Massively parallel hybrid particle-field MD
Project description
HylleraasMD (HyMD) is a massively parallel Python package for hybrid particle-field molecular dynamics (hPF-MD) simulations of coarse-grained bio- and soft-matter systems.
HyMD can run canonical hPF-MD simulations [1], or filtered density Hamiltonian hPF (HhPF-MD) simulations [2], with or without explicit PME electrostatic interactions [3]. It includes all standard intramolecular interactions, including stretching, bending, torsional, and combined bending-dihedral potentials. Additionally, topological reconstruction of permanent peptide chain backbone dipoles is possible for accurate recreation of protein conformational dynamics [4]. Martini style elastic networks (ElNeDyn) [5] are also supported.
HyMD uses the pmesh library for particle-mesh operations, with the PPFT [6] backend for FFTs through the pfft-python bindings. File IO is done via HDF5 formats to allow MPI parallel reads.
User Guide
Detailed installation and user guide, together with comprehensive example simulations are located in the HylleraasMD documentation.
Run simulations by
python3 -m hymd [CONFIGURATION_FILE] [TOPOLOGY_FILE] (--OPTIONAL_ARGS)
Run interactively in Google Colaboratory
A Google Colaboratory jupyter notebook is setup here with a working HyMD fully installed and executable in the browser.
Installation
Install HyMD with pip
by
python3 -m pip install --upgrade numpy mpi4py cython
python3 -m pip install hymd
See HyMD docs for more information.
Install dependencies
HyMD installation requires a working MPI compiler. It is highly recommended to have MPI-enabled HDF5 and h5py for running parallel simulations with HyMD. Install both on Ubuntu with
sudo apt-get update -y
sudo apt-get install -y pkg-config libhdf5-mpi-dev
python3 -m pip uninstall h5py # Remove any serial h5py installation present
CC="mpicc" HDF5_MPI="ON" python3 -m pip install --no-binary=h5py h5py
Run in docker
Alternatively, an up-to-date docker image is available from docker hub
docker pull mortele/hymd
docker run -it mortele/hymd
/app$ python3 -m pip install hymd
/app$
/app$ # Grab example input files
/app$ curl -O https://raw.githubusercontent.com/Cascella-Group-UiO/HyMD-tutorial/main/ideal_chain/ideal_chain.toml
/app$ curl -O https://raw.githubusercontent.com/Cascella-Group-UiO/HyMD-tutorial/main/ideal_chain/ideal_chain.HDF5
/app$
/app$ # Run simulation
/app$ python3 -m hymd ideal_chain.toml ideal_chain.HDF5 --verbose
Run tests
Clone the repository and run tests with pytest
git clone https://github.com/Cascella-Group-UiO/HyMD.git hymd
cd hymd
pytest
Running MPI enabled pytest tests is simplified with a convenient script
chmod +x pytest-mpi
pytest-mpi -oo -n 2 -ns
References
[1] Milano, G.; Kawakatsu, T. Hybrid particle-field molecular dynamics simulations for dense polymer systems. J. Chem. Phys. 2009, 130, 214106.
[2] Bore, S. L.; Cascella, M. Hamiltonian and alias-free hybrid particle–field molecular dynamics. J. Chem. Phys. 2020, 153, 094106.
[3] Kolli, H. B.; De Nicola, A.; Bore, S. L.; Schäfer, K.; Diezemann, G.; Gauss, J.; Kawakatsu, T.;Lu, Z.-Y.; Zhu, Y.-L.; Milano, G.; Cascella, M. Hybrid Particle-Field Molecular DynamicsSimulations of Charged Amphiphiles in an Aqueous Environment. J. Chem. Theory Comput. 2018, 14, 4928–4937.
[4] Bore, S. L.; Milano, G.; Cascella, M. Hybrid Particle-Field Model for Conformational Dynamics of Peptide Chains. J. Chem. Theory Comput. 2018, 14, 1120–1130.
[5] Periole, X.; Cavalli, M.; Marrink, S. J.; Ceruso, M. A. Combining an elastic network with a coarse-grained molecular force field: structure, dynamics, and intermolecular recognition. J. Chem. Theory Comput. 2009, 5.9, 2531-2543.
[6] Pippig, M. PFFT: An extension of FFTW to massively parallel architectures. SIAM J. Sci. Comput. 2013, 35, C213–C236.
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