CPPE is an open-source, light-weight C++ and Python library for Polarizable Embedding (PE)1,2 calculations. It provides an easy-to-use API to implement PE for ground-state self-consistent field (SCF) calculations and post-SCF methods. A convenient Python interface is also available.
CPPE enables PE calculations in the following programs:
Linear scaling electric field computations in CPPE are achieved through autogenerated code
by the fmmgen library.3
Examples for the open-source Python-driven programs can be found here.
The easiest way to install CPPE is via conda:
conda install cppe -c conda-forge
Manual builds can be done using CMake by running
git clone https://github.com/maxscheurer/cppe
cd cppe; mkdir build; cd build
cmake ..
make
Another way to install CPPE is via pip:
pip install cppe
Note that CPPE will be built from source and a C++14 compatible compiler is required (see below), and OpenMP parallelization is disabled in the setup.py/pip installation.
Alternatively, CPPE can be built from source using the setup.py script with
git clone https://github.com/maxscheurer/cppe
cd cppe
python setup.py install
If the Python interface should be built, specify the CMake option
-DENABLE_PYTHON_INTERFACE=ON. If pybind11 is not installed, CMake
will automatically download pybind11 and install it locally.
Installing through setup.py will always build the Python interface.
- C++ 14 compiler
- Python >= 3.6 (interpreter and development packages)
The tests can be run with
python setup.py build_ext -i; python setup.py test
for the setup.py build, or
source setup_environment.sh; py.test
for the CMake build.
| Papers: |   |
|---|---|
| Code: |  |
CPPE: An Open-Source C++ and Python Library for Polarizable Embedding
Maximilian Scheurer, Peter Reinholdt, Erik Rosendahl Kjellgren, Jógvan Magnus Haugaard Olsen, Andreas Dreuw, and Jacob Kongsted;
Journal of Chemical Theory and Computation 2019 15 (11), 6154-6163,
DOI: 10.1021/acs.jctc.9b00758
If you use the linear-scaling FMM implementation, please also cite:
Efficient Open-Source Implementations of Linear-Scaling Polarizable Embedding: Use Octrees to Save the Trees
Maximilian Scheurer, Peter Reinholdt, Jógvan Magnus Haugaard Olsen, Andreas Dreuw, and Jacob Kongsted;
Journal of Chemical Theory and Computation 2021,
DOI: 10.1021/acs.jctc.1c00225
1 Olsen, J. M. H.; Aidas, K.; Kongsted, J. (2010). Excited States in Solution through Polarizable Embedding. J. Chem. Theory Comput., 6 (12), 3721–3734. https://doi.org/10.1021/ct1003803
2 Olsen, J. M. H.; Kongsted, J. (2011). Molecular Properties through Polarizable Embedding. Advances in Quantum Chemistry (Vol. 61). https://doi.org/10.1016/B978-0-12-386013-2.00003-6
3 Pepper, R.; Fangohr, H. (2020). fmmgen: Automatic Code Generation of Operators for Cartesian Fast Multipole and Barnes-Hut Methods. arXiv:2005.12351
