GemNet: Universal Directional Graph Neural Networks for Molecules
Authors: Johannes Gasteiger, Florian Becker, Stephan Günnemann
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the benefits of the proposed changes in multiple ablation studies. Gem Net outperforms previous models on the COLL, MD17, and OC20 datasets by 34 %, 41 %, and 20 %, respectively, and performs especially well on the most challenging molecules. |
| Researcher Affiliation | Academia | Johannes Gasteiger, Florian Becker, Stephan Günnemann Technical University of Munich, Germany {j.gasteiger,beckerf,guennemann}@in.tum.de |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation is available online. https://www.daml.in.tum.de/gemnet |
| Open Datasets | Yes | We evaluate our model on four molecular dynamics datasets. COLL [28, CC-BY 4.0] consists of configurations taken from molecular collisions of different small organic molecules. MD17 [9] consists of configurations of multiple separate, thermalized molecules, considering only one molecule at a time. MD17@CCSD [10] uses the same setup, but calculates the forces using the more accurate and expensive CCSD or CCSD(T) method. The open catalyst (OC20) dataset [8, CC-BY 4.0] consists of energy relaxation trajectories of solid catalysts with adsorbate molecules. |
| Dataset Splits | Yes | Following the setup of Batzner et al. [4], we use 1000 training and validation configurations for MD17, and 950 training and 50 validation configurations for MD17@CCSD. |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, memory specifications, or cluster configurations) used for running experiments were provided in the paper. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software components (e.g., Python, PyTorch, CUDA, specific libraries or solvers). |
| Experiment Setup | Yes | We train our models using the AdamW optimizer [40] with a learning rate of 1e-4, an exponential decay of 0.01 per 100 000 steps, a batch size of 32, and an extensive search for the optimal force loss weight (which varied across datasets). |