Module: eds
| Description | Usage |
|---|---|
| Methods for incorporating additional information about CVs into MD simulations by adaptively determined linear bias parameters | |
| Authors: Glen Hocky and Andrew White |   |
Details
This Experiment Directed Simulation module contains methods for adaptively determining linear bias parameters such that each biased CV samples a new target mean value. This module implements the stochastic gradient descent algorithm in the original EDS paper that is cited below as well as additional minimization algorithms for Coarse-Grained Directed Simulation that are discussed in the second paper cited below.
The third paper cited below is a recent review on the method and its applications.
Notice that a similar method is available as MAXENT, although with different features and using a different optimization algorithm.
A tutorial using EDS specifically for biasing coordination number can be found on Andrew White's webpage.
Installation
This module is not installed by default. Add --enable-modules=eds to your './configure' command when building PLUMED to enable these features.
Actions
The following actions are part of this module
| Name | Description | Tags |
|---|---|---|
| EDS | Add a linear bias on a set of observables. | BIAS |
References
More information about this module is available in the following articles:
- A. D. White, G. A. Voth, Efficient and Minimal Method to Bias Molecular Simulations with Experimental Data. Journal of Chemical Theory and Computation. 10, 3023–3030 (2014)
- G. M. Hocky, T. Dannenhoffer-Lafage, G. A. Voth, Coarse-Grained Directed Simulation. Journal of Chemical Theory and Computation. 13, 4593–4603 (2017)
- D. B. Amirkulova, A. D. White, Recent advances in maximum entropy biasing techniques for molecular dynamics. Molecular Simulation. 45, 1285–1294 (2019)