Accelerating Molecular Graph Neural Networks via Knowledge Distillation
Authors: Filip Ekström Kelvinius, Dimitar Georgiev, Artur Toshev, Johannes Gasteiger
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate our proposed methods, we perform comprehensive benchmarking experiments on the OC20-2M [17] dataset (structure to energy and forces (S2EF) task) a large and diverse catalyst dataset; and COLL [6] a challenging molecular dynamics dataset. |
| Researcher Affiliation | Collaboration | Filip Ekström Kelvinius Linköping University filip.ekstrom@liu.se Dimitar Georgiev Imperial College London d.georgiev21@imperial.ac.uk Artur Petrov Toshev Technical University of Munich artur.toshev@tum.de Johannes Gasteiger Google Research johannesg@google.com |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. Methods are described in prose and mathematical equations. |
| Open Source Code | Yes | Associated code is available online2. 2https://github.com/gasteigerjo/ocp/blob/main/DISTILL.md |
| Open Datasets | Yes | To evaluate our proposed methods, we perform comprehensive benchmarking experiments on the OC20-2M [17] dataset (structure to energy and forces (S2EF) task) a large and diverse catalyst dataset; and COLL [6] a challenging molecular dynamics dataset. |
| Dataset Splits | Yes | Values represent the average across the four available validation sets. Results for individual validation datasets are provided in Appendix B. |
| Hardware Specification | Yes | Models were trained on NVIDIA A100 40 GB and NVIDIA RTX A6000 48 GB GPUs, except Gem Net-OC-small which were trained on NVIDIA A100 80 GB and NVIDIA RTX A6000 48 GB. All models were trained on single GPUs, except for Sch Net when trained on OC20-2M, which required 3 GPUs. Inference throughput was profiled on A100 40 GB GPUs, with reported values representing approximate numbers averaged across three evaluations. |
| Software Dependencies | No | The paper mentions using the 'Open Catalyst Project (OCP) codebase' but does not specify version numbers for this or any other software dependencies like programming languages or libraries. |
| Experiment Setup | Yes | We provide detailed information about the hyperparameters we used for each model in Tables 5, 6, and 7. Moreover, we summarize the KD weighting factors λ we used for each model configuration in Table 8. |