  An artificial neural network (ANN) is investigated as a tool for estimating
rate coefficients for the collisional excitation of molecules. The performance
of such a tool can be evaluated by testing it on a dataset of
collisionally-induced transitions for which rate coefficients are already
known: the network is trained on a subset of that dataset and tested on the
remainder. Results obtained by this method are typically accurate to within a
factor ~ 2.1 (median value) for transitions with low excitation rates and ~ 1.7
for those with medium or high excitation rates, although 4% of the ANN outputs
are discrepant by a factor of 10 more. The results suggest that ANNs will be
valuable in extrapolating a dataset of collisional rate coefficients to include
high-lying transitions that have not yet been calculated. For the asymmetric
top molecules considered in this paper, the favored architecture is a
cascade-correlation network that creates 16 hidden neurons during the course of
training, with 3 input neurons to characterize the nature of the transition and
one output neuron to provide the logarithm of the rate coefficient.
