Project ID: plumID:22.031
Name: Rare Event Kinetics from Adaptive Bias Enhanced Sampling
Archive: https://github.com/dhimanray/OPES-Flooding/archive/main.zip (browse)
Category: methods
Keywords: OPES Flooding, Kinetics, Rate, OPES, Machine Learning
PLUMED version: 2.9
Contributor: Dhiman Ray
Submitted on: 11 Aug 2022
Last revised: 14 Nov 2022
Publication: D. Ray, N. Ansari, V. Rizzi, M. Invernizzi, M. Parrinello, Rare Event Kinetics from Adaptive Bias Enhanced Sampling. Journal of Chemical Theory and Computation. 18, 6500–6509 (2022)

PLUMED input files

File Compatible with
2D_Potential/s27/plumed.dat tested on master
2D_Potential/s54/plumed.dat tested on master
2D_Potential/s90/plumed.dat tested on master
Ala2/phi/plumed.dat tested on master
Ala2/psi/plumed.dat tested on master
Chignolin/folding/plumed-descriptors.dat tested on master
Chignolin/folding/plumed_rate.dat tested on master
Chignolin/unfolding/plumed-descriptors.dat tested on master
Chignolin/unfolding/plumed_rate.dat tested on master

Last tested: 20 Jan 2023, 09:13:29

Project description and instructions
Introducing the OPES Flooding method for calculating kinetics of rare event processes. It is tested on model systems and folding/unfolding of chignolin. The MD engine used is GROMACS 2021. The Deep-LDA and Deep-TICA CV were trained using Pytorch 1.8.2 and mlcvs package. Note. The OPES flooding method is available from PLUMED version 2.8. But the Deep-LDA and Deep-TICA CV we used in this work are implemented in PLUMED 2.9.

Submission history
[v1] 11 Aug 2022: original submission
[v2] 14 Nov 2022: updated doi

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plumeDnest:22.031