Summary of Pretraining Strategy For Neural Potentials, by Zehua Zhang et al.
Pretraining Strategy for Neural Potentials
by Zehua Zhang, Zijie Li, Amir Barati Farimani
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Chemical Physics (physics.chem-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We propose a novel mask pretraining technique for Graph Neural Networks (GNNs) to improve their performance on fitting potential energy surfaces, particularly in water systems. Our approach pretrains GNNs by recovering spatial information related to masked-out atoms from molecules, which is then transferred and finetuned on atomic forcefields. This pretraining method enables GNNs to learn meaningful prior knowledge about structural and physical properties of molecule systems, useful for downstream tasks. Experimental results demonstrate that our proposed method outperforms GNNs trained from scratch or using alternative pretraining techniques like denoising, in terms of accuracy and convergence speed. Our approach is suitable for both energy-centric and force-centric GNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re working on a new way to improve Graph Neural Networks (GNNs) so they can better understand the structure of molecules. Right now, GNNs are good at learning patterns in data, but they don’t always get it right. Our method helps GNNs learn more about how atoms are arranged in molecules, which makes them better at predicting things like energy and forces between atoms. We tested our approach and found that it works better than other methods for training GNNs. This could be useful for people who study chemistry and want to use computers to help them understand the behavior of molecules. |
Keywords
* Artificial intelligence * Mask * Pretraining
