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 demonstrated how implementations for both central and graphical processing units (CPUs and GPUs) are achieved in ipie. We showed 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(μ-oxo) and μ-η2:η2 peroxo configurations to the exact known result with 105 to 106 determinants for small basis sets. We also report the isomerization energy with a quadruple- zeta basis set, which involved 32 electrons and 280 orbitals with 106 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.