Application of the Quantum Potential Neural Network to multi-electronic atoms

In this report, the application of the Quantum Potential Neural Network (QPNN) framework to many electron atomic systems is presented. For this study, full configuration interaction (FCI) one–electron density functions within predefined limits of accuracy were used to train the QPNN. The obtained results suggest that this new neural network is capable of learning the effective potential functions of many electron atoms in a completely unsupervised manner, and using only limited information from the probability density. Using the effective potential functions learned for each of the studied systems the QPNN was able to estimate the total energies of each of the systems (with a maximum of 10 trials) with a remarkable accuracy when compared to the FCI energies.