Tutorial 1 | Tutorial 2 | Examples | Documentation Pages
Now available with Jupyter notebooks and Python scripts for readily setting up models and simulations.
Mango-Selm is a Python/C++ package for performing fluid-structure interaction simulations interfacing with the LAMMPS MD package. Provides simulation methods for particle systems, materials, and complex fluids with fluctuating hydrodynamics approaches including stochastic immersed boundary methods and stochastic eulerian-lagrangian methods. The package includes approaches for
Allows for SELM, Immersed Boundary Methods, and related hydrodynamic solvers to be used in conjunction with LAMMPS simulations. LAMMPS is an optimized molecular dynamics package in C/C++ providing many interaction potentials and analysis tools for modeling and simulation. Interaction methods include particle-mesh electrostatics, common coarse-grained potentials, many-body interactions, and others.
Quick Start
To install pre-compiled package for Python use
pip install -U selm-lammps
To test the package installed run
python -c "from selm_lammps.tests import t1; t1.test()"
Pre-compiled binaries for (Debian 9+/Ubuntu and Centos 7+, Python 3.6+).
If you installed previously this package, please be sure to update to the latest version using
pip install --upgrade selm-lammps
For example models, notebooks, and scripts, see the examples folder.
Other ways to install the package
The codes can also be run on Windows using WSL Ubuntu 22.04. Another way to run prototype models and simulations on a desktop, such as Windows and MacOS, is using Docker container. For example, install Docker Desktop, or docker for linux, and then load a standard ubuntu container by using in the terminal docker run -it ubuntu:20.04 /bin/bash
You can then use apt update; apt install python3-pip
, and can then pip install and run the simulation package as above. Note use command python3
in place of python
when calling scripts. Pre-installed packages in anaconda also in docker run -it atzberg/ubuntu_20_04_anaconda1 /bin/bash
Use conda activate selm-lammps
May need to update packages to the latest version.
For more information on other ways to install or compile the package, please see the documentation page http://doc.mango-selm.org/
Python/Jupyter Notebooks for Modeling and Simulations
Immersed Boundary Methods and SELM Models now easily can be set up using Python or Jupyter Notebooks. See the documentation page and tutorial video for details, http://doc.mango-selm.org/
Tutorials: Tutorial 1 | Tutorial 2 | Documentation Pages
Downloads: The source package and additional binaries are available at the webpage: http://mango-selm.org/
Please cite the paper below when referencing this package:
Fluctuating Hydrodynamics Methods for Dynamic Coarse-Grained Implicit-Solvent Simulations in LAMMPS, Wang, Y. and Sigurdsson, J. K. and Atzberger, P. J., SIAM Journal on Scientific Computing, 2016, paper link.
@article{atz_selm_lammps_fluct_hydro,
title = {Fluctuating Hydrodynamics Methods for Dynamic Coarse-Grained
Implicit-Solvent Simulations in LAMMPS},
author = {Wang, Y. and Sigurdsson, J. K. and Atzberger, P. J.},
journal = {SIAM Journal on Scientific Computing},
volume = {38},
number = {5},
pages = {S62-S77},
year = {2016},
doi = {10.1137/15M1026390},
URL = {https://doi.org/10.1137/15M1026390},
}
Incorporating shear into stochastic Eulerian{\textendash}Lagrangian methods for rheological studies of complex fluids and soft materials, P.J. Atzberger, Physica D: Nonlinear Phenomena, 2013, paper link.
@article{atz_selm_shear_methods,
title = {Incorporating shear into stochastic Eulerian{\textendash}Lagrangian
methods for rheological studies of complex fluids and soft materials},
author = {Paul J. Atzberger},
journal = {Physica D: Nonlinear Phenomena},
publisher = {Elsevier {BV}},
volume = {265},
pages = {57--70},
year = {2013},
doi = {10.1016/j.physd.2013.09.002},
url = {https://doi.org/10.1016%2Fj.physd.2013.09.002},
}
Mailing List for Future Updates and Releases
Please join the mailing list for future updates and releases here.
Bugs or Issues
If you encounter any bugs or issues please let us know by providing information here.
Please submit usage and citation information
If you use this package or related methods, please let us know by submitting information here.
This helps us with reporting and with further development of the package. Thanks.
Acknowledgements We gratefully acknowledge support from NSF Career Grant DMS-0956210, NSF Grant DMS-1616353, DOE ASCR CM4 DE-SC0009254, and DOE Grant ASCR PHILMS DE-SC0019246.
Additional Information
http://atzberger.org/