Many-Body Adaptive Configuration Interaction Suite (MACIS) Copyright (c) 2023, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.

If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at [email protected].

NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.

The Many-Body Adaptive Configuration Interaction Suite (MACIS) is a modern, modular C++ library for high-performance quantum many-body methods based on configuration interaction (CI). MACIS currently provides a reuseable and extentible interface for the development of full CI (FCI), complete active-space (CAS) and selected-CI (sCI) methods for quantum chemistry. Efforts have primarily been focused on the development of distributed memory variants of the adaptive sampling CI (ASCI) method on CPU architectures, and work is underway to extend the functionality set to other methods commonly encountered in quantum chemistry and to accelerator architectures targeting exascale platforms.

MACIS is a work in progress. Its development has been funded by the Computational Chemical Sciences (CCS) program of the U.S. Department of Energy Office of Science, Office of Basic Energy Science (BES). It was originally developed under the Scalable Predictive Methods for Excitations and Correlated Phenomena (SPEC) Center.

• David Williams-Young (LBNL): dbwy [at] lbl [dot] gov
• Carlos Mejuto Zaera (SISSA)
• Norm Tubman (NASA)

• CMake (3.14+)
• BLAS / LAPACK
• BLAS++ / LAPACK++
• std::mdspan with Kokkos extensions
• spdlog
• MPI (Optional)
• OpenMP (Optional)
• Boost (Optional)
• Catch2 (Testing)

Please cite the following publications if MACIS was used in your publication or software:

% Distributed Memory ASCI Implementation
@article{williams23_distributed_asci,
    title={A parallel, distributed memory implementation of the adaptive
           sampling configuration interaction method},
    author={David B. Williams-Young and Norm M. Tubman and Carlos Mejuto-Zaera 
            and Wibe A. de Jong}, 
    journal={The Journal of Chemical Physics},
    volume={158},
    pages={214109},
    year={2023},
    doi={10.1063/5.0148650},
    preprint={https://arxiv.org/abs/2303.05688},
    url={https://pubs.aip.org/aip/jcp/article/158/21/214109/2893713/A-parallel-distributed-memory-implementation-of}
}
    

MACIS provides a CMake build system with automatic dependency management (through FetchContent). As such, a simple CMake invocation will often suffice for most purposes

cmake -S /path/to/macis -B /path/to/build [MACIS configure options]
cmake --build /path/to/build

MACIS is linkable both as an installed library as well as a CMake subproject via FetchContent

# MACIS Discovery
find_package( macis REQUIRED )
target_link_libraries( my_target PUBLIC macis::macis )

# MACIS as CMake Subproject
include(FetchContent)

# Set MACIS CMake options (see below)

# Pull master branch of MACIS
FetchContent_Declare( macis 
  GIT_REPOSITORY https://github/com/wavefunction91/MACIS.git 
  GIT_TAG master 
)
FetchContent_MakeAvailable( macis )

# Link to target
target_link_libraries( my_target PUBLIC macis::macis )

Coming Soon.... See tests/standalone_driver.cxx for an example end-to-end invocation of MACIS for CAS-CI, CAS-SCF and ASCI-CI.

Have a question, comment or concern? Open an Issue or start a Discussion.

MACIS is made freely available under the terms of the LBNL modified 3-Clause BSD license. See LICENSE.txt for details.

The development of MACIS has ben supported by the Center for Scalable Predictive methods for Excitations and Correlated phenomena (SPEC), which is funded by the U.S. Department of Energy (DoE), Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences as part of the Computational Chemical Sciences (CCS) program at Lawrence Berkeley National Laboratory under FWP 12553.