  We develop a statistical method to learn a molecular Hamiltonian matrix from
a time-series of electron density matrices. We extend our previous method to
larger molecular systems by incorporating physical properties to reduce
dimensionality, while also exploiting regularization techniques like ridge
regression for addressing multicollinearity. With the learned Hamiltonian we
can solve the Time-Dependent Hartree-Fock (TDHF) equation to propagate the
electron density in time, and predict its dynamics for field-free and field-on
scenarios. We observe close quantitative agreement between the predicted
dynamics and ground truth for both field-off trajectories similar to the
training data, and field-on trajectories outside of the training data.
