  Density Functional Theory (DFT) has become a cornerstone in the modeling of
metals. However, accurately simulating metals, particularly under extreme
conditions, presents two significant challenges. First, simulating complex
metallic systems at low electron temperatures is difficult due to their highly
delocalized density matrix. Second, modeling metallic warm-dense materials at
very high electron temperatures is challenging because it requires the
computation of a large number of partially occupied orbitals. This study
demonstrates that both challenges can be effectively addressed using the latest
advances in linear-scaling stochastic DFT methodologies. Despite the inherent
introduction of noise into all computed properties by stochastic DFT, this
research evaluates the efficacy of various noise reduction techniques under
different thermal conditions. Our observations indicate that the effectiveness
of noise reduction strategies varies significantly with the electron
temperature. Furthermore, we provide evidence that the computational cost of
stochastic DFT methods scales linearly with system size for metal systems,
regardless of the electron temperature regime.
