Diagrammatic Coupled Cluster Monte Carlo
Diagrammatic Coupled Cluster Monte Carlo
复制标题
图解耦合聚类蒙特卡罗
DOI：
10.1021/acs.jpclett.9b00067
复制
发表时间：
2019
影响因子：
5.5
通讯作者：
Scott, Charles
中科院分区：
文献类型：
--
作者：
Scott, Charles
我们提出了一种改进的耦合聚类蒙特卡罗算法，该算法通过动态构建耦合聚类图，对相似变换哈密顿量的截断 Baker-Campbell-Hausdorff 展开中的连接项进行随机采样。我们的新方法 diagCCMC 允许仅使用相似变换哈密顿量的连接分量来执行传播，从而大大降低了与耦合簇方程的随机解相关的内存成本。我们证明，对于完全局部的、非交互的系统，diagCCMC 能够表示耦合簇波函数，其内存成本随系统大小线性缩放。有利的内存成本是在固定随机粒度的唯一假设下观察到的，并且对于任意级别的耦合聚类理论都有效。随着有限氦原子链的解离，存储器成本也显着降低。通过拉伸氮分子的例子，这种方法也被证明在存在强相关性的情况下不会崩溃。我们的新颖方法使耦合集群蒙特卡罗的理论基础更接近确定性方法。
We propose a modified coupled cluster Monte Carlo algorithm that stochastically samples connected terms within the truncated Baker–Campbell–Hausdorff expansion of the similarity-transformed Hamiltonian by construction of coupled cluster diagrams on the fly. Our new approach—diagCCMC—allows propagation to be performed using only the connected components of the similarity-transformed Hamiltonian, greatly reducing the memory cost associated with the stochastic solution of the coupled cluster equations. We show that for perfectly local, noninteracting systems diagCCMC is able to represent the coupled cluster wavefunction with a memory cost that scales linearly with system size. The favorable memory cost is observed with the only assumption of fixed stochastic granularity and is valid for arbitrary levels of coupled cluster theory. Significant reduction in memory cost is also shown to smoothly appear with dissociation of a finite chain of helium atoms. This approach is also shown not to break down in the presence of strong correlation through the example of a stretched nitrogen molecule. Our novel methodology moves the theoretical basis of coupled cluster Monte Carlo closer to deterministic approaches.