ipie stands for Intelligent Python-based Imaginary-time Evolution with a focus on simplicity and speed.

ipie inherits a lot of QMC features from pauxy.

Copyright by Joonho Lee (joonholee@g.harvard.edu)

ipie is a Python-based auxiliary-field quantum Monte Carlo (AFQMC) package, designed for simplicity and computational efficiency. The package has seen substantial improvements in modularity, functionality, and compatibility since its first release.

Key features include:

• Ground State Energy Estimation: Calculate ground state energies of ab-initio systems with phaseless AFQMC.
• Distributed Hamiltonian Simulations: Run large-scale simulations distributed across multiple CPUs or GPUs, enabling calculations on systems too large for a single node or GPU card.
• GPU Acceleration: Support both CPU and GPU calculations, with GPU acceleration provided by CuPy/CUDA and CUDA-aware MPI.
• Extended AFQMC Algorithms: Includes free projection AFQMC, finite temperature AFQMC, automatic differentiation for property calculation, and (in the legacy code) AFQMC for electron-phonon systems.
• Simple Data Analysis
• Other legacy features from pauxy

For technical details, see our latest release papers:

• J. Chem. Theory Comput. 19, 109-121 (2023)
• J. Chem. Phys. 161, 162502 (2024)

Installation

A pure-Python wheel is available on PyPI and installs on Linux and Mac OS:

$ pip install ipie

For development, clone the repository:

$ git clone https://github.com/linusjoonho/ipie.git

Navigate to the top-level ipie directory and run:

$ pip install -r requirements.txt
$ pip install -e .

Requirements

To build ipie with MPI support (via mpi4py), install with:

$ pip install -e .[mpi]

This requires a working MPI installation on your machine, which can be installed via conda:

conda install openmpi

Refer to the mpi4py documentation for alternative installation options.

For GPU support, cupy is required and can be installed as outlined on the Cupy website. For CUDA-aware MPI, consider conda-forge.

For GPU cuTensorNet/cuQuantum support (used by the k-point and ISDF code paths), install the GPU extra, which pulls in cuquantum-python-cu12 (CUDA 12, Python >= 3.11) together with cupy:

$ pip install -e .[gpu]

For CUDA 13, install cuquantum-python-cu13 manually instead of the extra.

Running the Test Suite

Unit tests and driver tests are included. To run all tests:

$ pytest -v

More extensive parallel tests are executed in the CI; see .github/workflows/ci.yml for details.

Building Optimized C/C++ Code

ipie also provides optimized implementations for certain functions involving multiple Slater determinant trials using C/C++ code, which can be built for enhanced performance. To compile these functions into a shared library, navigate to the ipie/lib/wicks folder and use CMake and Make:

$ cmake .
$ make

Documentation

Documentation and tutorials are available at Read the Docs.