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

Abstract

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($\hat{I}¼$-oxo) and $\hat{I}¼$-$\hat{I}{\cdot }^2$:$\hat{I}{\cdot }^2$ 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.