Project ID: plumID:21.020
Name: Reweighted Jarzynski sampling
Archive: https://github.com/kbal/reweightedjarzynski/archive/main.zip (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
Last revised: 02 Nov 2021
Publication: K. M. Bal, Reweighted Jarzynski Sampling: Acceleration of Rare Events and Free Energy Calculation with a Bias Potential Learned from Nonequilibrium Work, Journal of Chemical Theory and Computation 17, 6766–6774 (2021)

PLUMED input files

File Compatible with
droplet/smd/plumed1.inp tested on v2.8 tested on master
droplet/smd/plumed2.inp tested on v2.8 tested on master
droplet/fes/plumed.inp tested on v2.8 tested on master with LOAD
dimer/smd/plumed1.inp tested on v2.8 tested on master
dimer/smd/plumed2.inp tested on v2.8 tested on master
dimer/fes/plumed.inp tested on v2.8 tested on master
sn2/smd/plumed.inp tested on v2.8 tested on master
sn2/fes/plumed.inp tested on v2.8 tested on master
hbr/smd/plumed.inp tested on v2.8 tested on master
hbr/fes/plumed.inp tested on v2.8 tested on master
hbr/hlda/1/plumed.inp tested on v2.8 tested on master
hbr/hlda/2/plumed.inp tested on v2.8 tested on master
hbr/metad/plumed.inp tested on v2.8 tested on master
hbr/ves/plumed.inp tested on v2.8 tested on master
hbr/opes/plumed.inp tested on v2.8 tested on master

Last tested: 30 Nov 2021, 15:18:51

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 nn.py script. 3) Calculate the true free energy surface from a biased run, using the NN as bias potential, as a reweighted histogram. Processing of the SMD runs has to be done with the nn.py scripts provided for each system, which depend 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.

Submission history
[v1] 07 May 2021: original submission
[v2] 01 Sep 2021: updated examples
[v3] 02 Nov 2021: updated doi

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