Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Statistical learning method for predicting density-matrix based electron
  dynamics   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.

0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ