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README.md

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Training and simulation inputs for paper "Descriptors-free Collective Variables From Geometric Graph Neural Networks".

Link to the paper: https://pubs.acs.org/doi/10.1021/acs.jctc.4c01197 (arXiv: https://arxiv.org/abs/2409.07339v3)

The contents are organized as follows:

  • plumed_pytorch_gnn: contains the plumed interface for GNN-based CVs
  • alad: contains the files to reproduce alanine dipeptide in a vacuum results
    • data: topology and force field files
    • analysis: analysis scripts
    • train: scripts for the training of the GNN-CVs and trained model
    • run_biased: simulation files for biased simulations using phi and psi as CVs
    • run_biased_gnn/10A_2layer_1c/1: simulation files for biased simulations using GNN-CV
    • run_unbiased/long: simulation files for unbiased simulations
  • nacl: contains the files reproduce NaCl dissociation in explicit water results
    • data: topology and force field files
    • analysis: analysis scripts
    • train: scripts for the training of the GNN-CVs and trained model
    • run_biased: simulation files for biased simulations using interionic distance and oxygen coordination of Na+ as CVs
    • run_biased_gnn/6A_2layer_1c/1: simulation files for biased simulations using GNN-CV
    • run_unbiased/long: simulation files for unbiased simulations
  • reaction: contains the files reproduce methyl migration of FDMB cation results
    • data: topology files
    • eval: plumed files to evaluate GNN-CV using plumed driver
    • run_biased: simulation files for biased simulations using coordiantion difference as CV
    • run_biased_gnn: simulation files for biased simulations using GNN-CV
    • run_unbiased: simulation files for unbiased simulations
    • train_ff: scripts for the training of MLCV based on feed-forward NN and trained model
    • train_ff_full: scripts for the training of MLCV based on feed-forward NN and a fully permutated dataset and trained model
    • train_gnn: scripts for the training of the GNN-CVs and trained model

The modified version of the mlcolvar library used for the GNN-CV training is available at: https://github.com/jintuzhang/mlcolvar

The relevant code for the GNN-CV definiton and training is implemented in the mlcolvar.graph module of such a library.