Project ID: plumID:21.020
Name: Reweighted Jarzynski sampling
Archive: https://github.com/kbal/reweightedjarzynski/archive/main.zip (browse)
Keywords: free energies, steered MD, neural network, nonequilibrium work, nucleation, chemical reactions
PLUMED version: 2.8
Contributor: Kristof Bal
Submitted on: 07 May 2021
PLUMED input files
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 nn.py script. 3) Calculate the true free energy surface from a biased run, using the NN as bias potential, as a reweighted histogram. A matching nn.py 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) nn.py scripts.
[v1] 07 May 2021: original submission
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