  Finding accurate solutions to the electronic Schr\"odinger equation plays an
important role in discovering important molecular and material energies and
characteristics. Consequently, solving systems with large numbers of electrons
has become increasingly important. Variational Monte Carlo (VMC) methods,
especially those approximated through deep neural networks, are promising in
this regard. In this paper, we aim to integrate one such model called the
FermiNet, a post-Hartree-Fock (HF) Deep Neural Network (DNN) model, into a
standard and widely used open source library, DeepChem. We also propose novel
initialization techniques to overcome the difficulties associated with the
assignment of excess or lack of electrons for ions.
