  High-precision atomic structure calculations require accurate modelling of
electronic correlations typically addressed via the configuration interaction
(CI) problem on a multiconfiguration wave function expansion. The latter can
easily become challenging or infeasibly large even for advanced supercomputers.
Here we develop a deep-learning approach which allows to preselect the most
relevant configurations out of large CI basis sets until the targeted energy
precision is achieved. The large CI computation is thereby replaced by a series
of smaller ones performed on an iteratively expanding basis subset managed by a
neural network. While dense architectures as used in quantum chemistry fail, we
show that a convolutional neural network naturally accounts for the physical
structure of the basis set and allows for robust and accurate CI calculations.
The method was benchmarked on basis sets of moderate size allowing for the
direct CI calculation, and further demonstrated on prohibitively large sets
where the direct computation is not possible.
