The Free Energy and Advanced Sampling Simulation Toolkit (FEASST) is a free, open-source, modular program to conduct molecular and particle-based simulations with flat-histogram Monte Carlo methods.
Website DOI: https://doi.org/10.18434/M3S095
Code repository: https://github.com/usnistgov/feasst
Discussion list: https://groups.google.com/a/list.nist.gov/d/forum/feasst
Plugins contains the list of features available. Some features include but are not limited to the following:
Metropolis Monte Carlo
Wang-Landau Monte Carlo
Transition-matrix Monte Carlo
Mayer-sampling Monte Carlo
Grand canonical ensemble
Lennard-Jones with Yukawa, LRC, cut and force shift
Charged interactions with the Ewald summation
Cell list and neighbor list
Interface with C++ or as a Python module
Checkpointing to save and restart simulations
Tutorial describes an example Lennard-Jones Monte Carlo simulation. After completing this basic tutorial, check out tutorials specific to each Plugins. For example, see the tutorials of System, MonteCarlo and FlatHistogram.
The search box for the html documentation was disabled for security reasons. But the html is generated entirely from the downloaded code. Thus, the grep command is a great option to search for more information on keywords. For example, if you would like more information on RandomMT19937 but are not sure where to find it, you could search headers files
grep -r --include=*.h RandomMT19937
And find that the class is part of the Math plugin.
Build from source code¶
To begin, [apt/yum/brew] install g++ cmake git.
SWIG is required. Version 3.0.12 is recommended if your current SWIG version does not work properly.
Anaconda with Python 3 is recommended.
CMake attempts to find the python libraries during compilation. But you may want to specify them manually.
git clone https://github.com/usnistgov/feasst.git mkdir feasst/build cd feasst/build conda env create -f ../py/feasst.yml conda activate feasst # alternatively, without env create, include some tutorial dependencies # pip install jupyter matplotlib pandas scipy # for tutorials cmake -DUSE_SWIG=ON .. # alternatively, for manually setting the python path # cmake -DUSE_SWIG=ON -DSET_PYTHON_PATH=ON -DPYTHON_INCLUDE_DIR=/path/to/anaconda/include/python3.7m -DPYTHON_LIBRARY=/path/to/anaconda/lib/libpython3.7m.so .. make -j4 make install -j4 python ../py/test.py # optional test
First, install the C++ library.
git clone https://github.com/usnistgov/feasst.git mkdir feasst/build cd feasst/build cmake .. # optionally, include -DUSE_GTEST=ON for gtest make install -j4 make test # optional test
Then, compile the specific simulation you wish to run (e.g., tutorial).
cd /path/to/feasst/tutorial/ mkdir build; cd build cmake .. make ./tutorial
CMake defaults to install in the build directory. But you can also specify the path as follows.
cmake -DCMAKE_INSTALL_PREFIX=/path/to/install/dir ..
Later, when you build your tutorial executable, if your build directory is not ~/feasst/build, then specify the path to the build directory as follows:
cmake -DCMAKE_PREFIX_PATH=/path/to/install/dir ..
Sometimes running the executable results in an error that a plugin cannot be found. In that case LD_LIBRARY_PATH needs to be set as follows:
Please Contact us if you run into an issue not listed below.
SWIG (from Homebrew) is likely version 4, which sometimes causes a SEGFAULT when trying to run feasst. Try SWIG version 3 instead.
Sometimes CMake has trouble finding anaconda, and if you use SET_PYTHON_PATH described above, you may need to look out for the .dylib instead of .so
CMake and SWIG versions are usually too old. Try the command cmake3 instead of cmake. Otherwise, install SWIG 3.
Install Windows subsystem for Linux (Ubuntu 16)
See Ubuntu 16
Ubuntu 18 or 20¶
We are not aware of any issues with an Ubuntu 18 or 20 install.
Cray (NERSC CORI)¶
OpenMP functions apparently do not work unless the cray programming environment is disabled.
Build from Docker¶
The Docker is no longer being updated and contains a very old version of the code. Installation via Docker is an alternative if you are experiencing installation issues. Unfortunately there is a performance penalty using this method.
First, install docker
Ubuntu: apt install docker
macOS: download and install Docker Desktop.
CentOS: yum install docker
On a mac, run Docker.app, login, startup and skip this step. Otherwise, start docker and create a docker group to avoid root.
sudo service docker start sudo groupadd docker sudo usermod -aG docker $USER newgrp docker
git clone https://github.com/usnistgov/feasst.git docker build -t hhatch/feasst:firsttry feasst/
Alternatively you could pull feasst from docker hub (version may be old):
docker pull hhatch/feasst:firsttry
Then you can interactively run feasst via
docker run -v ~/scripts:/mnt/scripts -it hhatch/feasst:firsttry cd /mnt/scripts
Or with ~/notebooks
docker run -v ~/notebooks:/mnt/notebooks -i -t -p 8888:8888 hhatch/feasst:firsttry /bin/bash -c "jupyter notebook --notebook-dir=/mnt/notebooks --ip='*' --port=8888 --no-browser --allow-root"
Harold Wickes Hatch
Any and all bug reports are greatly appreciated in order to improve FEASST.
The following checklist is recommended to improve reproducibility so that I can get back to you more quickly.
Reproduce the issue with minimal example to reduce the complexity, run time and file size.
Provide all output.
Provide the version (e.g. bash command “git describe –abbrev=10 –dirty –always –tags”).
Provide the user code (e.g., C++ with int main() or .py).
Certain commercial firms and trade names are identified in this document in order to specify the installation and usage procedures adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that related products are necessarily the best available for the purpose.
This license was obtained from https://www.nist.gov/director/licensing (revised as of July 2017).
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