Equivariant Networks for Crystal Structures
Authors: Oumar Kaba, Siamak Ravanbakhsh
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, these models achieve competitive results with state-of-the-art on property prediction tasks. and We perform experimental tests of our models on the Materials Project database and report results comparable to or better than baselines. |
| Researcher Affiliation | Academia | School of Computer Science, Mc Gill University Mila Quebec Artiļ¬cial Intelligence Institute |
| Pseudocode | No | The paper describes mathematical equations for message passing but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | A link to the relevant code will also be accessible upon publication of the paper. |
| Open Datasets | Yes | We perform experiments using the Materials Project dataset [33] 1. This standard dataset of materials informatics comprises more than 120K materials... and Finally, we perform experiments using the Perov-5 dataset [9] as provided by [69]. |
| Dataset Splits | Yes | Training, validation, and test splits are 80%, 10%, and 10% of the dataset. |
| Hardware Specification | Yes | The training was performed on a single NVIDIA A100 GPU and a dual Intel Gold 6248R CPU, 20 cores machine. Experiments were performed on Mila s compute cluster with Slurm. |
| Software Dependencies | No | The paper mentions using 'Pytorch [49]' and 'Pytorch Scatter package [22]' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | The full hyperparameter setup is provided in Appendix A.7. and in Appendix A.7: The learning rate was chosen with a cosine annealing schedule with a warm-up period, starting from a learning rate of 1e-4 and decaying to 1e-6. The batch size was set to 64. The model was trained for 100 epochs, and the best model was chosen based on the validation set performance. We used a weight decay of 1e-2 and gradient clipping with a norm of 1.0. We use a hidden dimension of 128 for all layers, except the output layer. |