A Neural-Network-Based Selective Configuration Interaction Approach to Molecular Electronic Structure

Understand

By combining Hartree-Fock with a neural-network-supported quantum-cluster solver proposed recently in the context of solid-state lattice models, we formulate a scheme for selective neural-network configuration interaction (NNCI) calculations and implement it with various options for the type of basis set and boundary conditions.

• The method's performance is evaluated in studies of several small molecules as a step toward calculations of larger systems.
• In particular, the correlation energy in the N$_2$ molecule is compared with published full CI calculations that included nearly $10^{10}$ Slater determinants, and the results are reproduced with only $4\cdot10^{5}$ determinants using NNCI.
• A clear advantage is seen from increasing the set of orbitals included rather than approaching full CI for a smaller set.