The electronic correlation energy of diatomic molecules and heavy
atoms is estimated using a back propagation neural network approach. The
supervised learning is accomplished using known exact results of the
electronic correlation energy. The recall rate, that is, the performance of
the net in recognizing the training set, is about 96%. The
correctness of values given to the test set and prediction rate is at the
90% level. We generate tables for the electronic correlation energy
of several diatomic molecules and all the neutral atoms up to radon
(Rn). © 1997 by John Wiley & Sons, Inc. J Comput
Chem 18: 1407–1414, 1997