Learning Full Configuration Interaction Electron Correlations with Deep Learning [article]

Hector H. Corzo and Arijit Sehanobish and Onur Kara
2021 arXiv   pre-print
In this report, we present a deep learning framework termed the Electron Correlation Potential Neural Network (eCPNN) that can learn succinct and compact potential functions. These functions can effectively describe the complex instantaneous spatial correlations among electrons in many--electron atoms. The eCPNN was trained in an unsupervised manner with limited information from Full Configuration Interaction (FCI) one--electron density functions within predefined limits of accuracy. Using the
more » ... ffective correlation potential functions generated by eCPNN, we can predict the total energies of each of the studied atomic systems with a remarkable accuracy when compared to FCI energies.
arXiv:2106.08138v2 fatcat:zaa3pe5bnvaszj4qt742pvie54