Equivariant Neural Operator Learning with Graphon Convolution
Authors: Chaoran Cheng, Jian Peng
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments on large-scale electron density datasets, we observed that our model significantly outperformed the current state-of-the-art architectures. Multiple ablation studies were also carried out to demonstrate the effectiveness of the proposed architecture. |
| Researcher Affiliation | Academia | Chaoran Cheng University of Illinois Urbana-Champaign chaoran7@illinois.edu Jian Peng University of Illinois Urbana-Champaign jianpeng@illinois.edu |
| Pseudocode | No | The paper describes its methods but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/ccr-cheng/Inf GCN-pytorch. |
| Open Datasets | Yes | QM9. The QM9 dataset [41, 39] contains 133,885 species with up to nine heavy atoms (CONF). The density data as well as the data split come from [17, 18]... Cubic. This large-scale dataset contains electron densities on 17,418 cubic inorganic materials [53]... MD. The dataset contains 6 small molecules... The former 4 molecules are from [1]... The latter two are from [2]. |
| Dataset Splits | Yes | QM9... gives 123835 training samples, 50 validation samples, and 10000 testing samples. |
| Hardware Specification | Yes | All models were trained on a single NVIDIA A100 GPU. |
| Software Dependencies | No | Our implementation was based on the e3nn3 package which implements efficient spherical vector manipulations. No specific version numbers for software dependencies are provided. |
| Experiment Setup | Yes | We set the maximal degree of spherical tensors to L = 7, with 16 radial basis and 3 convolution layers. [...] The training and testing specifications are provided in Table 4. The Table includes details such as 'cutoff', 'n_iter', 'lr', 'patience', 'batch_size', 'lr_decay', 'train_sample', 'inf_sample' for different models and datasets. |