Project ID: plumID:21.020
Name: Reweighted Jarzynski sampling
Archive: (browse)
Category: methods
Keywords: free energies, steered MD, neural network, nonequilibrium work, nucleation, chemical reactions
PLUMED version: 2.8
Contributor: Kristof Bal
Submitted on: 07 May 2021
Publication: unpublished

PLUMED input files

File Compatible with
droplet/smd/plumed1.inp tested on master
droplet/smd/plumed2.inp tested on master
droplet/fes/plumed.inp tested on master with LOAD
dimer/smd/plumed1.inp tested on master
dimer/smd/plumed2.inp tested on master
dimer/fes/plumed.inp tested on master
sn2/smd/plumed.inp tested on master
sn2/fes/plumed.inp tested on master
hbr/smd/plumed.inp tested on master
hbr/fes/plumed.inp tested on master
hbr/hlda/1/plumed.inp tested on master
hbr/hlda/2/plumed.inp tested on master

Last tested: 14 Jun 2021, 15:49:37

Project description and instructions
A simple enhanced sampling approach using a bias potential learned from a nonequilibrium work distribution and the Jarzynski equality. LAMMPS, CP2K, and PLUMED inputs are provided. For each system, the workflow is as follows. 1) Perform a number of steered MD (SMD) runs along the reaction coordinate of choice. 2) Fit a neural network (NN) to the approximate free energy surface with the script. 3) Calculate the true free energy surface from a biased run, using the NN as bias potential, as a reweighted histogram. A matching script is provided for each system, and depends on scikit-learn and numpy. For the HBr system we also have inputs for the HLDA analysis, for which you’ll need G. Piccini’s scripts with CP2K and LAMMPS (matching inputs provided). Processing of the SMD runs has to be done with the (also provided) scripts.

Submission history
[v1] 07 May 2021: original submission

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