Towards Symmetry-Aware Generation of Periodic Materials
Authors: Youzhi Luo, Chengkai Liu, Shuiwang Ji
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate our proposed Sy Mat method in two periodic material generation tasks, including random generation and property optimization. |
| Researcher Affiliation | Academia | Youzhi Luo, Chengkai Liu, Shuiwang Ji Department of Computer Science & Engineering Texas A&M University College Station, TX 77843 {yzluo,liuchengkai,sji}@tamu.edu |
| Pseudocode | Yes | Algorithm 1: Langevin Dynamics Sampling Algorithm for Coordinate Generation |
| Open Source Code | Yes | Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS). |
| Open Datasets | Yes | We evaluate Sy Mat on three benchmark datasets curated by Xie et al. [52], including Perov5 [3, 2], Carbon-24 [35], and MP-20 [17]. |
| Dataset Splits | Yes | For all three datasets, we split them with a ratio of 3:1:1 as training, validation, and test sets in our experiments. |
| Hardware Specification | Yes | The total training needs around 3, 5, and 10 hours on a GTX 2080 GPU for Perov-5, Carbon-24, and MP-20 datasets, separately. |
| Software Dependencies | No | The paper mentions software components like Sphere Net, VAE, and MLPs, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In our Sy Mat model, the Sphere Net model is composed of 4 message passing layers and the hidden size is set to 128. Also, all MLP models in the VAE decoder is composed of 2 linear layers with a Re LU function between them, and the hidden size is set to 256. During training, we set the learning rate to 0.001, the batch size to 128, and the epoch number to 1,000. |