Fast and Universal Kohn-Sham Density Functional Theory Algorithm for Warm Dense Matter to Hot Dense Plasma.

Area of Science:

• Computational physics
• Quantum mechanics
• Plasma physics

Background:

• Modeling warm dense matter and hot dense plasma is vital for understanding fusion experiments, planetary interiors, and dwarf stars.
• This state of matter presents significant challenges for experimental and analytical modeling due to its complex mixture of electrons, molecules, and ions.
• Quantum mechanical Kohn-Sham density functional theory (KS-DFT) is essential for ab initio atomistic computation but faces computational scaling limitations.

Purpose of the Study:

• To develop a computational method that overcomes the limitations of existing KS-DFT approaches for modeling warm dense matter and hot dense plasma across a wide temperature range.
• To create a hybrid algorithm that combines the strengths of deterministic and stochastic KS-DFT methods.

Main Methods:

• Developed a universal mixed stochastic-deterministic algorithm for KS-DFT.
• Leveraged the underlying physics of KS-DFT to integrate deterministic and stochastic approaches seamlessly.
• Applied the algorithm to self-consistent field calculations for temperatures ranging from 3 to 50 eV.

Main Results:

• Significantly accelerated self-consistent field calculations for KS-DFT in the 3-50 eV temperature range.
• Achieved stable molecular dynamics simulations.
• Obtained accurate diffusion coefficients for the modeled systems.

Conclusions:

• The developed mixed algorithm provides a computationally efficient and accurate method for KS-DFT at various temperatures.
• This approach enhances the predictive capabilities for simulating extreme states of matter.
• Enables more reliable modeling for fields such as fusion energy research and astrophysics.