分子系统最小二乘张量超收缩技术的基准测试
Benchmarking Least-Squares Tensor Hypercontraction Techniques for Molecular Systems
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中文总结 AI 辅助
本研究通过在多种分子系统上对不同THC构造算法开展基准测试，明确其精度与效率的差异，同时在开源库PyTHC中提供参考实现，填补了该领域系统比较的空白。
中文摘要 AI 辅助
为降低多体电子结构方法的计算成本，对双电子积分（ERI）张量进行低秩近似是关键步骤。近年来，一系列新的张量分解技术（如张量超收缩（THC）方法）被开发以实现该目标，但目前缺乏对不同THC构造算法的精度与效率的系统比较。本研究针对这一缺口，在广泛的分子系统上对这些技术进行基准测试，除提供不同技术优缺点的有用见解外，还在新开发的开源库PyTHC中提供参考实现。
英文摘要
Finding a low-rank approximation for the two-electron integral (ERI) tensor is a crucial step towards reducing the computational cost of many-body electronic structure methods. In recent years, a range of new tensor factorization techniques, like the so-called tensor-hypercontraction (THC) approach, have been developed to achieve this goal. Unfortunately, a systematic comparison of accuracy and efficiency of different THC construction algorithms is missing. In this work, we address this gap by benchmarking these techniques across a wide range of molecular systems. In addition to offering useful insights into the advantages and disadvantages of the different techniques, we provide reference implementations in the newly developed open-source library, PyTHC.
