Project ID: plumID:24.020
Name: Graph Neural Network-State Predictive Information Bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics
Archive: https://github.com/connorzzou/PLUMED-NEST/raw/main/PLUMED_GNN_SPIB.zip
Category: bio
Keywords: LJ7, alanine, well-tempered metadynamics, infrequent metadynamics, machine learning
PLUMED version: 2.8.1
Contributor: Ziyue Zou, Dedi Wang, Pratyush Tiwary
Submitted on: 22 Sep 2024
Last revised: 16 Jul 2025
Publication: Z. Zou, D. Wang, P. Tiwary, A graph neural network-state predictive information bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics. Digital Discovery. 4, 211–221 (2025)
PLUMED input files
Last tested: 25 Jul 2025, 11:34:37
Project description and instructions
Software used was PLUMED-2.8.1 with PYTORCH module, GROMACS 2021.6 (alanine tetrapeptide), and GROMACS 2019.6 (alanine dipeptide)
Click here to open manual pages for actions used in this project.
Submission history
[v1] 22 Sep 2024: original submission
[v2] 16 Jul 2025: updated reference
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