FUndamental Library Language for Reverse Monte Carlo or fullrmc project has started in 2015 as a consequence of the incapability of modelling complex atomic systems’ structure. fullrmc is not a standard RMC software but it’s rather a molecular/atomic stochastic fitting platform to reverse modeling experimental data. fullrmc implementation exceeds in its capabilities and functionalities traditional RMC and Metropolis–Hastings algorithm. Therefore RMC appellation in fullrmc is not accurate but it’s retained to respecting the community terminology. Since fullrmc was published a first official release 0.2.0 was made. Later, successive releases were made as needed.
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 = {http://dx.doi.org/10.1002/jcc.24304},
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 - http://dx.doi.org/10.1002/jcc.24304
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 -