M3RG-IITD / MDBENCHGNN

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Code to paper: EGRAFFBENCH: EVALUATION OF EQUIVARIANT GRAPH NEURAL NETWORK FORCE FIELDS FOR ATOMISTIC SIMULATIONS

Image

Setup environment

see : env

Download example data and model weights(~450 MB): folder example_mdbenchgnn

  1. cd ../
  2. pip install gdown (install gdown to download data from google drive)
  3. gdown --folder --id 1PrrKaMBbjMyt3DrXM94XQ-X48nVrf-kY

create few symbolic-links (symbolic-links are great way to save space and avoid copying data)

  1. cd mdbenchgnn/
  2. ln -s ../example_mdbenchgnn example
  3. ln -s path/to/output_dir output_dir_sl
  4. ln -s path/to/data_dir data_ls

Models supported

  1. Nequip

  2. Allegro

  3. Mace

  4. Botnet

  5. Equiformer

  6. TorchMDnet

Try on example data: example/lips/data

  1. to train the models go through: scripts/train
  2. to run a MD simulation on trained model: scripts/md_simulation

Run on custom datasets:

see : preprocessing

Postprocess

  1. Radial Distribution function scripts/struct_props

Acknowledgement

Our implementation is based on PyTorch, PyG, nequip, allegro, equiformer, mace, TorchMD-NET and MDsim