SE(3) Equivariant Graph Neural Networks with Complete Local Frames
Authors: Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Nanning Zheng, Bin Shao, Tie-Yan Liu
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on two tasks: Newton mechanics modeling and equilibrium molecule conformation generation. Extensive experimental results demonstrate that our model achieves the best or competitive performance in two types of datasets. |
| Researcher Affiliation | Collaboration | 1Chinese Academy of Sciences, China 2Xi an Jiaotong University, China 3George Mason University, USA 4Microsoft Research, USA. |
| Pseudocode | Yes | Algorithm 1 Clof Net |
| Open Source Code | No | The paper cites external open-source projects used in their work (e.g., EGNN, NRI) but does not provide a link or statement about releasing the source code for their own proposed method (Clof Net). |
| Open Datasets | Yes | We evaluate the proposed model on the GEOM-QM9 and GEOM-Drugs datasets (Axelrod & Gomez-Bombarelli, 2020) as well as the ISO17 dataset (Sch utt et al., 2017). |
| Dataset Splits | Yes | Following EGNN, for each system, we sample 3,000 trajectories for training, 2,000 for validation and 2,000 for test. |
| Hardware Specification | Yes | The forward time is measured by averaging over multiple batches on an Nvidia Tesla V100 GPU |
| Software Dependencies | No | The paper mentions software like Pytorch (Paszke et al., 2019), Adam optimizer (Kingma & Ba, 2014), and Dopri5 solver (Dormand & Prince, 1980), but it provides these as citations rather than specific version numbers for software dependencies necessary for reproduction. |
| Experiment Setup | Yes | All baselines consist of 4 layers with hidden dimension 64 and are trained with Adam W optimizer (Loshchilov & Hutter, 2017) via a Mean Squared Error (MSE) loss. The learning rate and training epochs are tuned independently for each model... The Clof Net is equipped with 4 Graph Transformer blocks and the hidden dimensions are set to 288. All models are trained with Adam optimizer via the score matching loss function (See Appendix A.7.3, 49) for 400 epochs. |