This git repository is only updated with new releases of fullrmc. A private repository is used for development.


FUndamental Library Language for Reverse Monte Carlo or fullrmc is a molecular/atomic stochastic fitting platform to reverse modeling experimental data. fullrmc is not a standard RMC software but exceeds in its capabilities and functionalities traditional RMC and Metropolis-Hastings algoritm. Therefore RMC appellation in fullrmc, is not accurate but it’s retained to respecting the community terminology. RMC is probably best known for its applications in condensed matter physics and solid state chemistry. RMC is used to solve an inverse problem whereby an atomic model is adjusted until its atoms position have the greatest consistency with a set of experimental data. fullrmc is a python package with its core and calculation modules optimized and compiled in Cython/c. fullrmc’s Engine sub-module is the main module that contains the definition of ‘Engine’ which is the main and only class used to launch the stochastic calculation. fullrmc is a fully object-oriented package where everything can be overloaded allowing easy development, implementation and maintenance of the code. It's core sub-package and modules are fully optimized written in cython/C. fullrmc is unique in its approach, among other functionalities:

  1. Atomic and molecular systems are supported.
  2. All types (not limited to cubic) of periodic boundary conditions systems are supported.
  3. Atoms can be grouped into groups so the system can evolve atomically, clusterly, molecularly or any combination of those.
  4. Every group can be assigned a different move generator (translation, rotation, a combination of moves generators, etc).
  5. Selection of groups to perform moves can be done manually OR automatically, randomly OR NOT !!
  6. Supports Artificial Intelligence and Reinforcement Machine Learning algorithms.

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Ask your questions!forum/fullrmc


fullrmc requires:
Installation using pip:

numpy and cython must be installed and updated manually.

pip install -U "numpy>=1.17.4"
pip install -U "cython>=0.29.14"
pip install fullrmc
Installation by cloning github repository

Ensure all fullrmc required packages are installed and up to data by executing the following python script:

# check whether all packages are already installed
from __future__ import print_function
from pkg_resources import parse_version as PV
for name, ver in [('numpy'      ,'1.17.4') ,
                  ('cython'     ,'0.29.14'),
                  ('pyrep'      ,'3.2.0') ,
                  ('pdbparser'  ,'0.1.8') ,
                  ('pysimplelog','2.0.0') ,
                  ('pylocker'   ,'3.0.0') ,
                  ('matplotlib' ,'3.1.2'  )]:
        lib = __import__(name)
        print('%s must be installed for fullrmc to run properly.'%(name))
        if PV(lib.__version__) < PV(ver):
            print('%s installed version %s is below minimum suggested version %s. Updating %s is highly) recommended.'%(name, lib.__version__, ver, name)
            print('%s is installed properly and minimum version requirement is met.'%(name))

Locate python's site-packages by executing the following python script:

import os
os.path.join(os.path.dirname(os.__file__), 'site_packages')

Navigate to site_packages folder and clone git repository from command line:

cd .../site_packages
git clone  

Change directory to .../site_packages/fullrmc/Extensions. Then compile fullrmc extensions from command line as the following:

cd .../site_packages/fullrmc/Extensions
python build_ext --inplace

Online documentation

Citing fullrmc

If you use fullrmc in a scientific publication, we would appreciate citations to the following paper:

Text entry:

Bachir Aoun; Fullrmc, a Rigid Body Reverse Monte Carlo Modeling Package Enabled with Machine Learning and Artificial Intelligence; J. Comput. Chem. 2016, 37, 1102–1111. DOI: 10.1002/jcc.24304

Bibtex entry:

    @article {JCC:JCC24304,
    author = {Aoun, Bachir},
    title = {Fullrmc, a rigid body reverse monte carlo modeling package enabled with machine learning and artificial intelligence},
    journal = {Journal of Computational Chemistry},
    volume = {37},
    number = {12},
    issn = {1096-987X},
    url = {},
    doi = {10.1002/jcc.24304},
    pages = {1102--1111},
    keywords = {reverse monte carlo, rigid body, machine learning, pair distribution function, modeling},
    year = {2016},

EndNote entry:

    Provider: John Wiley & Sons, Ltd
    Content:text/plain; charset="UTF-8"

    TY  - JOUR
    AU  - Aoun, Bachir
    TI  - Fullrmc, a rigid body reverse monte carlo modeling package enabled with machine learning and artificial intelligence
    JO  - Journal of Computational Chemistry
    JA  - J. Comput. Chem.
    VL  - 37
    IS  - 12
    SN  - 1096-987X
    UR  -
    DO  - 10.1002/jcc.24304
    SP  - 1102
    EP  - 1111
    KW  - reverse Monte Carlo
    KW  - rigid body
    KW  - machine learning
    KW  - pair distribution function
    KW  - modeling
    PY  - 2016
    ER  -

Authors and developers