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
Last tested: 22 Apr 2025, 10:13:40
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.
Click here to open manual pages for actions used in this project.
Submission history
[v1] 07 May 2021: original submission
[v2] 01 Sep 2021: updated examples
[v3] 02 Nov 2021: updated doi
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