Improved modularity and new features in ipie: Toward even larger AFQMC calculations on CPUs and GPUs at zero and finite temperatures

TL;DR

The paper presents significant improvements in the ipie AFQMC package, enhancing modularity, scalability, and features for large-scale quantum chemistry simulations on CPUs and GPUs at zero and finite temperatures.

Contribution

It introduces new capabilities such as distributed Hamiltonian simulations, GPU-accelerated multi-Slater wavefunctions, and implementations of finite temperature and electron-phonon AFQMC methods.

Findings

Enabled large system simulations with multi-GPU support

Achieved near-exact energies for complex multi-reference systems

Expanded AFQMC methods to finite temperature and electron-phonon systems

Abstract

ipie is a Python-based auxiliary-field quantum Monte Carlo (AFQMC) package that has undergone substantial improvements since its initial release [J. Chem. Theory Comput., 2023, 19(1): 109-121]. This paper outlines the improved modularity and new capabilities implemented in ipie. We highlight the ease of incorporating different trial and walker types and the seamless integration of ipie with external libraries. We enable distributed Hamiltonian simulations of large systems that otherwise would not fit on single CPU node or GPU card. This development enabled us to compute the interaction energy of a benzene dimer with 84 electrons and 1512 orbitals with multi-GPUs. Using CUDA and cupy for NVIDIA GPUs, ipie supports GPU-accelerated multi-slater determinant trial wavefunctions [arXiv:2406.08314] to enable efficient and highly accurate simulations of large-scale systems. This allows for near-exact ground state energies of multi-reference clusters, [Cu$_2$O$_2$]$^{2+}$ and [Fe$_2$S$_2$(SCH$_3$)$_4$]$^{2-}$. We also describe implementations of free projection AFQMC, finite temperature AFQMC, AFQMC for electron--phonon systems, and automatic differentiation in AFQMC for calculating physical properties. These advancements position ipie as a leading platform for AFQMC research in quantum chemistry, facilitating more complex and ambitious computational method development and their applications.