  We propose the concept of machine learning configuration interaction (MLCI)
whereby an artificial neural network is trained on-the-fly to predict important
new configurations in an iterative selected configuration interaction
procedure. We demonstrate that the neural network can discriminate between
important and unimportant configurations, that it has not been trained on, much
better than by chance. MLCI is then used to find compact wavefunctions for
carbon monoxide at both stretched and equilibrium geometries. We also consider
the multireference problem of the water molecule with elongated bonds. Results
are contrasted with those from other ways of selecting configurations:
first-order perturbation, random selection and Monte Carlo configuration
interaction. Compared with these other serial calculations, this prototype MLCI
is competitive in its accuracy, converges in significantly fewer iterations
than the stochastic approaches, and requires less time for the higher-accuracy
computations.
