Learning Full Configuration Interaction Electron Correlations with Deep Learning
This work addresses the challenge of accurately simulating electron correlations in atomic systems, which is crucial for quantum chemistry and materials science, but it appears incremental as it builds on existing FCI methods with a new deep learning approach.
The authors tackled the problem of modeling electron correlations in many-electron atoms by developing a deep learning framework called eCPNN, which learns compact potential functions from FCI data and predicts total energies with remarkable accuracy compared to FCI energies.
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 effective 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.