Summary of Ada-gnn: Atom-distance-angle Graph Neural Network For Crystal Material Property Prediction, by Jiao Huang and Qianli Xing and Jinglong Ji and Bo Yang
ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction
by Jiao Huang, Qianli Xing, Jinglong Ji, Bo Yang
First submitted to arxiv on: 22 Jan 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 This research paper presents a novel approach to predicting crystal properties by modeling atoms and structures as graphs. Building upon previous works, the authors propose a dual scale neighbor partitioning mechanism to efficiently handle bond angles, which are crucial for crystal property prediction. They also introduce an Atom-Distance-Angle Graph Neural Network (ADA-GNN) architecture that processes node information and structural information separately. The experimental results demonstrate improved accuracy and inference time on two large-scale material benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about predicting the properties of crystals, like what they’re made of and how they behave. Scientists use special graphs to represent these tiny structures, and this research makes it faster and more accurate by considering not just distances between atoms but also angles between bonds. The authors developed a new way of looking at these structures that works really well, and their results are the best so far on two big datasets. |
Keywords
* Artificial intelligence * Gnn * Graph neural network * Inference
