Plasma Physics (physics.plasm-ph)

Abstract: Achieving entire large scale kinetic modelling is a crucial task for the development and optimization of modern plasma devices. With the trend of decreasing pressure in applications such as plasma etching, kinetic simulations are necessary to self-consistently capture the particle dynamics. The standard, explicit, electrostatic, momentum-conserving Particle-In-Cell method suffers from tight stability constraints to resolve the electron plasma length (i.e. Debye length) and time scales (i.e. plasma period). This results in very high computational cost, making this technique generally prohibitive for the large volume entire device modeling (EDM). We explore the Direct Implicit algorithm and the explicit Energy Conserving algorithm as alternatives to the standard approach, which can reduce computational cost with minimal (or controllable) impact on results. These algorithms are implemented into the well-tested EDIPIC-2D and LTP-PIC codes, and their performance is evaluated by testing on a 2D capacitively coupled plasma discharge scenario. The investigation revels that both approaches enable the utilization of cell sizes larger than the Debye length, resulting in reduced runtime, while incurring only a minor compromise in accuracy. The methods also allow for time steps larger than the electron plasma period, however this can lead to numerical heating or cooling. The study further demonstrates that by appropriately adjusting the ratio of cell size to time step, it is possible to mitigate this effect to acceptable level.

Abstract: In traditional finite-temperature Kohn-Sham density functional theory (KSDFT), the well-known orbitals wall restricts the use of first-principles molecular dynamics methods at extremely high temperatures. However, stochastic density functional theory (SDFT) can overcome the limitation. Recently, SDFT and its related mixed stochastic-deterministic density functional theory, based on the plane-wave basis set, have been implemented in the first-principles electronic structure software ABACUS [Phys. Rev. B 106, 125132(2022)]. In this study, we combine SDFT with the Born-Oppenheimer molecular dynamics (BOMD) method to investigate systems with temperatures ranging from a few tens of eV to 1000 eV. Importantly, we train machine-learning-based interatomic models using the SDFT data and employ these deep potential models to simulate large-scale systems with long trajectories. Consequently, we compute and analyze the structural properties, dynamic properties, and transport coefficients of warm dense matter. The abovementioned methods offer a new approach with first-principles accuracy to tackle various properties of warm dense matter.