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

TL;DR

The paper introduces ipie, a Python-based AFQMC program optimized for CPUs and GPUs, demonstrating its efficiency and accuracy in calculating complex chemical isomerization energies with modest strong correlation.

Contribution

Developed ipie, a flexible Python AFQMC software with GPU support, achieving competitive performance and accurate results on complex chemical systems.

Findings

ipie performs comparably or faster than C++ codes on CPUs and GPUs.

Accurate AFQMC isomerization energies for [Cu2O2]2+ with small and large basis sets.

Successful convergence of energies with controlled error estimates.

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($\mu$-oxo) and $\mu$-$\eta^2$:$\eta^2$ 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.