论文标题
分解和嵌入随机$ GW $自我能源
Decomposition and embedding in the stochastic $GW$ self-energy
论文作者
论文摘要
我们提出了两个新的开发项目，用于计算$ GW $近似中激发的状态能量。首先，将绿色功能和筛选的库仑相互作用的计算分解为两个部分：一个是确定性的，而另一部分则依赖于随机抽样。其次，这种分离允许构建一个子空间自能，该子空间自能源仅包含来自特定（空间或能量）感兴趣的区域的动态相关性。该方法在周期性HBN单层和HBN - 格拉彭异质结构中的氮呈态的大规模模拟中进行了例证。我们证明，强烈局部状态的确定性嵌入会显着降低统计误差，并且计算成本降低的数量级超过了一个数量级。计算的子空间自能源揭示了界面耦合如何影响电子相关性并确定对激发态寿命的贡献。虽然嵌入对于正确治疗杂质状态是必需的，但分解可产生对异质系统中量子现象的新物理见解。
We present two new developments for computing excited state energies within the $GW$ approximation. First, calculations of the Green's function and the screened Coulomb interaction are decomposed into two parts: one is deterministic while the other relies on stochastic sampling. Second, this separation allows constructing a subspace self-energy, which contains dynamic correlation from only a particular (spatial or energetic) region of interest. The methodology is exemplified on large-scale simulations of nitrogen-vacancy states in a periodic hBN monolayer and hBN-graphene heterostructure. We demonstrate that the deterministic embedding of strongly localized states significantly reduces statistical errors, and the computational cost decreases by more than an order of magnitude. The computed subspace self-energy unveils how interfacial couplings affect electronic correlations and identifies contributions to excited-state lifetimes. While the embedding is necessary for the proper treatment of impurity states, the decomposition yields new physical insight into quantum phenomena in heterogeneous systems.
