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Stochastic coupled cluster theory: Efficient sampling of the coupled cluster expansion

机译:随机耦合集群理论:耦合集群扩展的有效采样

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We consider the sampling of the coupled cluster expansion within stochastic coupled cluster theory. Observing the limitations of previous approaches due to the inherently non-linear behavior of a coupled cluster wavefunction representation, we propose new approaches based on an intuitive, well-defined condition for sampling weights and on sampling the expansion in cluster operators of different excitation levels. We term these modifications even and truncated selections, respectively. Utilising both approaches demonstrates dramatically improved calculation stability as well as reduced computational and memory costs. These modifications are particularly effective at higher truncation levels owing to the large number of terms within the cluster expansion that can be neglected, as demonstrated by the reduction of the number of terms to be sampled when truncating at triple excitations by 77% and hextuple excitations by 98%. Published by AIP Publishing.
机译:我们考虑随机耦合集群理论内的耦合集群扩展的采样。 由于耦合群集波段表示的固有非线性行为,观察先前方法的局限性,我们提出了基于直观,明确的采样权重的新方法,以及在不同激励级别的集群运营商中采样扩展。 我们分别术语这些修改和截断的选择。 利用两种方法表明,显着改善了计算稳定性以及降低的计算和内存成本。 由于可以忽略的集群扩展内的大量术语,这些修改在较高的截断水平上特别有效,如可以忽略的群体扩展,通过减少在三重激发时截断77%和Hextuple激励时的术语数量减少 98%。 通过AIP发布发布。

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