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arXiv 2404.09280physics.chem-phquant-ph
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神经网络量子态的改进优化及在铬二聚体上的测试

Improved Optimization for the Neural-network Quantum States and Tests on the Chromium Dimer

Xiang Li, Jia-Cheng Huang, Guang-Ze Zhang, Hao-En Li, Zhu-Ping Shen, Chen Zhao, Jun Li, Han-Shi Hu

AI总结:

本文提出自适应学习率、约束优化和块优化三种算法,显著提升了基于神经网络量子态的变分蒙特卡罗优化的效率与鲁棒性,在多参考键拉伸和强关联体系中以较低成本实现超越耦合簇理论的精度。

摘要:

Neural-network Quantum States (NQS)的出现显著推进了波函数拟设研究,引发了轨道空间变分蒙特卡罗(VMC)探索的复兴。本工作引入三种算法增强以降低使用NQS进行VMC优化的计算需求:自适应学习率算法、约束优化和块优化。我们在cc-pVDZ基组内对H2O和N2的复杂多参考键拉伸评估了改进后的算法,并在Ahlrichs SV基组中计算了强关联铬二聚体(Cr2)的基态能量。与耦合簇理论相比,我们的结果在相对适中的CPU成本下实现了更高的精度。本工作展示了如何利用这些策略提升优化效率与鲁棒性,为更有效地优化基于大规模受限玻尔兹曼机(RBM)的NQS开辟了新路径,标志着NQS在实际量子化学应用中的重大进展。

英文摘要:

The advent of Neural-network Quantum States (NQS) has significantly advanced wave function ansatz research, sparking a resurgence in orbital space variational Monte Carlo (VMC) exploration. This work introduces three algorithmic enhancements to reduce computational demands of VMC optimization using NQS: an adaptive learning rate algorithm, constrained optimization, and block optimization. We evaluate the refined algorithm on complex multireference bond stretches of $\rm H_2O$ and $\rm N_2$ within the cc-pVDZ basis set and calculate the ground-state energy of the strongly correlated chromium dimer ($\rm Cr_2$) in the Ahlrichs SV basis set. Our results achieve superior accuracy compared to coupled cluster theory at a relatively modest CPU cost. This work demonstrates how to enhance optimization efficiency and robustness using these strategies, opening a new path to optimize large-scale Restricted Boltzmann Machine (RBM)-based NQS more effectively and marking a substantial advancement in NQS's practical quantum chemistry applications.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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