Jiang Tong, Baumgarten Moritz K A, Loos Pierre-François, Mahajan Ankit, Scemama Anthony, Ung Shu Fay, Zhang Jinghong, Malone Fionn D, Lee Joonho
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, USA.
Laboratoire de Chimie et Physique Quantiques (UMR 5626), Université de Toulouse, CNRS, UPS, Toulouse, France.
J Chem Phys. 2024 Oct 28;161(16). doi: 10.1063/5.0225596.
ipie is a Python-based auxiliary-field quantum Monte Carlo (AFQMC) package that has undergone substantial improvements since its initial release [Malone et al., J. Chem. Theory Comput. 19(1), 109-121 (2023)]. 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 a single central processing unit node or graphics processing unit (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 [Huang et al. arXiv:2406.08314 (2024)] to enable efficient and highly accurate simulations of large-scale systems. This allows for near-exact ground state energies of multi-reference clusters, [Cu2O2]2+ and [Fe2S2(SCH3)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.
ipie是一个基于Python的辅助场量子蒙特卡罗（AFQMC）软件包，自首次发布以来已经有了实质性的改进[马龙等人，《化学理论与计算杂志》19(1)，109 - 121(2023)]。本文概述了ipie中改进的模块化和新功能。我们强调了纳入不同试验和游走器类型的便捷性以及ipie与外部库的无缝集成。我们实现了对大型系统的分布式哈密顿量模拟，否则这些系统无法在单个中央处理器节点或图形处理器（GPU）卡上运行。这一进展使我们能够使用多GPU计算具有84个电子和1512个轨道的苯二聚体的相互作用能。利用针对NVIDIA GPU的CUDA和cupy，ipie支持GPU加速的多斯莱特行列式试验波函数[黄等人，arXiv:2406.08314(2024)]，以实现对大规模系统的高效且高精度模拟。这使得能够得到多参考簇[Cu2O2]2 + 和[Fe2S2(SCH3)4]2 - 的近精确基态能量。我们还描述了自由投影AFQMC（辅助场量子蒙特卡罗）、有限温度AFQMC、用于电子 - 声子系统的AFQMC以及在AFQMC中用于计算物理性质的自动微分的实现。这些进展使ipie成为量子化学中AFQMC研究的领先平台，促进了更复杂、更具雄心的计算方法开发及其应用。
