FCAM

A methodology for the calculation of multidimensional free-energy landscapes of molecular systems, based on analysis of molecular dynamics trajectories wherein stationary or adaptive biases have been applied to enhance the sampling of one or more collective variables.

https://pubmed.ncbi.nlm.nih.gov/34669402/

Please cite:

Marinelli, F. and J.D. Faraldo-Gomez, Force-Correction Analysis Method for Derivation of Multidimensional Free-Energy Landscapes from Adaptively Biased Replica Simulations. J Chem Theory Comput, 2021, 17: p. 6775-6788

This release includes:

calcf_vgauss.py: python program to calculate mean forces based on the Force-Correction Analysis Method.
graf_fes_kmc.py: python program to derive free energy landscapes and minimum free energy paths from mean forces using kinetic Monte Carlo.
FCAM_software_doc.pdf: documentation and examples.
tutorial: examples of FCAM applications.
tools: awk/bash scripts for one-dimensional free energy integration and partitioning neighbors search.

Changes from the previous release:

calcf_vgauss.py:
   Mean forces are calculated also when bias forces are not calculated or provided (assuming unbiased sampling)
   Fixed minor bugs related to masking (option -nobound now works without errors)
   Fixed error associated to REMOVE_COMP option when changing order of the input file
   Updated log output associated to these changes

graf_fes_kmc.py:
   Updated output format when calculating minimum free energy paths 
   Added error estimate on free energy derived from KMC
   Added some controls of nans 

In tools:
    generalized partition neighbors script
    minor fix on get_integral script

Minor updates on documentation
Tutorial files are now zipped