TurboGenius：用于从头算量子蒙特卡洛方法高通量计算的Python套件
TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods
AI总结：
本研究推出开源Python包TurboGenius及配套工作流工具TurboWorkflows，可结合TurboRVB实现从头算QMC高通量计算，验证了工具可靠性并通过多数据集DMC基准测试证明其计算结果与参考值相符。
摘要：
TurboGenius是一款开源Python包，旨在通过Python脚本完全控制从头算量子蒙特卡洛（QMC）任务，可结合TurboRVB [K. Nakano等人，J. Phys. Chem. 152, 204121 (2020)]开展高通量计算。本文概述了TurboGenius包，并展示了高通量模式下取得的若干结果。为借助TurboGenius实现高通量计算，我们开发了另一款开源Python包TurboWorkflows，可用于构建基于TurboGenius的简单工作流。我们通过两项任务验证了其有效性：（1）验证TurboRVB包中实现的密度泛函理论（DFT）和QMC驱动程序；（2）针对多个数据集开展扩散蒙特卡洛（DMC）计算基准测试。针对任务（1），我们检验了TurboRVB与其他成熟量子化学包之间的跨包一致性。经确认，在局域密度近似（LDA）下，PySCF得到的DFT能量与TurboRVB的结果一致；PySCF和Quantum Package得到的哈特里-福克（HF）能量，与TurboRVB采用HF波函数得到的变分蒙特卡洛能量一致。这些验证测试进一步核验了TurboRVB包的可靠性。针对任务（2），我们利用DMC计算测试了Gaussian-2集的原子化能、S22、A24和SCAI集的结合能，以及12种立方晶体的平衡晶格参数。研究发现，对于本文分析的所有化合物，采用LDA节面的DMC计算均能得到令人满意的结果，即与高水平计算参考值或实验参考值均一致。
英文摘要：
TurboGenius is an open-source Python package designed to fully control ab initio quantum Monte Carlo (QMC) jobs using a Python script, which allows one to perform high-throughput calculations combined with TurboRVB [K. Nakano et al. J. Phys. Chem. 152, 204121 (2020)]. This paper provides an overview of the TurboGenius package and showcases several results obtained in a high-throughput mode. For the purpose of performing high-throughput calculations with TurboGenius, we implemented another open-source Python package, TurboWorkflows, that enables one to construct simple workflows using TurboGenius. We demonstrate its effectiveness by performing (1) validations of density functional theory (DFT) and QMC drivers as implemented in the TurboRVB package and (2) benchmarks of Diffusion Monte Carlo (DMC) calculations for several data sets. For (1), we checked inter-package consistencies between TurboRVB and other established quantum chemistry packages. By doing so, we confirmed that DFT energies obtained by PySCF are consistent with those obtained by TurboRVB within the local density approximation (LDA), and that Hartree-Fock (HF) energies obtained by PySCF and Quantum Package are consistent with variational Monte Carlo energies obtained by TurboRVB with the HF wavefunctions. These validation tests constitute a further reliability check of the TurboRVB package. For (2), we benchmarked atomization energies of the Gaussian-2 set, binding energies of the S22, A24, and SCAI sets, and equilibrium lattice parameters of 12 cubic crystals using DMC calculations. We found that, for all compounds analyzed here, the DMC calculations with the LDA nodal surface give satisfactory results, i.e., consistent either with high-level computational or with experimental reference values.
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