论文标题
快速和通用的kohn假假密度函数理论算法，用于热量血浆的热量密集物质
Fast and Universal Kohn Sham Density Functional Theory Algorithm for Warm Dense Matter to Hot Dense Plasma
论文作者
论文摘要
了解许多过程，例如融合实验，行星内部和矮星恒星在很大程度上取决于温暖密集物质（WDM）和热血浆的微观物理学建模。这种复杂的物质状态由变性电子，分子和离子的瞬时混合物组成。该政权挑战了实验和分析建模，需要预测\ emph {ab libio}原子计算，通常基于量子机械的Kohn-Sham密度功能理论（KS-DFT）。但是，随温度和系统大小的立方计算缩放禁止通过大部分WDM制度使用DFT。最近开发的KS-DFT随机方法可以在高温下使用，其精度与确定性方法完全相同，但是随机误差可以缓慢收敛，并且对于中等温度（<50 eV）仍然很昂贵。我们已经在任何温度下开发了一种用于DFT的通用混合随机确定算法。这种方法利用KS-DFT的物理学无缝整合了这些不同方法的最佳方面。我们证明，这种方法显着加速了3至50 eV的温度，同时产生稳定的分子动力学和准确的扩散系数。
Understanding many processes, e.g. fusion experiments, planetary interiors and dwarf stars, depends strongly on microscopic physics modeling of warm dense matter (WDM) and hot dense plasma. This complex state of matter consists of a transient mixture of degenerate and nearly-free electrons, molecules, and ions. This regime challenges both experiment and analytical modeling, necessitating predictive \emph{ab initio} atomistic computation, typically based on quantum mechanical Kohn-Sham Density Functional Theory (KS-DFT). However, cubic computational scaling with temperature and system size prohibits the use of DFT through much of the WDM regime. A recently-developed stochastic approach to KS-DFT can be used at high temperatures, with the exact same accuracy as the deterministic approach, but the stochastic error can converge slowly and it remains expensive for intermediate temperatures (<50 eV). We have developed a universal mixed stochastic-deterministic algorithm for DFT at any temperature. This approach leverages the physics of KS-DFT to seamlessly integrate the best aspects of these different approaches. We demonstrate that this method significantly accelerated self-consistent field calculations for temperatures from 3 to 50 eV, while producing stable molecular dynamics and accurate diffusion coefficients.
