llumys 1.0.1

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LLUMYS - README

LLUMYS: Learning Loss informed Uncertainty Aware MLIP from Yonsei and Sydney

LLUMYS is an E(3)-equivariant graph neural network interatomic potential that learns per-atom uncertainty based on the learning loss concept. It has been developed as a companion package to NARA (a Firefly algorithm based atomic structure optimization framework). The name LLUMYS originates from lumen, meaning “light”, symbolizing the glow of a firefly.

Prerequisites

• Python ≥ 3.10
• ASE ≥ 3.26.0

Earlier versions are compatible, but 3.26.0 or later is recommended because optimizer.irun now explicitly includes gradient evaluation/log

• e3nn
• pytorch ≥ 2.4

Please follow the installation guide on the official PyTorch website

• (optional) openequivariance, GCC≥9

For additional GPU acceleration. Requires installation of the matching CUDA toolkit for your PyTorch and CUDA driver versions.

Installation

Using PIP

You can install LLUMYS directly from PyPI:

pip install llumys

→ To use OpenEquivariance, install openequivariance package first, matching with your GPU environment ($pip install openequivariance)

Usage

(1) Import LLUMYS (choose one based on your case)

## a. For import default GNN model
from llumys.gnn import EquivariantGNN
from llumys.train import main_GNN

## b. For import learning-loss model
from llumys.gnn_LL import EquivariantGNN_UC
from llumys.train import main_UC

## c. For import OpenEquivariance-implemented versions of default GNN model
from llumys.gnn_oeq import EquivariantGNN
from llumys.train_oeq import main_GNN

## d. For import OpenEquivariance-implemented versions of LL model
from llumys.gnn_LL_oeq import EquivariantGNN_UC
from llumys.train_oeq import main_UC

(2) Train the model

# Setting
device = "cuda" # or mps or cpu

### tag-out parameters will be set as default
gnn_config = dict(
    r_cut = 5.0, # radius cutoff
    irreps_hidden = "128x0e+128x1o", # low option. -> '256x0e' (invariant) or '128x0e+128x1o' or '32x0e+32x0o+32x1e+32x1o' are usually used for real cases
    dtype = "float64", # float64 is overally recommended for optimization, float32 could be enough for MD simulations    
    # irreps_out_hidden = "16x0e",
    # node_embedding_dim = 32,
    # edge_n_basis = 8,
    # n_invariant_neurons = 8, # number of hidden neurons in radial function, smaller is faster [from nequip documentatoin]
    # n_invariant_layers = 1, # number of radial layers, Usually 1-3 works best, smaller is faster [from nequip documentatoin]
    # n_message_passing = 3,
    # max_grad_norm = 10,
    )

# Training
## for a or c -> use main_GNN
main = main_GNN
## for b or d -> use main_UC
main = main_UC

main(
    xyz_filename = "[your_train_dataset_filename.xyz]",
    valid_xyz = "[your_valid_dataset_filename.xyz]", # or use train_ratio for splitting the xyz
    device = device,
    batch_size = 5, # when including forces, 1-5 will be more appropriate.
    valid_batch_size = 10, # higher the faster. set this below OOM
    max_epoch = 1000,
    patience = 50, # to not use early_stopping, set patience=None 
    best_model_filename = "[filename-for-best-model]",
    loss_filename = "[filename-for-losses]",
    gnn_config = gnn_config,
)

(3) Usage: using LLUMYS as an ASE calculator

# import GNN wrapper
## a. default GNN model
from llumys.ase_wrapper import GNNWrapper
## b. LL model
from llumys.ase_wrapper import GNNUCWrapper
## c. default GNN model - OEQ
from llumys.ase_wrapper_oeq import GNNWrapper
## d. LL model - OEQ
from llumys.ase_wrapper_oeq import GNNUCWrapper

# loading the best-model.pt
## for a or c
WRAPPER = GNNWrapper
## for b or d
WRAPPER = GNNUCWrapper

ase_calc = WRAPPER(gnn_model="[filename-for-best-model.pt]", device=device)

→ For more detailed information, please see Tutorials

Acknowledgement

TBD

License Information

This project is licensed under the MIT License - see the LICENSE file for details.

---

Troubleshooting

1. CUDA error: invalid device ordinal

RuntimeError: CUDA error: invalid device ordinal
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

→ This error occurs when multiple GPUs exist on a node but the user explicitly selects a non-allocated GPU (ex. device=torch.device("cuda:2")). In such cases, specify the target GPU when running the Python script:

CUDA_DEVICE_ORDER=PCI_BUS_ID CUDA_VISIBLE_DEVICES=2 python [your-script-name.py]

1. ImportError when import openequivariance

ImportError: Unable to load OpenEquivariance extension library: CUDA_HOME environment variable is not set. Please set it to your CUDA install root.

Check if nvcc --version runs properly.

→ If it doesn’t work, you may need to reinstall CUDA toolkit.

→ If it does work, add the following line to your .bashrc or job script to make sure OpenEquivariance recognizes CUDA:

export CUDA_HOME="$(dirname "$(dirname "$(which nvcc)")")"

1. GCC version error: Please use GCC 9 or later

#error "You're trying to build PyTorch with a too old version of GCC. We need GCC 9 or later."

Check the gcc versions with gcc -v

1. Import hangs with no error message

If a Python script hangs during import without showing any error message, first check whether the issue is related to the OpenEquivariance JIT extension by running:

$ python -c "import openequivariance"

If this also hangs without any error message, the problem may be caused by the PyTorch extensions cache. In this case, set TORCH_EXTENSIONS_DIR to a job-local or user-defined directory before running the script.

For example:

export TORCH_EXTENSIONS_DIR=$PBS_JOBFS/torch_extensions

mkdir -p "$TORCH_EXTENSIONS_DIR"

This forces PyTorch to build extensions in the specified directory instead of reusing the default cache path, which can help avoid stale lock or shared-cache issues on HPC systems.