This is the official implementation of the LEFTNet method proposed in the following paper.

Weitao Du, Yuanqi Du, Limei Wang, Dieqiao Feng, Guifeng Wang, Shuiwang Ji, Carla Gomes, Zhi-Ming Ma. "A New Perspective on Building Efficient and Expressive 3D Equivariant Graph Neural Networks". [NeurIPS 2023]

We include key dependencies below. The versions we used are in the parentheses.

• PyTorch (1.9.0)
• PyTorch Geometric (1.7.2)

device=0
target='homo' # 'mu', 'alpha', 'homo', 'lumo', 'gap', 'r2', 'zpve','U0', 'U', 'H', 'G', 'Cv'
python main_qm9.py --device $device --target $target

device=9
name='aspirin' #aspirin, benzene2017, ethanol, malonaldehyde, naphthalene, salicylic, toluene, uracil
python main_md17.py --device 0 --name $name

@article{du2023new,
  title={A new perspective on building efficient and expressive 3D equivariant graph neural networks},
  author={Du, Weitao and Du, Yuanqi and Wang, Limei and Feng, Dieqiao and Wang, Guifeng and Ji, Shuiwang and Gomes, Carla and Ma, Zhi-Ming},
  journal={arXiv preprint arXiv:2304.04757},
  year={2023}
}