  Simulating warm dense matter that undergoes a wide range of temperatures and
densities is challenging. Predictive theoretical models, such as
quantum-mechanics-based first-principles molecular dynamics (FPMD), require a
huge amount of computational resources. Herein, we propose a deep learning
based scheme, called electron temperature dependent deep potential molecular
dynamics (TDDPMD), for efficiently simulating warm dense matter with the
accuracy of FPMD. The TDDPMD simulation is several orders of magnitudes faster
than FPMD, and, unlike FPMD, its efficiency is not affected by the electron
temperature. We apply the TDDPMD scheme to beryllium (Be) in a wide range of
temperatures (0.4 to 2500 eV) and densities (3.50 to 8.25 g/cm$^3$). Our
results demonstrate that the TDDPMD method not only accurately reproduces the
structural properties of Be along the principal Hugoniot curve at the FPMD
level, but also yields even more reliable diffusion coefficients than typical
FPMD simulations due to its ability to simulate larger systems with longer
time.
