  The concept of machine learning configuration interaction (MLCI) [J. Chem.
Theory Comput. 2018, 14, 5739], where an artificial neural network (ANN) learns
on the fly to select important configurations, is further developed so that
accurate ab initio potential energy curves can be efficiently calculated. This
development includes employing the artificial neural network also as a hash
function for the efficient deletion of duplicates on the fly so that the
singles and doubles space does not need to be stored and this barrier to
scalability is removed. In addition configuration state functions are
introduced into the approach so that pure spin states are guaranteed, and the
transferability of data between geometries is exploited. This improved approach
is demonstrated on potential energy curves for the nitrogen molecule, water,
and carbon monoxide. The results are compared with full configuration
interaction values, when available, and different transfer protocols are
investigated. It is shown that, for all of the considered systems, accurate
potential energy curves can now be efficiently computed with MLCI. For the
potential curves of N$_{2}$ and CO, MLCI can achieve lower errors than
stochastically selecting configurations while also using substantially less
processor hours.
