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Retrieved on: 2024-05-02 15:15:01
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Summary
The article discusses the use of Graph Neural Networks for predicting properties of crystalline materials by learning from their crystallographic data within the contexts of condensed matter physics and materials science. It specifically addresses the development of invariant and continuous descriptors for machine learning algorithms to handle the periodic and isometric nature of crystal structures.
Article found on: www.nature.com
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