Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Equivariant Neural Operator Learning with Graphon Convolution
Authors: Chaoran Cheng, Jian Peng
NeurIPS 2023 | Venue PDF | 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 EMAIL Jian Peng University of Illinois Urbana-Champaign EMAIL |
| 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. |