Anisotropic Message Passing: Graph Neural Networks with Directional and Long-Range Interactions
Authors: Moritz Thürlemann, Sereina Riniker
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results are split into three parts: First, two model systems are investigated to explore the proposed modifications. Second, the model is applied to an existing dataset of QM/MM systems. Finally, results for QM9 are reported as a comparison with existing models.In Table 1, results for a 3-layer AMP(3)-DP model are reported.Table 3: Mean absolute error (MAE) for the 12 properties of the QM9 dataset (Ramakrishnan et al., 2014). AMP refers to the model proposed in this work. |
| Researcher Affiliation | Academia | Moritz Th urlemann, Sereina Riniker Department of Chemistry and Applied Biosciences ETH Z urich Vladimir-Prelog-Weg 2, 8093 Z urich, Switzerland moritzt@ethz.ch, sriniker@ethz.ch |
| Pseudocode | No | The paper provides mathematical equations and descriptions of the message passing steps, but no explicitly labeled 'Pseudocode' or 'Algorithm' block, nor a structured procedure formatted like code. |
| Open Source Code | Yes | Code and model parameters used to produce the results in this work are made available at github.com/rinikerlab/AMP. |
| Open Datasets | Yes | All datasets used in this work are either already publicly available or made available under the same URL [github.com/rinikerlab/AMP]. |
| Dataset Splits | Yes | The datasets were taken from B oselt et al. (2021) and randomly split into training/validation/test sets of 7 000/2 000/1 000 samples, respectively. |
| Hardware Specification | Yes | All timings are reported based on a Nvidia Titan V.The authors gratefully acknowledge NVIDIA for providing a Titan V under the NVIDIA Academic Hardware Grant Program. |
| Software Dependencies | Yes | Models were implemented with Tensor Flow (2.6.2 and 2.9.1) (Abadi et al., 2015; Developers, 2021). |
| Experiment Setup | Yes | Three message passing layers were used except for the QM9 dataset, where we used four message passing layers.Model parameters were optimized using ADAM (Kingma & Ba, 2017) with default parameters (β1 = 0.9, β2 = 0.999, ϵ = 10 7) and an exponentially decaying learning rate (5 10 4, 10 5).Gradients were clipped by their global norm with a clip norm of 1 (Pascanu et al., 2012).Single layer models were used for all experiments. Models were trained for 10 000 steps using a batch size of five. |