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Capturing Molecular Interactions in Graph Neural Networks: A Case Study in Multi-Component Phase Equilibrium

By Shiyi Qin and others
Graph neural networks (GNNs) have been widely used for predicting molecular properties, especially for single molecules. However, when treating multi-component systems, GNNs have mostly used simple data representations (concatenation, averaging, or self-attention on features of individual components) that might fail to capture molecular interactions and potentially limit prediction accuracy. In... Show more
September 23, 2022
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Capturing Molecular Interactions in Graph Neural Networks: A Case Study in Multi-Component Phase Equilibrium
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