  The ground state of second-quantized quantum chemistry Hamiltonians provides
access to an important set of chemical properties. Wavefunctions based on ML
architectures have shown promise in approximating these ground states in a
variety of physical systems. In this work, we show how to achieve
state-of-the-art energies for molecular Hamiltonians using the the neural
network backflow wave-function. To accomplish this, we optimize this ansatz
with a variant of the deterministic optimization scheme based on SCI introduced
by [Li, et. al JCTC (2023)] which we find works better than standard MCMC
sampling. For the molecules we studied, NNBF gives lower energy states than
both CCSD and other neural network quantum states. We systematically explore
the role of network size as well as optimization parameters in improving the
energy. We find that while the number of hidden layers and determinants play a
minor role in improving the energy, there is significant improvements in the
energy from increasing the number of hidden units as well as the batch size
used in optimization with the batch size playing a more important role.
