Machine Learning Exchange-Correlation Potential in Time Dependent Density Functional Theory

Understand

We propose a machine learning based approach to develop the exchange-correlation potential of time dependent density functional theory (TDDFT).

• The neural network projection from the time-varying electron densities to the corresponding correlation potentials in the time-dependent Kohn-Sham equation is trained using a few exact datasets for a model system of electron-hydrogen scattering.
• We demonstrate that this neural network potential can capture the complex structures in the time-dependent correlation potential during the scattering process and provide correct scattering probabilities, which are not obtained by the standard adiabatic functionals.
• We also show that it is possible to incorporate the nonadiabatic (or memory) effect in the potential with this machine learning technique, which significantly improves the accuracy of the dynamics.