ipie: A Python-Based Auxiliary-Field Quantum Monte Carlo Program with Flexibility and Efficiency on CPUs and GPUs

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 [Cu2O2]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 [Cu2O2]2+ using selected configuration interaction trials show that it is possible to converge the ph-AFQMC isomerization energy between bis(mu-oxo) and mu-12:12 peroxo configurations to the exact known results for small basis sets with 105-106 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 106 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.

Automated summary

In this paper, we report the development of a Python-based AFQMC program called ipie, including timing benchmarks and new results on the isomerization of [Cu2O2]2+. We demonstrate the support for both CPUs and GPUs in ipie and show its interface with PySCF as well as a simple template for adding new estimators. Our benchmarks show that ipie is faster or similarly performing compared to other C++ codes on both CPUs and GPUs. Moreover, our results on [Cu2O2]2+ demonstrate the accuracy and efficiency of ipie in handling systems with moderate strong correlation and large-scale dynamic correlation.