Estimating correlation energy of diatomic molecules and atoms with neural networks
Geraldo Magela e Silva, Paulo H. Acioli, Antonio Carlos Pedroza · Journal of Computational Chemistry · 1997
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