Malone Fionn D, Mahajan Ankit, Spencer James S, Lee Joonho
Google Research, Venice, California 90291, United States.
Department of Chemistry, University of Colorado, Boulder, Colorado 80302, United States.
J Chem Theory Comput. 2023 Jan 10;19(1):109-121. doi: 10.1021/acs.jctc.2c00934. Epub 2022 Dec 12.
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 [CuO]. 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 [CuO] using selected configuration interaction trials show that it is possible to converge the ph-AFQMC isomerization energy between bis(μ-oxo) and μ-η:η peroxo configurations to the exact known results for small basis sets with 10-10 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 determinants in the trial wave function. These results highlight the utility of ph-AFQMC and ipie for systems with modest strong correlation and large-scale dynamic correlation.
我们报告了基于Python的辅助场量子蒙特卡罗（AFQMC）程序ipie的开发情况，给出了初步的计时基准以及关于[CuO]异构化的新AFQMC结果。我们展示了ipie如何实现中央处理器和图形处理器（CPU和GPU）的计算。我们展示了ipie与PySCF的接口以及向ipie添加新估计器的简单模板。我们与其他C++代码QMCPACK和Dice的计时基准比较表明，在CPU和GPU上考虑的所有化学系统中，ipie的速度更快或性能相当。我们使用选定的组态相互作用试验对[CuO]的结果表明，对于具有10 - 10个行列式的小基组，有可能将双（μ - 氧代）和μ - η:η过氧构型之间的ph - AFQMC异构化能收敛到确切的已知结果。我们还报告了使用四重zeta基组的异构化能，估计误差小于1千卡/摩尔，该计算涉及52个电子和290个轨道，试探波函数中有10个行列式。这些结果突出了ph - AFQMC和ipie对于具有适度强关联和大规模动态关联系统的实用性。
