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..
GemNet: Universal Directional Graph Neural Networks for Molecules
Authors: Johannes Gasteiger, Florian Becker, Stephan Günnemann
NeurIPS 2021 | Venue PDF | 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 EMAIL |
| 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). |