Summary of Self-supervised Learning For Crystal Property Prediction Via Denoising, by Alexander New et al.
Self-supervised learning for crystal property prediction via denoising
by Alexander New, Nam Q. Le, Michael J. Pekala, Christopher D. Stiles
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Materials Science (cond-mat.mtrl-sci)
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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 The proposed crystal denoising self-supervised learning (CDSSL) strategy improves the accuracy of predicting crystalline material properties by leveraging a novel pretext task-based approach. This method pretrains graph network models to recover valid material structures from perturbed versions, outperforming traditional training methods across various materials, properties, and dataset sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better predict the properties of crystals, which is important for discovering new materials. They came up with a new way to train computer models using self-supervised learning, where they first teach the model to fix distorted crystal structures before actually predicting the property values. This new approach works well across different types of materials and even improves performance when there’s limited data. |
Keywords
» Artificial intelligence » Self supervised
