  The rapid development of deep learning techniques has driven the emergence of
a neural network-based variational Monte Carlo method (referred to as
FermiNet), which has manifested high accuracy and strong predictive power in
the electronic structure calculations of atoms, molecules as well as some
periodic systems. Recently, the implementation of the effective core potential
(ECP) scheme in it further facilitates more efffcient calculations in practice.
But there still lack comprehensive assessments on the ECP's performance under
the FermiNet. In this work, we set sail to ffll this gap by conducting
extensive tests on the ffrst two row elements regarding their atomic spectral
and molecular properties. Our major ffnding is that in general the qualities of
ECPs have been correctly reffected under the FermiNet. Two recently built ECP
tables, namely ccECP and eCEPP, seem to prevail on the overall performance.
Speciffcally, ccECP performs slightly better on the spectral precision and
covers more elements, while eCEPP is more systematically built from both shape
and energy consistency, and better treats the core polarisation. On the other
hand, the high accuracy of the all-electron calculations is hindered by the
absence of relativistic effects as well as the numerical instabilities in some
heavier elements. Finally, with further in-depth discussions, we generate
possible directions for developing and improving the FermiNet in the near
future.
