论文标题
IPIE：一个基于Python的辅助场量子蒙特卡洛计划，具有柔韧性和效率的CPU和GPU
ipie: A Python-based Auxiliary-Field Quantum Monte Carlo Program with Flexibility and Efficiency on CPUs and GPUs
论文作者
论文摘要
我们报告了IPIE的基于Python的辅助场量子Carlo（AFQMC）计划的开发，并具有初步的定时基准和新的AFQMC结果，结果是[Cu $ _2 $ _2 $ o $ _2 $ _2 $ _2 $$]^{2+} $的异构化。我们证明了如何在IPIE中实现中央和图形处理单元（CPU和GPU）的实现。我们显示了IPIE与PYSCF的接口，以及一个直接的模板，用于向IPIE添加新的估计器。我们针对其他C ++代码QMCPACK和DICE的定时基准表明，对于CPU和GPU上考虑的所有化学系统，IPIE的速度更快或类似地执行。我们在[Cu $ _2 $ o $ _2 $$]^{2+} $使用选定配置互动试验上的结果表明，可以在Bis（$μ$ -oxo）和$μ$ - $ - $ $ - $η^2 $ perox $ perox $ perox $ perox $ new $ ph-afqmc异构化能量中收敛决定因素。我们还报告了具有四倍体 - ZETA基础设置的异构化能，其估计误差小于KCAL/MOL，该误差涉及在试验波函数中具有$ 10^6 $决定因素的52个电子和290个轨道。这些结果突出了pH-AFQMC和IPIE的实用性，该系统对于具有适度的强相关性和大规模动态相关性的系统。
We report the development of a python-based auxiliary-field quantum Monte Carlo (AFQMC) program, ipie, with preliminary timing benchmarks and new AFQMC results on the isomerization of [Cu$_2$O$_2$$]^{2+}$. We demonstrate how implementations for both central and graphical processing units (CPUs and GPUs) are achieved in ipie. We show an interface of ipie with PySCF as well as a straightforward template for adding new estimators to ipie. Our timing benchmarks against other C++ codes, QMCPACK and Dice, suggest that ipie is faster or similarly performing for all chemical systems considered on both CPUs and GPUs. Our results on [Cu$_2$O$_2$$]^{2+}$ using selected configuration interaction trials show that it is possible to converge the ph-AFQMC isomerization energy between bis($μ$-oxo) and $μ$-$η^2$:$η^2$ peroxo configurations to the exact known results for small basis sets with $10^5$ to $10^6$ determinants. We also report the isomerization energy with a quadruple-zeta basis set with an estimated error less than a kcal/mol, which involved 52 electrons and 290 orbitals with $10^6$ determinants in the trial wavefunction. These results highlight the utility of ph-AFQMC and ipie for systems with modest strong correlation and large-scale dynamic correlation.
