

Demonstrates potential for broader application of deep learning in electronic structure calculations.

Abstract

Deep learning techniques have opened a new venue for electronic structure theory in recent years. In contrast to traditional methods, deep neural networks provide much more expressive and flexible wave function ansatz, resulting in better accuracy and time scaling behavior. In order to study larger systems while retaining sufficient accuracy, we integrate a powerful neural-network based model (FermiNet) with the effective core potential method, which helps to reduce the complexity of the problem by replacing inner core electrons with additional semi-local potential terms in Hamiltonian. In this work, we calculate the ground state energy of 3d transition metal atoms and their monoxide which are quite challenging for original FermiNet work, and the results are in good consistency with both experimental data and other state-of-the-art computational methods. Our development is an important step for a broader application of deep learning in the electronic structure calculation of molecules and materials.